The head of the US government’s AI testing institute, Chris Fall, has resigned about three months after taking charge of the Center for AI Standards and Innovation (CAISI), the federal organization responsible for evaluating advanced artificial intelligence models for safety and security.

Current National Institute of Standards and Technology NIST Director Arvind Raman will serve as acting CAISI Director following Fall’s departure while continuing to oversee the Commerce Department office responsible for the institute, the Daily Signal reported, citing two people familiar with the matter.

A Commerce Department spokesperson who spoke to the publication did not disclose a reason for the resignation.

Fall assumed leadership of CAISI in April after the Trump administration reorganized the former US AI Safety Institute under NIST. The institute develops methodologies for evaluating frontier AI models and works with AI developers on voluntary technical assessments covering areas such as cybersecurity, model misuse, reliability and other risks associated with increasingly capable AI systems.

The leadership change comes as governments and AI companies continue developing technical approaches for evaluating frontier AI models while enterprises expand deployments of generative AI and agentic AI across business operations.

In recent months, the Commerce Department has taken a more active role in AI policy involving advanced models, placing greater attention on how the federal government evaluates technologies with potential national security implications.

Continuity matters more than personalities

CAISI works with AI developers such as Anthropic, Google’s DeepMind and OpenAI on voluntary evaluations of frontier AI models and develops methodologies for testing model capabilities and risks. The institute does not regulate AI developers or certify commercial AI systems.

For enterprises, those evaluations are one source of technical information alongside vendors’ own testing, third-party security assessments and internal AI governance programs.

Sanchit Vir Gogia, chief analyst at Greyhound Research, said enterprises should focus less on the individual leading the institute and more on whether its technical work continues with the same level of consistency and transparency.

“Leadership churn at CAISI weakens the signal long before it weakens the science,” Gogia said. “The testing has not stopped. Its authority simply does not travel as cleanly once the leadership does not.”

According to Gogia, the more important question for enterprises is not whether the institute’s evaluation work will continue but whether the processes supporting those evaluations remain stable.

“The instinct is to ask whether the pipeline is breaking,” he said. “The more useful question is where the pipeline now sits.”

Enterprises still carry the burden of AI governance

Gogia said organizations should continue treating government-led AI evaluations as one input into their governance processes rather than as evidence that a model is inherently safe for enterprise deployment.

“A government evaluation was always a signal, never a certificate,” he said. “A signal loses value the moment its issuer becomes unpredictable.”

He said enterprises should instead monitor whether CAISI maintains consistent evaluation methodologies, continues publishing technical findings and preserves continuity within its research teams under interim leadership.

“The name on the door is not the signal. The behaviour underneath it is,” Gogia said.

Gogia also cautioned against linking Fall’s resignation to recent Commerce Department actions involving AI policy or export controls, noting that there is no public evidence connecting the two.

“CAISI evaluates; it does not enforce export controls, because it holds no such power,” he said. “This is not a testing body reaching for enforcement. It is enforcement reaching past the testing body.”

With Raman assuming the role on an interim basis, the next significant milestone for enterprises will be the appointment of a permanent director, and whether the institute’s evaluation programs continue without disruption, the analyst said.

Gogia said the successor’s mandate may prove more important than the individual selected.

“A CAISI result is not a safe harbour,” he said. “It informs an obligation; it does not discharge one.” NIST did not immediately respond to a request for comment.

OpenAI has launched Presence, an enterprise service for deploying voice and chat agents that can resolve customer and employee requests, potentially automating some work now handled by frontline support teams.

The agents can answer questions and operate IT systems, and enterprises can decide what actions the agents may take and when they should seek human approval for actions or transfer a case to a human.

OpenAI is already using Presence internally for its English-language phone support channel, where it verifies callers and uses account information to complete approved actions. The company said the system resolves 75% of inbound issues without human assistance.

Another OpenAI service, Codex, can be used to monitor agents and suggest updates or improvements to processes. In OpenAI’s own tests, suggestions from Codex helped reduce handoffs to humans by 15 percentage points over 10 days, it said. Presence also includes simulation and evaluation tools that allow companies to test an agent before deployment. The tests assess whether it reaches the correct outcome, follows company policy, and hands a case to an employee when required.

OpenAI intends each Presence deployment to deal with one kind of task, for example billing issues, insurance claims, or employee IT service requests, with agents getting only the knowledge and system access required for that task.

Presence is not a self-service product: Enterprises will have to sign up for the limited availability program, with integration performed by OpenAI or selected global systems integrators.

Companies exploring or testing Presence include Spanish bank BBVA, which is evaluating the service for everyday banking support in Mexico, and Japanese technology group SoftBank, which is using it in trials involving Japanese-language customer interactions. Australian insurer IAG is assessing whether the technology can help it respond to surges in customer demand during severe weather events.

Workforce impact

OpenAI’s announcement did not address the potential effect of Presence on employment. But its claimed automation rate raises questions about how the technology could affect staffing in customer service and other support functions.

Pareekh Jain, CEO of Pareekh Consulting, said CIOs should regard the 75% figure as evidence that the technology can work, rather than as a benchmark that every enterprise can expect to reach.

Jain said OpenAI’s deployment benefits from being built around the company’s own products and data. Large enterprises may achieve lower automation rates because they must contend with fragmented legacy systems, uneven knowledge bases and more complex compliance demands.

“Most organizations should expect lower initial automation levels that improve over time as the AI agent is refined,” Jain said.

The first workforce effect is more likely to be slower hiring than immediate layoffs, according to Tulika Sheel, senior vice president at Kadence International.

“The roles most exposed are likely to be repetitive, high-volume functions such as frontline customer support and routine back-office processing,” Sheel said. “However, I would expect the first impact to be on hiring and team growth rather than immediate large-scale job cuts. Over time, enterprises may redesign roles around AI-assisted workflows, with humans focusing more on complex cases, escalation, and relationship management.”

Jain said Tier-1 support agents handling predictable queries would face the most exposure. Broader reductions would become more likely only after companies reorganize their operations around the technology.

However, Lian Jye Su, chief analyst at Omdia, said Presence is unlikely to increase the threat of job displacement because companies have used similar customer-support automation from vendors such as Genesys, NiCE, Five9 and AWS for years.

Enterprises are more likely to use Presence alongside employees, with AI handling routine requests while people remain responsible for work requiring judgment and empathy, Su said.

Cost and operational risks

Analysts said CIOs should examine whether Presence can maintain resolution quality as usage grows, since fewer human handoffs could leave employees dealing with a more difficult mix of cases.

“The key question is not simply how many tasks AI can handle, but whether it can handle them reliably at scale,” Sheel said.

The financial case will depend partly on the cost of connecting Presence to existing systems and maintaining the controls needed to govern its use, according to Jain. “Often the biggest cost of enterprise AI is not tokens but integration and governance,” Jain added.

Companies will need to determine what systems and data the agents can access, monitor their performance, and audit the actions they take. Those investments could offset early savings.

Su said the complexity of enterprise IT will make it difficult for OpenAI to automate entire workflows on its own. Enterprises will still need to work with other technology providers and human employees, while CIOs will favor systems that can be audited and integrated with existing infrastructure.

Jain said the economics could improve if companies use the same integrations and governance controls across additional workflows.

This article first appeared on CIO.

I haven’t slept much in the past few weeks. Not because of some theoretical cyber risk that keeps many executives awake, but because reality just delivered a real wake-up call to our industry — a call that every executive must answer, now.

Imagine this: A major global enterprise, a company most of us interact with indirectly every single day, wakes up to find its entire digital environment obliterated. Thousands of employees in dozens of offices and remote locations are suddenly offline. Customers are cut off, supply chains grind to a halt and regulators are notified with a chilling admission: “We have no idea when we’ll be back.”

This wasn’t ransomware. There was no negotiation, no decryption key to buy, no easy way out. It was destruction — deliberate, coordinated and geopolitically motivated — not monetary.

As a chief customer officer who’s worked with countless customers on cyberattack risks, my perspective hits a bit differently than a CISO or a CTO. I see the aftermath, not just the attack surface. I see the faces behind the tickets, the operations team locked out of their own systems, the support agent answering panicked calls at dawn. And I ask: How many organizations have actually stress-tested their response to this scenario — not a hypothetical, but this very real, lights-out event? Here’s what every leader needs to confront today:

Recovery is not just a technical exercise

The first assumption to break during a real crisis is the belief that recovery is purely technical.

Many organizations have done tabletop exercises and have a backup and recovery playbook, so they feel prepared. They can point to backup windows, retention schedules and immutability controls. The moment a true blackout happens, a different reality surfaces. The people who own the recovery steps either do not know each other, lack the authority to make decisions without supervisor approval or need guidance from offline systems.

The reality is that technical infrastructure almost always holds up better than human infrastructure. Organizations have built their recovery strategy around the assumption that someone competent will be awake, available and empowered when a cyber event happens.

Still, backups are only as good as their independence. Let’s be blunt: If your recovery infrastructure shares identity, authentication or network trust with your Microsoft tenant (such as Azure, Microsoft 365 or Teams), you don’t actually have a recovery plan; you have a false sense of one — and a liability. A recent survey found that while 90% of organizations express confidence in their ability to recover from a cyber incident, fewer than one in three ransomware victims fully recovered their data.

True resilience means immutable, air-gapped backups, untouchable by the same compromise. Anything less is an illusion. I talk to customers about their recovery plans constantly. The customers who have rehearsed all scenarios sleep soundly. Those who haven’t? They’re rolling the dice.

Most business continuity plans ignore ‘total blackout’

I’ve reviewed hundreds of business continuity plans. Almost all assume partial failures — a region, an application, a data center. But what if every system, in every country, goes dark simultaneously? That’s an entirely different playbook. If your team hasn’t run a drill for a global, simultaneous outage, you’re not prepared. The probability is low, but the cost of being unready is existential.

Connected devices, OT systems, field hardware, partner integrations — they all plug into your enterprise network. When the core collapses, it’s not just IT at risk. It’s operational technology, physical safety systems and in regulated sectors, potentially human lives. Understanding and testing those interdependencies is non-negotiable.

This is also where boards need to change the conversation. A study found that only 5% of companies have cybersecurity experts on their board of directors. Recovery time objectives (RTOs) should not be buried in technical appendices. It’s all jargon to boards. That makes translation essential. RTOs must be explained in terms of business impact. “We can recover in four hours” is a technical statement. “Every hour of downtime costs us $2.3M and creates regulatory exposure in three jurisdictions” is a board statement.

That is the level of clarity leaders need.

The most prepared organizations do not wait for an incident to educate the board. They bring the conversation forward proactively. They frame recovery in business terms: revenue, regulatory standing, customer trust and brand reputation.

The most effective framing is often simple. Show the most critical systems. Show what happens if each one is down for one hour, four hours, 24 hours and 72 hours. Show the current recovery capability against each and then show the gap.

If your board is not demanding real answers, your business continuity strategy is likely underfunded and your business is exposed. This is a risk conversation worth forcing because the consequences do not stay inside IT. They can show up in customer churn or missed revenue and ruin an organization’s reputation.

Threat intelligence must be actionable, not archived

Geopolitical attacks, hacktivist campaigns and nation-state targeting aren’t abstract threats. They are active risks, and that intelligence cannot languish in the security team’s inbox. Executive leadership must be looped in — and immediately — so gaps can be closed before they’re exploited. Too often, intelligence enters the security operations function and never reaches the teams responsible for recovery infrastructure or executive decision-making.

If a threat actor is targeting a specific class of backup agents, the team responsible for those agents needs to know now, not two weeks from now. If intelligence suggests destructive activity against a sector, recovery owners need to validate isolation, access paths and restoration procedures immediately. If geopolitical tension increases the likelihood of targeting, executive leadership needs to understand what exposure exists and what actions are being taken. The organizations that survive aren’t just the best at incident response. They’re the ones who anticipated, rehearsed and invested before the attack.

Part of investing in a recovery strategy requires closing the loop between signal and action. The most prepared organizations have already mapped their critical recovery dependencies to specific threat categories. When intelligence touches one of those categories, there is a named owner and a clear set of actions. No guessing or forwarding emails into the void is needed because the distance between the warning and the employees’ ability to do something is shortened.

Looking ahead, the conversation will continue to evolve beyond traditional cyber response. Because in an AI-enabled enterprise, the new question is whether the data within those systems can still be trusted. When AI systems make decisions based on enterprise data, the attack surface becomes the data’s accuracy. A threat actor who quietly corrupts a dataset over 90 days before a recovery event has done more damage than just downtime. They can poison the inputs driving decisions across the business.

Regardless of how AI will change threat intelligence and cyber response, these principles remain the same. Know your problem, whether structural or technological. Ensure your human infrastructure keeps pace with your technical infrastructure, with clear cross-functional ownership and the tools and knowledge to act autonomously. Communicate with your boards often — and correctly.

Let’s not wait for the next headline to ask, “Are we ready?” Have those conversations now. Test your assumptions. Close your gaps. Because in today’s threat landscape, resilience isn’t IT’s job — it’s everyone’s mandate.

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OpenAI has launched Presence, an enterprise service for deploying voice and chat agents that can resolve customer and employee requests, potentially automating some work now handled by frontline support teams.

The agents can answer questions and operate IT systems, and enterprises can decide what actions the agents may take and when they should seek human approval for actions or transfer a case to a human.

OpenAI is already using Presence internally for its English-language phone support channel, where it verifies callers and uses account information to complete approved actions. The company said the system resolves 75% of inbound issues without human assistance.

Another OpenAI service, Codex, can be used to monitor agents and suggest updates or improvements to processes. In OpenAI’s own tests, suggestions from Codex helped reduce handoffs to humans by 15 percentage points over 10 days, it said. Presence also includes simulation and evaluation tools that allow companies to test an agent before deployment. The tests assess whether it reaches the correct outcome, follows company policy, and hands a case to an employee when required.

OpenAI intends each Presence deployment to deal with one kind of task, for example billing issues, insurance claims, or employee IT service requests, with agents getting only the knowledge and system access required for that task.

Presence is not a self-service product: Enterprises will have to sign up for the limited availability program, with integration performed by OpenAI or selected global systems integrators.

Companies exploring or testing Presence include Spanish bank BBVA, which is evaluating the service for everyday banking support in Mexico, and Japanese technology group SoftBank, which is using it in trials involving Japanese-language customer interactions. Australian insurer IAG is assessing whether the technology can help it respond to surges in customer demand during severe weather events.

Workforce impact

OpenAI’s announcement did not address the potential effect of Presence on employment. But its claimed automation rate raises questions about how the technology could affect staffing in customer service and other support functions.

Pareekh Jain, CEO of Pareekh Consulting, said CIOs should regard the 75% figure as evidence that the technology can work, rather than as a benchmark that every enterprise can expect to reach.

Jain said OpenAI’s deployment benefits from being built around the company’s own products and data. Large enterprises may achieve lower automation rates because they must contend with fragmented legacy systems, uneven knowledge bases and more complex compliance demands.

“Most organizations should expect lower initial automation levels that improve over time as the AI agent is refined,” Jain said.

The first workforce effect is more likely to be slower hiring than immediate layoffs, according to Tulika Sheel, senior vice president at Kadence International.

“The roles most exposed are likely to be repetitive, high-volume functions such as frontline customer support and routine back-office processing,” Sheel said. “However, I would expect the first impact to be on hiring and team growth rather than immediate large-scale job cuts. Over time, enterprises may redesign roles around AI-assisted workflows, with humans focusing more on complex cases, escalation, and relationship management.”

Jain said Tier-1 support agents handling predictable queries would face the most exposure. Broader reductions would become more likely only after companies reorganize their operations around the technology.

However, Lian Jye Su, chief analyst at Omdia, said Presence is unlikely to increase the threat of job displacement because companies have used similar customer-support automation from vendors such as Genesys, NiCE, Five9 and AWS for years.

Enterprises are more likely to use Presence alongside employees, with AI handling routine requests while people remain responsible for work requiring judgment and empathy, Su said.

Cost and operational risks

Analysts said CIOs should examine whether Presence can maintain resolution quality as usage grows, since fewer human handoffs could leave employees dealing with a more difficult mix of cases.

“The key question is not simply how many tasks AI can handle, but whether it can handle them reliably at scale,” Sheel said.

The financial case will depend partly on the cost of connecting Presence to existing systems and maintaining the controls needed to govern its use, according to Jain. “Often the biggest cost of enterprise AI is not tokens but integration and governance,” Jain added.

Companies will need to determine what systems and data the agents can access, monitor their performance, and audit the actions they take. Those investments could offset early savings.

Su said the complexity of enterprise IT will make it difficult for OpenAI to automate entire workflows on its own. Enterprises will still need to work with other technology providers and human employees, while CIOs will favor systems that can be audited and integrated with existing infrastructure.

Jain said the economics could improve if companies use the same integrations and governance controls across additional workflows.

I have worked alongside enterprise technology for more than 30 years and watched AI evolve from a lab experiment into the modern boardroom’s core focus. However, the last few years of implementing AI alongside our customers have delivered our most profound reality checks.

The initial hype has faded, leaving CIOs to drive real enterprise value. Based on my experience implementing Google, OpenAI and Anthropic technologies, here are the fundamental, technology-agnostic lessons every leader must anchor their strategy around.

AI as a leadership multiplier

The most common tactical error we see is treating AI as an isolated technology project. What I have observed among our customers is that true success does not come from organizations that define a standalone “AI strategy,” but rather from those leaders that integrate AI into their business strategy.

When our customers isolate AI and define an AI strategy, it inevitably treats it like a “technological toy” to experiment with. This approach yields fragmented, orphaned initiatives that fail to scale because they are fundamentally disconnected from their core corporate objectives. What I learned is that AI is not the ultimate destination; it is a powerful catalyst. We have replaced “What can AI do for our customers?” with a more strategic question, “How does AI accelerate their existing business goals?”

Think of AI like electricity. No modern corporation designs a standalone “electricity strategy.” Instead, all companies route it invisibly across the entire organization to illuminate offices, power production lines and drive communication. AI must be woven into the enterprise fabric in the exact same way, acting as an underlying utility that supercharges your existing operational model.

Integrating AI into the broader business strategy also dictates how we measure success. It forces a shift away from short-term tech vanity metrics and anchors the technology into a long-term roadmap.

When AI remains trapped within the IT department of our customers, we notice that it is relegated to a mere “software experiment.” To become a true competitive advantage, we observed that AI requires intense cross-functional orchestration. This perspective does not diminish the merit of the technical team; their expertise is fundamental for establishing the architecture, data governance and tools your enterprise requires. However, while IT builds the foundational infrastructure, it lacks the organizational authority to decide what should be built on top of it. Only the CEO or the owner of the company can step in to ensure AI leaves the “toy project” phase and integrates into the DNA of the organization.

The requirement for top-down, executive ownership stems from three critical realities observed in the field:

• Silo-smashing and data collaboration: True enterprise AI is data-hungry and that data lives across disparate business lines, finance, operations, marketing and customer service. Only the CEO possesses the cross-functional authority to demand that data silos be dismantled.
• Cultural transformation and fear mitigation: AI triggers widespread anxiety over job displacement across all industries and hierarchies. When relegated to an “IT project,” resistance spikes as teams view it as a threat to their livelihoods. When I saw the CEO lead this cultural shift directly is when I noticed the best results.
• C-Suite education and strategic alignment: The mandate for AI capability cannot just be delegated downward; the transformation must begin at the very top. I have conducted more than 70 presentations for the Board of Directors and C-Level teams. These people need to be actively educated not on technical code, but on specific business use cases, return on investment (ROI) frameworks and how AI resolves core organizational bottlenecks.

PwC’s data found that only 12% of enterprises have achieved both cost and revenue benefits from AI. Those elite 12% succeeded precisely because their CEOs embedded AI extensively across strategic decision-making and cross-functional workflows. AI is simply too disruptive and too critical to be left exclusively in the hands of technical experts. If AI is not on the CEO’s weekly agenda, it is fundamentally missing from the company’s true strategy.

AI as a new operational framework

Traditional IT systems have operated on strict algorithmic certainty: if you input a specific set of data, the system executes an immutable line of code and guarantees the same, predictable output every single time.

AI completely breaks this paradigm. Because modern AI is built on probabilistic models, it does not execute static formulas; instead, it predicts the most likely correct response based on mathematical probabilities. This means that AI solutions carry an inherent, small percentage of uncertainty and variability. A prompt entered today might yield a slightly different, though contextually valid, output tomorrow.

Executive leadership and organizational cultures must be actively educated to accept and navigate this fundamental shift. Traditional quality assurance frameworks for software are designed for a 100% success rate. Applying this rigid standard to AI will paralyze your initiatives, keeping 80% of your projects trapped eternally in the pilot phase. This happened to us in a food and beverage company in Latin America a couple of years ago. After this experience, we started to include conditions in our contracts that tolerate statistical margins of error and still define the project as a success.

In terms of cost calculation, we had to teach CIOs and business managers to forget the monthly subscription model for AI and learn to manage the primary unit of exchange in modern AI: the token.

To understand AI costs, executives must understand how large language models process data. AI models do not read full words; instead, they break text, images or code down into “pieces” called tokens. As a baseline, every 100 words process as approximately 130 to 140 tokens. Because the major AI providers use the token as their currency, your business is billed dynamically based on the exact volume of tokens consumed by every query submitted (input) and every response generated (output).

Many leaders believe AI costs are fixed due to flat-rate enterprise tiers ($25–$30/user). This is a temporary illusion. These venture-capital-subsidized rates mask true operational costs and come with dynamic usage limits. Modeling long-term ROI on them guarantees a severe budget shock when true consumption pricing takes over.

The solution is not to halt AI adoption; doing so means losing your competitive edge. Instead, the cost per token must cease to be treated as a technical footnote relegated to the IT department. It must be elevated to a core business variable.

Risks in the AI adoption model

Since the beginning of the AI boom, I have seen all our customers making a critical tactical error that could cost them heavily in the medium term: they are focusing only on operational efficiency (reducing costs with AI).

I have observed that an alarmingly high percentage of companies remain trapped in pilot phases focused exclusively on short-term cost reduction. Bain & Company’s global Automation and AI Pathfinder Survey found that the largest share of companies measuring their AI initiatives (exactly 40%) realized cost reductions of 10% or less, heavily missing their internal targets. Our customers are putting too many resources and effort into marginal financial gains and in doing so, they are jeopardizing their most valuable assets: service quality, resilience and customer trust.

Utilizing AI solely to slash headcount or cut operational corners is a dangerous trap that introduces severe field liabilities. A financial service organization in Latin America announced that they saved $1 million in customer support by replacing humans with AI chatbots. However, the mid-term reality revealed a different story: a damaged brand reputation due to AI errors and an influx of frustrated clients fleeing because the automated system cannot handle special cases.

Putting a company on an extreme AI diet might make it look leaner on next quarter’s financial statement, but over-indexing on cost-cutting will ultimately leave the business too weak to compete when market dynamics shift. We are now inviting our customers to change the question from “How much money will AI save us?” to “How will we leverage AI to exponentially increase the long-term value of our enterprise?”

Deploying enterprise AI is a marathon, not a sprint, and the terrain changes with every mile. The organizations that thrive in this next era will be those that transition from fascination to discipline, treating AI not as a magic bullet for immediate savings, but as a core capability that demands rigorous governance, architectural foresight and cultural maturity. Navigating this shift requires moving past the theoretical hype and anchoring decisions in raw, field-tested reality.

As we continue to deploy these technologies across industries, the blueprint for success is being rewritten in real time. Let’s keep this conversation going as we map out the future of business intelligence together.

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Late in a large enterprise SAP transformation, the strategic governance conversations began to drift. Instead of executive decisions, we found ourselves debating whether the program needed dedicated testing, whether cutover required a full weekend, whether twenty Agile teams really needed coordination support and whether offshore resources were adding value at all.

The questions were not coming from the delivery teams. They were coming from the executive sponsor.

The sponsor had recently been elevated into a senior leadership role and had never sponsored a technology transformation at this scale. The challenge was not authority. The sponsor had every right to ask any question they wanted. The challenge was that strategic governance had quietly turned into a debate about delivery practices, because the sponsor did not yet have the transformation context to focus the conversation anywhere else.

This is not a story about a bad sponsor. The executive in this case was a capable senior leader with strong judgment and authentic intent. They had been placed into a role they had not yet been prepared for, and the pattern that followed was structural, not personal. It is one of the more common patterns I have observed across enterprise transformation programs, and one of the most consistently misdiagnosed.

Most program failures are not execution failures. They are sponsor mismatches.

When governance becomes a debate about delivery practices

When the sponsor does not understand what an enterprise transformation actually requires, governance forums stop functioning as decision bodies and start functioning as practice debates.

You see it in the questions that get asked. Why do we need a dedicated testing phase? Can the Build timeline be compressed? Why does cutover need a full weekend? Why do we need more Scrum Masters across 20 product teams? Can the US team simply work longer hours instead of using offshore resources? On one program, the sponsor suggested shifting the entire project’s working hours to India time, despite roughly 85 percent of the delivery organization being based in the United States.

None of these questions are unreasonable in isolation. Each one targets a real cost or timeline pressure. The problem is what is missing underneath them: an understanding of the operational risks the original choices were designed to mitigate.

When sponsors ask delivery-practice questions without that context, the program leadership team ends up defending the work instead of advancing it. Decision velocity drops. Trust between the program and its sponsor erodes. Senior delivery talent disengages from governance forums where the conversation never reaches the decisions they need made. What looks from the outside like an active sponsor producing engagement is, from inside the program, an active drain on the cycles needed to deliver.

The compounding cost is not unique to any single program. PMI’s research on executive sponsorship consistently identifies sponsor engagement quality, rather than sponsor presence alone, as one of the strongest predictors of project success. The visible symptom is debate. The actual cost is unmade decisions.

Authority is rarely the issue. Literacy is

When transformations stall under a mismatched sponsor, the diagnostic instinct is to question the sponsor’s authority. Are they senior enough? Do they have the cross-functional reach? Can they unblock?

In most of the programs I have led or advised, authority was not the limiting factor. The sponsor in the SAP program above had ample authority. They could unblock any decision the program needed. What had not been developed was the transformation literacy to know which decisions mattered, which were technical noise and which were execution risks that should not be optimized away.

This is what I have come to think of as the literacy problem. Sponsors elevated into transformation roles often have deep functional expertise (finance, operations, business unit leadership) but limited exposure to the distinct functions of PMO, organizational change management, agile delivery, testing and cutover, and how each one reduces a specific category of implementation risk. They are not expected to be SAP configuration experts. But they need enough transformation literacy to recognize which questions actually belong in a steering committee.

Harvard Business Review’s research on effective executive sponsorship has emphasized that sponsorship effectiveness depends as much on judgment as on authority. Judgment is where literacy becomes operational. A sponsor with authority but limited transformation literacy will optimize for speed and cost in ways that consistently underestimate risk. A sponsor with both will make the tradeoffs the program actually needs.

Prosci’s longstanding benchmark studies on change management have ranked active and visible executive sponsorship as the single greatest contributor to change success for two decades. The word that matters in that finding is active. Active sponsorship without transformation literacy can introduce real cost. Not because the sponsor is acting against the program, but because the optimization choices they make are based on incomplete information about what the program is built to protect against.

Shift the conversation from delivery practices to business risk

When the sponsor relationship is already in place and cannot be changed, the program leadership team has one move that consistently works: shift the conversation.

On the SAP program above, we stopped explaining why the testing phase existed. We started explaining the business risk of reducing it. We stopped debating the number of Scrum Masters. We started connecting delivery capacity to coordination across more than twenty Agile teams and the business cost of losing that coordination. We reframed offshore support as a way to maintain delivery momentum around the clock rather than asking the U.S. team to sustain fifteen-hour days.

The shift is from defending delivery practice to explaining business risk. The sponsor does not need to understand why testing takes the time it does. They need to understand what the program is exposed to if testing is compressed. They do not need to know how many Scrum Masters are statistically optimal for twenty Agile teams. They need to know what coordination breaks when the number is wrong.

This reframing accomplishes two things. First, it brings the conversation back to the level at which sponsors actually make decisions: tradeoffs between business outcomes and business risks. Second, it builds transformation literacy in the sponsor over time, almost as a byproduct. By the third or fourth iteration of business-risk-framed conversations, the sponsor begins to ask the right questions on their own.

In practice, this happens through small but deliberate moves. When the sponsor asks why a phase needs the time it takes, the program lead names two or three things that could go wrong if the time is cut and what each would cost the business. When the sponsor asks why a role is needed, the program lead names the work that would not get done without it. Every delivery-practice question gets converted into a business-risk answer.

The program leadership team’s job is not to make the sponsor an expert in SAP delivery. It is to provide enough transformation context so that executive decisions reflect both business priorities and implementation realities.

There are a few phrases I have used with executive sponsors over the years that capture the underlying issue. The sharpest one:

If the decision has to go above the sponsor, they are not the sponsor.

Sponsorship is defined by what the sponsor can decide without asking someone else. That is the test. Anything else is the appearance of sponsorship, not the substance.

For CIOs supporting enterprise transformation, the implication is direct. Sponsor selection, or sponsor preparation when selection is not an option, is not a hierarchy question. It is a transformation capability question. The same execution discipline that goes into defining decision rights, structuring governance and protecting delivery momentum should apply, with equal rigor, to assessing sponsor fit and building sponsor literacy before the program begins.

A sponsor does not need to be the technical expert. They do need to know when to trust the people who are.

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For years, I have been saying that cybersecurity is no longer a technology problem. It has become a business leadership challenge.

Yet, despite record levels of spending, ever-growing security teams, increasingly sophisticated technologies and a constant stream of new regulations, organizations continue to suffer major cyber incidents with alarming regularity. Every week seems to bring news of another ransomware attack, supply chain compromise or data breach affecting organizations that many would have assumed were well protected.

The obvious conclusion is that we are asking the wrong questions.

Too many executive teams remain preoccupied with the latest threat actor, the newest security product the CISO wants to buy or the latest vulnerability making headlines. Those issues matter, but they are not what should be keeping CEOs awake at night.

In my view, there are three far more fundamental issues that deserve the attention of every chief executive.

1. Corporate complexity, and the widening gap between business leadership and cybersecurity reality

Perhaps the biggest cybersecurity risk facing large organizations today is not technical at all.

It is the growing disconnect between executive perception and operational reality.

Many boards genuinely believe their organizations are reasonably well protected. They receive regular dashboards showing improving maturity scores, increasing compliance levels, falling vulnerability counts and reassuring traffic-light reports.

Unfortunately, cyber attackers do not read dashboards.

Behind those executive reports often lies an increasingly complex technology landscape, thousands of unmanaged digital assets, ageing infrastructure, rampant shadow IT, fragmented ownership, inconsistent governance and security teams struggling to keep pace with relentless business change.

The problem is rarely a lack of effort.

It is that corporate complexity has reached a level where traditional governance mechanisms are no longer capable of providing an accurate picture of organizational resilience.

Executives believe they understand the level of cyber risk they face because they receive regular reports. Those reports often measure activity rather than resilience.

Governance committees end up debating around another percentage point of phishing awareness or vulnerability remediation, while fundamental issues remain unaddressed in the background.

2. Organizational inertia, and the need for executive structure to evolve faster

Cyber criminals continue to evolve rapidly. Large organizations generally do not.

This is the second issue that should concern every CEO.

Throughout my career, I have observed organizations repeatedly responding to new cyber threats by adding another technology platform, another monitoring capability, another compliance framework or another governance committee.

Very rarely do they stop to redesign how cybersecurity operates.

The result is what I described several years ago as the “Cybersecurity Spiral of Failure”.

• As complexity and regulation increase, organizations invest in more security products.
• More products create more complexity.
• More products and greater complexity generate more alerts.
• More alerts require more analysts.
• More analysts produce more reports.
• More reports continue to build up executive confidence.
• Meanwhile, the underlying structural weaknesses remain largely unchanged, technical debt piles up and costs escalate.

And when the inevitable breach eventually happens, reality reveals itself, but distrust also sets in between senior executives and security teams.

This is not a funding problem. Nor is it a skills problem. It is fundamentally an operating model problem.

Many organizations continue trying to solve twenty-first century challenges using governance, accountability, organizational and reporting structures designed twenty-five years ago.

The cybersecurity function itself has evolved dramatically. Many executive structures have not.

This organizational inertia extends beyond technology: It affects budgeting cycles, investment priorities, procurement processes, accountability models and decision-making speed.

Cyber attackers innovate every day. Organizational change often takes years.

That imbalance should worry every CEO.

3. Accelerating technological disruption, and how it challenges organizations in areas where they are intrinsically weak

The third issue is potentially the most significant over the coming decade.

• Artificial intelligence, autonomous agents and machine identities
• Software supply chain complexity.
• Quantum computing, and post-quantum cryptography

Each of these developments represents far more than another technical trend.

Together, they fundamentally change the dynamics of cybersecurity.

Artificial intelligence is transforming countless business processes. At the same time, it is also increasing both the speed and sophistication of cyber-attacks while simultaneously transforming defensive capabilities.

Organizations have become increasingly dependent on software ecosystems that extend far beyond their own direct control. Engaging with the supply chain in ways that lead to a genuine appreciation of the risks involved has become a key challenge for most cybersecurity practices.

Quantum computing may eventually invalidate much of today’s cryptographic algorithms, forcing organizations into one of the largest technology efforts since Y2K — but without the benefit of a fixed deadline and faced by a problem that is considerably more complex and hyperconnected IT estates that have little to do with those of the late 90s.

None of these challenges can be solved overnight: They require clear governance, sustained investment over a few years and cross-functional organizational coordination.

Most large organizations are weak on those three fronts: This is precisely why CEOs should be focusing on them now.

Waiting until some of those risks become obvious will almost certainly be too late.

Businesses naturally prioritise immediate commercial pressures. Cybersecurity often involves preparing for risks whose timing remains uncertain.

But one of the greatest leadership failures I keep seeing remains the inability of organizations to act decisively on known unknowns.

That tension explains why many organizations delay action until external events force them to respond. Unfortunately, cybersecurity rarely rewards late action.

Leadership will determine who succeeds

Cybersecurity discussions still frequently focus on technology. I believe they should focus far more on leadership.

Technology will continue evolving. Threat actors will continue adapting. Regulations will continue expanding. Those developments are inevitable.

What remains within the control of every CEO is how their organization responds.

Does cybersecurity remain an IT issue? Or is it recognised as an integral part of business resilience?

How is cybersecurity accountability assigned at executive level? Or does it still rest largely with a CISO hidden in the organization?

Does the board spend sufficient time discussing resilience? Or does cybersecurity appear only when approving budgets or reviewing incidents?

These questions will increasingly determine organizational success.

The companies that navigate the next decade successfully will not necessarily be those spending the most on cybersecurity. Nor will they be those deploying the latest security technologies first.

They will be the organizations whose leadership recognises that cybersecurity has become a permanent business capability — embedded into governance, strategy, operational decision-making and organizational culture.

That transformation cannot be delegated. It begins with the CEO.

And perhaps that is the single biggest issue that should keep every chief executive awake at night: Not when the next cyber-attack will happen, but whether their organization is evolving quickly enough on those matters to meet a threat landscape that is changing much faster than the business itself.

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Atos has launched a new sovereign cloud platform aimed squarely at European public sector bodies, healthcare providers and defense organizations. It’s the latest effort by European companies in their fight back against US dominance.

Atos Sovereign Cloud offers a range of controls for data management, providing customers with resilience and full control over their data, complying with existing European legislation on data residency.

“Digital sovereignty has become a global operational priority for organizations that need to manage dependencies, jurisdictional exposure and disruption risks across complex digital environments. Atos Sovereign Cloud gives customers a practical way to apply sovereign controls to their most critical workloads, while preserving flexibility, resilience and the ability to innovate securely,” said Michael Kollar, Atos Group digital sovereignty leader.

There has been a concerted effort by European organizations to meet US competition head-on. Earlier this month, four companies signed up to the certification program introduced by the European cloud body, CISPE,

This is the latest effort by Atos to get the company back on track. In April, it sold its supercomputer company, Bull to the French government, after a previous attempt in 2024 to sell off its computer services division and after a refinancing deal in the same year.

The company implemented a further round of refinancing this year and in its half-yearly report claimed that this was an “important milestone” in securing the company’s future. However, in its first-quarter results revenue showed an organic decline of 11 percent, so any projected growth may be some time in the future. Whether its Sovereign Cloud offering is the harbinger of the revival remains to be seen.

Atos has launched a new sovereign cloud platform aimed squarely at European public sector bodies, healthcare providers and defense organizations. It’s the latest effort by European companies in their fight back against US dominance.

Atos Sovereign Cloud offers a range of controls for data management, providing customers with resilience and full control over their data, complying with existing European legislation on data residency.

“Digital sovereignty has become a global operational priority for organizations that need to manage dependencies, jurisdictional exposure and disruption risks across complex digital environments. Atos Sovereign Cloud gives customers a practical way to apply sovereign controls to their most critical workloads, while preserving flexibility, resilience and the ability to innovate securely,” said Michael Kollar, Atos Group digital sovereignty leader.

There has been a concerted effort by European organizations to meet US competition head-on. Earlier this month, four companies signed up to the certification program introduced by the European cloud body, CISPE,

This is the latest effort by Atos to get the company back on track. In April, it sold its supercomputer company, Bull to the French government, after a previous attempt in 2024 to sell off its computer services division and after a refinancing deal in the same year.

The company implemented a further round of refinancing this year and in its half-yearly report claimed that this was an “important milestone” in securing the company’s future. However, in its first-quarter results revenue showed an organic decline of 11 percent, so any projected growth may be some time in the future. Whether its Sovereign Cloud offering is the harbinger of the revival remains to be seen.

This article first appeared on CIO.

Atos has launched a new sovereign cloud platform aimed squarely at European public sector bodies, healthcare providers and defense organizations. It’s the latest effort by European companies in their fight back against US dominance.

Atos Sovereign Cloud offers a range of controls for data management, providing customers with resilience and full control over their data, complying with existing European legislation on data residency.

“Digital sovereignty has become a global operational priority for organizations that need to manage dependencies, jurisdictional exposure and disruption risks across complex digital environments. Atos Sovereign Cloud gives customers a practical way to apply sovereign controls to their most critical workloads, while preserving flexibility, resilience and the ability to innovate securely,” said Michael Kollar, Atos Group digital sovereignty leader.

There has been a concerted effort by European organizations to meet US competition head-on. Earlier this month, four companies signed up to the certification program introduced by the European cloud body, CISPE,

This is the latest effort by Atos to get the company back on track. In April, it sold its supercomputer company, Bull to the French government, after a previous attempt in 2024 to sell off its computer services division and after a refinancing deal in the same year.

The company implemented a further round of refinancing this year and in its half-yearly report claimed that this was an “important milestone” in securing the company’s future. However, in its first-quarter results revenue showed an organic decline of 11 percent, so any projected growth may be some time in the future. Whether its Sovereign Cloud offering is the harbinger of the revival remains to be seen.

I have worked alongside enterprise technology for more than 30 years and watched AI evolve from a lab experiment into the modern boardroom’s core focus. However, the last few years of implementing AI alongside our customers have delivered our most profound reality checks.

The initial hype has faded, leaving CIOs to drive real enterprise value. Based on my experience implementing Google, OpenAI and Anthropic technologies, here are the fundamental, technology-agnostic lessons every leader must anchor their strategy around.

AI as a leadership multiplier

The most common tactical error we see is treating AI as an isolated technology project. What I have observed among our customers is that true success does not come from organizations that define a standalone “AI strategy,” but rather from those leaders that integrate AI into their business strategy.

When our customers isolate AI and define an AI strategy, it inevitably treats it like a “technological toy” to experiment with. This approach yields fragmented, orphaned initiatives that fail to scale because they are fundamentally disconnected from their core corporate objectives. What I learned is that AI is not the ultimate destination; it is a powerful catalyst. We have replaced “What can AI do for our customers?” with a more strategic question, “How does AI accelerate their existing business goals?”

Think of AI like electricity. No modern corporation designs a standalone “electricity strategy.” Instead, all companies route it invisibly across the entire organization to illuminate offices, power production lines and drive communication. AI must be woven into the enterprise fabric in the exact same way, acting as an underlying utility that supercharges your existing operational model.

Integrating AI into the broader business strategy also dictates how we measure success. It forces a shift away from short-term tech vanity metrics and anchors the technology into a long-term roadmap.

When AI remains trapped within the IT department of our customers, we notice that it is relegated to a mere “software experiment.” To become a true competitive advantage, we observed that AI requires intense cross-functional orchestration. This perspective does not diminish the merit of the technical team; their expertise is fundamental for establishing the architecture, data governance and tools your enterprise requires. However, while IT builds the foundational infrastructure, it lacks the organizational authority to decide what should be built on top of it. Only the CEO or the owner of the company can step in to ensure AI leaves the “toy project” phase and integrates into the DNA of the organization.

The requirement for top-down, executive ownership stems from three critical realities observed in the field:

• Silo-smashing and data collaboration: True enterprise AI is data-hungry and that data lives across disparate business lines, finance, operations, marketing and customer service. Only the CEO possesses the cross-functional authority to demand that data silos be dismantled.
• Cultural transformation and fear mitigation: AI triggers widespread anxiety over job displacement across all industries and hierarchies. When relegated to an “IT project,” resistance spikes as teams view it as a threat to their livelihoods. When I saw the CEO lead this cultural shift directly is when I noticed the best results.
• C-Suite education and strategic alignment: The mandate for AI capability cannot just be delegated downward; the transformation must begin at the very top. I have conducted more than 70 presentations for the Board of Directors and C-Level teams. These people need to be actively educated not on technical code, but on specific business use cases, return on investment (ROI) frameworks and how AI resolves core organizational bottlenecks.

PwC’s data found that only 12% of enterprises have achieved both cost and revenue benefits from AI. Those elite 12% succeeded precisely because their CEOs embedded AI extensively across strategic decision-making and cross-functional workflows. AI is simply too disruptive and too critical to be left exclusively in the hands of technical experts. If AI is not on the CEO’s weekly agenda, it is fundamentally missing from the company’s true strategy.

AI as a new operational framework

Traditional IT systems have operated on strict algorithmic certainty: if you input a specific set of data, the system executes an immutable line of code and guarantees the same, predictable output every single time.

AI completely breaks this paradigm. Because modern AI is built on probabilistic models, it does not execute static formulas; instead, it predicts the most likely correct response based on mathematical probabilities. This means that AI solutions carry an inherent, small percentage of uncertainty and variability. A prompt entered today might yield a slightly different, though contextually valid, output tomorrow.

Executive leadership and organizational cultures must be actively educated to accept and navigate this fundamental shift. Traditional quality assurance frameworks for software are designed for a 100% success rate. Applying this rigid standard to AI will paralyze your initiatives, keeping 80% of your projects trapped eternally in the pilot phase. This happened to us in a food and beverage company in Latin America a couple of years ago. After this experience, we started to include conditions in our contracts that tolerate statistical margins of error and still define the project as a success.

In terms of cost calculation, we had to teach CIOs and business managers to forget the monthly subscription model for AI and learn to manage the primary unit of exchange in modern AI: the token.

To understand AI costs, executives must understand how large language models process data. AI models do not read full words; instead, they break text, images or code down into “pieces” called tokens. As a baseline, every 100 words process as approximately 130 to 140 tokens. Because the major AI providers use the token as their currency, your business is billed dynamically based on the exact volume of tokens consumed by every query submitted (input) and every response generated (output).

Many leaders believe AI costs are fixed due to flat-rate enterprise tiers ($25–$30/user). This is a temporary illusion. These venture-capital-subsidized rates mask true operational costs and come with dynamic usage limits. Modeling long-term ROI on them guarantees a severe budget shock when true consumption pricing takes over.

The solution is not to halt AI adoption; doing so means losing your competitive edge. Instead, the cost per token must cease to be treated as a technical footnote relegated to the IT department. It must be elevated to a core business variable.

Risks in the AI adoption model

Since the beginning of the AI boom, I have seen all our customers making a critical tactical error that could cost them heavily in the medium term: they are focusing only on operational efficiency (reducing costs with AI).

I have observed that an alarmingly high percentage of companies remain trapped in pilot phases focused exclusively on short-term cost reduction. Bain & Company’s global Automation and AI Pathfinder Survey found that the largest share of companies measuring their AI initiatives (exactly 40%) realized cost reductions of 10% or less, heavily missing their internal targets. Our customers are putting too many resources and effort into marginal financial gains and in doing so, they are jeopardizing their most valuable assets: service quality, resilience and customer trust.

Utilizing AI solely to slash headcount or cut operational corners is a dangerous trap that introduces severe field liabilities. A financial service organization in Latin America announced that they saved $1 million in customer support by replacing humans with AI chatbots. However, the mid-term reality revealed a different story: a damaged brand reputation due to AI errors and an influx of frustrated clients fleeing because the automated system cannot handle special cases.

Putting a company on an extreme AI diet might make it look leaner on next quarter’s financial statement, but over-indexing on cost-cutting will ultimately leave the business too weak to compete when market dynamics shift. We are now inviting our customers to change the question from “How much money will AI save us?” to “How will we leverage AI to exponentially increase the long-term value of our enterprise?”

Deploying enterprise AI is a marathon, not a sprint, and the terrain changes with every mile. The organizations that thrive in this next era will be those that transition from fascination to discipline, treating AI not as a magic bullet for immediate savings, but as a core capability that demands rigorous governance, architectural foresight and cultural maturity. Navigating this shift requires moving past the theoretical hype and anchoring decisions in raw, field-tested reality.

As we continue to deploy these technologies across industries, the blueprint for success is being rewritten in real time. Let’s keep this conversation going as we map out the future of business intelligence together.

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ISC2 has begun developing a vendor-neutral AI security certification aimed at cybersecurity professionals working to secure AI systems and manage emerging AI risks.

The nonprofit organization, best known for the CISSP certification, says it is seeking volunteers worldwide to help define the knowledge and skills that will shape the new credential. While ISC2 has not finalized the certification domains, the certification is expected to address both technical AI security and governance topics, with a pilot exam planned before the end of 2026.

According to Casey Marks, ISC2 chief operating officer, feedback from cybersecurity practitioners led ISC2 to conclude that AI security had grown beyond expanding AI content within existing certifications.

“AI has reached a tipping point,” Marks says. “AI no longer is just another tool; instead, it has fundamentally changed the cybersecurity practice itself.”

ISC2 already includes AI-related content in certifications including CISSP and CCSP, but Marks says practitioners have identified new responsibilities and risks that extend beyond those programs. “Enterprise security teams are currently grappling with significant knowledge gaps, particularly around securing model architectures against new vulnerabilities like prompt injection, data poisoning, and model inversion,” Marks adds.

Organizations are working to understand emerging governance frameworks, including the NIST AI Risk Management Framework and ISO/IEC 42001, while adapting traditional application security and security operations workflows to AI systems, he says.

ISC2 has not finalized the certification domains, but Marks says the organization expects the credential to address both technical controls and governance practices for securing AI systems and managing AI risk. The certification will use ISC2’s established certification development process, which relies on cybersecurity practitioners to define job roles, develop exam content, and validate competencies.

Marks says ISC2 will continue to update the certification through ongoing input from cybersecurity professionals, in addition to its regular certification review process.

The organization is also determining which professionals the certification will target. Marks says AI security responsibilities are emerging across security architecture, risk management, security operations, software development security, governance and compliance, communication and network security, and security assessment and testing. ISC2 says the certification will reflect how those roles are evolving.

For organizations that are building AI security programs now, Marks recommends using existing AI training resources, adopting established governance frameworks, creating cross-functional AI security working groups, and participating in the certification development process.

Marks says ISC2 expects AI knowledge to become part of most cybersecurity roles while a more specialized AI security discipline continues to develop. He says organizations will increasingly need professionals with foundational AI security knowledge, as well as specialists in areas such as adversarial machine learning, model architectures, and AI data pipelines.

Looking ahead, Marks says he expects AI security expertise to evolve into both a foundational skill for cybersecurity professionals and a specialized discipline of its own.

“At this time, we are seeing a hybrid evolution occurring in real time: AI security is simultaneously becoming a baseline expectation for all security roles, while also carving out a dedicated, highly specialized discipline,” Marks says.

For years, I have been saying that cybersecurity is no longer a technology problem. It has become a business leadership challenge.

Yet, despite record levels of spending, ever-growing security teams, increasingly sophisticated technologies and a constant stream of new regulations, organizations continue to suffer major cyber incidents with alarming regularity. Every week seems to bring news of another ransomware attack, supply chain compromise or data breach affecting organizations that many would have assumed were well protected.

The obvious conclusion is that we are asking the wrong questions.

Too many executive teams remain preoccupied with the latest threat actor, the newest security product the CISO wants to buy or the latest vulnerability making headlines. Those issues matter, but they are not what should be keeping CEOs awake at night.

In my view, there are three far more fundamental issues that deserve the attention of every chief executive.

1. Corporate complexity, and the widening gap between business leadership and cybersecurity reality

Perhaps the biggest cybersecurity risk facing large organizations today is not technical at all.

It is the growing disconnect between executive perception and operational reality.

Many boards genuinely believe their organizations are reasonably well protected. They receive regular dashboards showing improving maturity scores, increasing compliance levels, falling vulnerability counts and reassuring traffic-light reports.

Unfortunately, cyber attackers do not read dashboards.

Behind those executive reports often lies an increasingly complex technology landscape, thousands of unmanaged digital assets, ageing infrastructure, rampant shadow IT, fragmented ownership, inconsistent governance and security teams struggling to keep pace with relentless business change.

The problem is rarely a lack of effort.

It is that corporate complexity has reached a level where traditional governance mechanisms are no longer capable of providing an accurate picture of organizational resilience.

Executives believe they understand the level of cyber risk they face because they receive regular reports. Those reports often measure activity rather than resilience.

Governance committees end up debating around another percentage point of phishing awareness or vulnerability remediation, while fundamental issues remain unaddressed in the background.

2. Organizational inertia, and the need for executive structure to evolve faster

Cyber criminals continue to evolve rapidly. Large organizations generally do not.

This is the second issue that should concern every CEO.

Throughout my career, I have observed organizations repeatedly responding to new cyber threats by adding another technology platform, another monitoring capability, another compliance framework or another governance committee.

Very rarely do they stop to redesign how cybersecurity operates.

The result is what I described several years ago as the “Cybersecurity Spiral of Failure”.

• As complexity and regulation increase, organizations invest in more security products.
• More products create more complexity.
• More products and greater complexity generate more alerts.
• More alerts require more analysts.
• More analysts produce more reports.
• More reports continue to build up executive confidence.
• Meanwhile, the underlying structural weaknesses remain largely unchanged, technical debt piles up and costs escalate.

And when the inevitable breach eventually happens, reality reveals itself, but distrust also sets in between senior executives and security teams.

This is not a funding problem. Nor is it a skills problem. It is fundamentally an operating model problem.

Many organizations continue trying to solve twenty-first century challenges using governance, accountability, organizational and reporting structures designed twenty-five years ago.

The cybersecurity function itself has evolved dramatically. Many executive structures have not.

This organizational inertia extends beyond technology: It affects budgeting cycles, investment priorities, procurement processes, accountability models and decision-making speed.

Cyber attackers innovate every day. Organizational change often takes years.

That imbalance should worry every CEO.

3. Accelerating technological disruption, and how it challenges organizations in areas where they are intrinsically weak

The third issue is potentially the most significant over the coming decade.

• Artificial intelligence, autonomous agents and machine identities
• Software supply chain complexity.
• Quantum computing, and post-quantum cryptography

Each of these developments represents far more than another technical trend.

Together, they fundamentally change the dynamics of cybersecurity.

Artificial intelligence is transforming countless business processes. At the same time, it is also increasing both the speed and sophistication of cyber-attacks while simultaneously transforming defensive capabilities.

Organizations have become increasingly dependent on software ecosystems that extend far beyond their own direct control. Engaging with the supply chain in ways that lead to a genuine appreciation of the risks involved has become a key challenge for most cybersecurity practices.

Quantum computing may eventually invalidate much of today’s cryptographic algorithms, forcing organizations into one of the largest technology efforts since Y2K — but without the benefit of a fixed deadline and faced by a problem that is considerably more complex and hyperconnected IT estates that have little to do with those of the late 90s.

None of these challenges can be solved overnight: They require clear governance, sustained investment over a few years and cross-functional organizational coordination.

Most large organizations are weak on those three fronts: This is precisely why CEOs should be focusing on them now.

Waiting until some of those risks become obvious will almost certainly be too late.

Businesses naturally prioritise immediate commercial pressures. Cybersecurity often involves preparing for risks whose timing remains uncertain.

But one of the greatest leadership failures I keep seeing remains the inability of organizations to act decisively on known unknowns.

That tension explains why many organizations delay action until external events force them to respond. Unfortunately, cybersecurity rarely rewards late action.

Leadership will determine who succeeds

Cybersecurity discussions still frequently focus on technology. I believe they should focus far more on leadership.

Technology will continue evolving. Threat actors will continue adapting. Regulations will continue expanding. Those developments are inevitable.

What remains within the control of every CEO is how their organization responds.

Does cybersecurity remain an IT issue? Or is it recognised as an integral part of business resilience?

How is cybersecurity accountability assigned at executive level? Or does it still rest largely with a CISO hidden in the organization?

Does the board spend sufficient time discussing resilience? Or does cybersecurity appear only when approving budgets or reviewing incidents?

These questions will increasingly determine organizational success.

The companies that navigate the next decade successfully will not necessarily be those spending the most on cybersecurity. Nor will they be those deploying the latest security technologies first.

They will be the organizations whose leadership recognises that cybersecurity has become a permanent business capability — embedded into governance, strategy, operational decision-making and organizational culture.

That transformation cannot be delegated. It begins with the CEO.

And perhaps that is the single biggest issue that should keep every chief executive awake at night: Not when the next cyber-attack will happen, but whether their organization is evolving quickly enough on those matters to meet a threat landscape that is changing much faster than the business itself.

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Late in a large enterprise SAP transformation, the strategic governance conversations began to drift. Instead of executive decisions, we found ourselves debating whether the program needed dedicated testing, whether cutover required a full weekend, whether twenty Agile teams really needed coordination support and whether offshore resources were adding value at all.

The questions were not coming from the delivery teams. They were coming from the executive sponsor.

The sponsor had recently been elevated into a senior leadership role and had never sponsored a technology transformation at this scale. The challenge was not authority. The sponsor had every right to ask any question they wanted. The challenge was that strategic governance had quietly turned into a debate about delivery practices, because the sponsor did not yet have the transformation context to focus the conversation anywhere else.

This is not a story about a bad sponsor. The executive in this case was a capable senior leader with strong judgment and authentic intent. They had been placed into a role they had not yet been prepared for, and the pattern that followed was structural, not personal. It is one of the more common patterns I have observed across enterprise transformation programs, and one of the most consistently misdiagnosed.

Most program failures are not execution failures. They are sponsor mismatches.

When governance becomes a debate about delivery practices

When the sponsor does not understand what an enterprise transformation actually requires, governance forums stop functioning as decision bodies and start functioning as practice debates.

You see it in the questions that get asked. Why do we need a dedicated testing phase? Can the Build timeline be compressed? Why does cutover need a full weekend? Why do we need more Scrum Masters across 20 product teams? Can the US team simply work longer hours instead of using offshore resources? On one program, the sponsor suggested shifting the entire project’s working hours to India time, despite roughly 85 percent of the delivery organization being based in the United States.

None of these questions are unreasonable in isolation. Each one targets a real cost or timeline pressure. The problem is what is missing underneath them: an understanding of the operational risks the original choices were designed to mitigate.

When sponsors ask delivery-practice questions without that context, the program leadership team ends up defending the work instead of advancing it. Decision velocity drops. Trust between the program and its sponsor erodes. Senior delivery talent disengages from governance forums where the conversation never reaches the decisions they need made. What looks from the outside like an active sponsor producing engagement is, from inside the program, an active drain on the cycles needed to deliver.

The compounding cost is not unique to any single program. PMI’s research on executive sponsorship consistently identifies sponsor engagement quality, rather than sponsor presence alone, as one of the strongest predictors of project success. The visible symptom is debate. The actual cost is unmade decisions.

Authority is rarely the issue. Literacy is

When transformations stall under a mismatched sponsor, the diagnostic instinct is to question the sponsor’s authority. Are they senior enough? Do they have the cross-functional reach? Can they unblock?

In most of the programs I have led or advised, authority was not the limiting factor. The sponsor in the SAP program above had ample authority. They could unblock any decision the program needed. What had not been developed was the transformation literacy to know which decisions mattered, which were technical noise and which were execution risks that should not be optimized away.

This is what I have come to think of as the literacy problem. Sponsors elevated into transformation roles often have deep functional expertise (finance, operations, business unit leadership) but limited exposure to the distinct functions of PMO, organizational change management, agile delivery, testing and cutover, and how each one reduces a specific category of implementation risk. They are not expected to be SAP configuration experts. But they need enough transformation literacy to recognize which questions actually belong in a steering committee.

Harvard Business Review’s research on effective executive sponsorship has emphasized that sponsorship effectiveness depends as much on judgment as on authority. Judgment is where literacy becomes operational. A sponsor with authority but limited transformation literacy will optimize for speed and cost in ways that consistently underestimate risk. A sponsor with both will make the tradeoffs the program actually needs.

Prosci’s longstanding benchmark studies on change management have ranked active and visible executive sponsorship as the single greatest contributor to change success for two decades. The word that matters in that finding is active. Active sponsorship without transformation literacy can introduce real cost. Not because the sponsor is acting against the program, but because the optimization choices they make are based on incomplete information about what the program is built to protect against.

Shift the conversation from delivery practices to business risk

When the sponsor relationship is already in place and cannot be changed, the program leadership team has one move that consistently works: shift the conversation.

On the SAP program above, we stopped explaining why the testing phase existed. We started explaining the business risk of reducing it. We stopped debating the number of Scrum Masters. We started connecting delivery capacity to coordination across more than twenty Agile teams and the business cost of losing that coordination. We reframed offshore support as a way to maintain delivery momentum around the clock rather than asking the U.S. team to sustain fifteen-hour days.

The shift is from defending delivery practice to explaining business risk. The sponsor does not need to understand why testing takes the time it does. They need to understand what the program is exposed to if testing is compressed. They do not need to know how many Scrum Masters are statistically optimal for twenty Agile teams. They need to know what coordination breaks when the number is wrong.

This reframing accomplishes two things. First, it brings the conversation back to the level at which sponsors actually make decisions: tradeoffs between business outcomes and business risks. Second, it builds transformation literacy in the sponsor over time, almost as a byproduct. By the third or fourth iteration of business-risk-framed conversations, the sponsor begins to ask the right questions on their own.

In practice, this happens through small but deliberate moves. When the sponsor asks why a phase needs the time it takes, the program lead names two or three things that could go wrong if the time is cut and what each would cost the business. When the sponsor asks why a role is needed, the program lead names the work that would not get done without it. Every delivery-practice question gets converted into a business-risk answer.

The program leadership team’s job is not to make the sponsor an expert in SAP delivery. It is to provide enough transformation context so that executive decisions reflect both business priorities and implementation realities.

There are a few phrases I have used with executive sponsors over the years that capture the underlying issue. The sharpest one:

If the decision has to go above the sponsor, they are not the sponsor.

Sponsorship is defined by what the sponsor can decide without asking someone else. That is the test. Anything else is the appearance of sponsorship, not the substance.

For CIOs supporting enterprise transformation, the implication is direct. Sponsor selection, or sponsor preparation when selection is not an option, is not a hierarchy question. It is a transformation capability question. The same execution discipline that goes into defining decision rights, structuring governance and protecting delivery momentum should apply, with equal rigor, to assessing sponsor fit and building sponsor literacy before the program begins.

A sponsor does not need to be the technical expert. They do need to know when to trust the people who are.

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OpenAI has launched Presence, an enterprise service for deploying voice and chat agents that can resolve customer and employee requests, potentially automating some work now handled by frontline support teams.

The agents can answer questions and operate IT systems, and enterprises can decide what actions the agents may take and when they should seek human approval for actions or transfer a case to a human.

OpenAI is already using Presence internally for its English-language phone support channel, where it verifies callers and uses account information to complete approved actions. The company said the system resolves 75% of inbound issues without human assistance.

Another OpenAI service, Codex, can be used to monitor agents and suggest updates or improvements to processes. In OpenAI’s own tests, suggestions from Codex helped reduce handoffs to humans by 15 percentage points over 10 days, it said. Presence also includes simulation and evaluation tools that allow companies to test an agent before deployment. The tests assess whether it reaches the correct outcome, follows company policy, and hands a case to an employee when required.

OpenAI intends each Presence deployment to deal with one kind of task, for example billing issues, insurance claims, or employee IT service requests, with agents getting only the knowledge and system access required for that task.

Presence is not a self-service product: Enterprises will have to sign up for the limited availability program, with integration performed by OpenAI or selected global systems integrators.

Companies exploring or testing Presence include Spanish bank BBVA, which is evaluating the service for everyday banking support in Mexico, and Japanese technology group SoftBank, which is using it in trials involving Japanese-language customer interactions. Australian insurer IAG is assessing whether the technology can help it respond to surges in customer demand during severe weather events.

Workforce impact

OpenAI’s announcement did not address the potential effect of Presence on employment. But its claimed automation rate raises questions about how the technology could affect staffing in customer service and other support functions.

Pareekh Jain, CEO of Pareekh Consulting, said CIOs should regard the 75% figure as evidence that the technology can work, rather than as a benchmark that every enterprise can expect to reach.

Jain said OpenAI’s deployment benefits from being built around the company’s own products and data. Large enterprises may achieve lower automation rates because they must contend with fragmented legacy systems, uneven knowledge bases and more complex compliance demands.

“Most organizations should expect lower initial automation levels that improve over time as the AI agent is refined,” Jain said.

The first workforce effect is more likely to be slower hiring than immediate layoffs, according to Tulika Sheel, senior vice president at Kadence International.

“The roles most exposed are likely to be repetitive, high-volume functions such as frontline customer support and routine back-office processing,” Sheel said. “However, I would expect the first impact to be on hiring and team growth rather than immediate large-scale job cuts. Over time, enterprises may redesign roles around AI-assisted workflows, with humans focusing more on complex cases, escalation, and relationship management.”

Jain said Tier-1 support agents handling predictable queries would face the most exposure. Broader reductions would become more likely only after companies reorganize their operations around the technology.

However, Lian Jye Su, chief analyst at Omdia, said Presence is unlikely to increase the threat of job displacement because companies have used similar customer-support automation from vendors such as Genesys, NiCE, Five9 and AWS for years.

Enterprises are more likely to use Presence alongside employees, with AI handling routine requests while people remain responsible for work requiring judgment and empathy, Su said.

Cost and operational risks

Analysts said CIOs should examine whether Presence can maintain resolution quality as usage grows, since fewer human handoffs could leave employees dealing with a more difficult mix of cases.

“The key question is not simply how many tasks AI can handle, but whether it can handle them reliably at scale,” Sheel said.

The financial case will depend partly on the cost of connecting Presence to existing systems and maintaining the controls needed to govern its use, according to Jain. “Often the biggest cost of enterprise AI is not tokens but integration and governance,” Jain added.

Companies will need to determine what systems and data the agents can access, monitor their performance, and audit the actions they take. Those investments could offset early savings.

Su said the complexity of enterprise IT will make it difficult for OpenAI to automate entire workflows on its own. Enterprises will still need to work with other technology providers and human employees, while CIOs will favor systems that can be audited and integrated with existing infrastructure.

Jain said the economics could improve if companies use the same integrations and governance controls across additional workflows.

OpenAI has launched Presence, an enterprise service for deploying voice and chat agents that can resolve customer and employee requests, potentially automating some work now handled by frontline support teams.

The agents can answer questions and operate IT systems, and enterprises can decide what actions the agents may take and when they should seek human approval for actions or transfer a case to a human.

OpenAI is already using Presence internally for its English-language phone support channel, where it verifies callers and uses account information to complete approved actions. The company said the system resolves 75% of inbound issues without human assistance.

Another OpenAI service, Codex, can be used to monitor agents and suggest updates or improvements to processes. In OpenAI’s own tests, suggestions from Codex helped reduce handoffs to humans by 15 percentage points over 10 days, it said. Presence also includes simulation and evaluation tools that allow companies to test an agent before deployment. The tests assess whether it reaches the correct outcome, follows company policy, and hands a case to an employee when required.

OpenAI intends each Presence deployment to deal with one kind of task, for example billing issues, insurance claims, or employee IT service requests, with agents getting only the knowledge and system access required for that task.

Presence is not a self-service product: Enterprises will have to sign up for the limited availability program, with integration performed by OpenAI or selected global systems integrators.

Companies exploring or testing Presence include Spanish bank BBVA, which is evaluating the service for everyday banking support in Mexico, and Japanese technology group SoftBank, which is using it in trials involving Japanese-language customer interactions. Australian insurer IAG is assessing whether the technology can help it respond to surges in customer demand during severe weather events.

Workforce impact

OpenAI’s announcement did not address the potential effect of Presence on employment. But its claimed automation rate raises questions about how the technology could affect staffing in customer service and other support functions.

Pareekh Jain, CEO of Pareekh Consulting, said CIOs should regard the 75% figure as evidence that the technology can work, rather than as a benchmark that every enterprise can expect to reach.

Jain said OpenAI’s deployment benefits from being built around the company’s own products and data. Large enterprises may achieve lower automation rates because they must contend with fragmented legacy systems, uneven knowledge bases and more complex compliance demands.

“Most organizations should expect lower initial automation levels that improve over time as the AI agent is refined,” Jain said.

The first workforce effect is more likely to be slower hiring than immediate layoffs, according to Tulika Sheel, senior vice president at Kadence International.

“The roles most exposed are likely to be repetitive, high-volume functions such as frontline customer support and routine back-office processing,” Sheel said. “However, I would expect the first impact to be on hiring and team growth rather than immediate large-scale job cuts. Over time, enterprises may redesign roles around AI-assisted workflows, with humans focusing more on complex cases, escalation, and relationship management.”

Jain said Tier-1 support agents handling predictable queries would face the most exposure. Broader reductions would become more likely only after companies reorganize their operations around the technology.

However, Lian Jye Su, chief analyst at Omdia, said Presence is unlikely to increase the threat of job displacement because companies have used similar customer-support automation from vendors such as Genesys, NiCE, Five9 and AWS for years.

Enterprises are more likely to use Presence alongside employees, with AI handling routine requests while people remain responsible for work requiring judgment and empathy, Su said.

Cost and operational risks

Analysts said CIOs should examine whether Presence can maintain resolution quality as usage grows, since fewer human handoffs could leave employees dealing with a more difficult mix of cases.

“The key question is not simply how many tasks AI can handle, but whether it can handle them reliably at scale,” Sheel said.

The financial case will depend partly on the cost of connecting Presence to existing systems and maintaining the controls needed to govern its use, according to Jain. “Often the biggest cost of enterprise AI is not tokens but integration and governance,” Jain added.

Companies will need to determine what systems and data the agents can access, monitor their performance, and audit the actions they take. Those investments could offset early savings.

Su said the complexity of enterprise IT will make it difficult for OpenAI to automate entire workflows on its own. Enterprises will still need to work with other technology providers and human employees, while CIOs will favor systems that can be audited and integrated with existing infrastructure.

Jain said the economics could improve if companies use the same integrations and governance controls across additional workflows.

This article first appeared on CIO.

I haven’t slept much in the past few weeks. Not because of some theoretical cyber risk that keeps many executives awake, but because reality just delivered a real wake-up call to our industry — a call that every executive must answer, now.

Imagine this: A major global enterprise, a company most of us interact with indirectly every single day, wakes up to find its entire digital environment obliterated. Thousands of employees in dozens of offices and remote locations are suddenly offline. Customers are cut off, supply chains grind to a halt and regulators are notified with a chilling admission: “We have no idea when we’ll be back.”

This wasn’t ransomware. There was no negotiation, no decryption key to buy, no easy way out. It was destruction — deliberate, coordinated and geopolitically motivated — not monetary.

As a chief customer officer who’s worked with countless customers on cyberattack risks, my perspective hits a bit differently than a CISO or a CTO. I see the aftermath, not just the attack surface. I see the faces behind the tickets, the operations team locked out of their own systems, the support agent answering panicked calls at dawn. And I ask: How many organizations have actually stress-tested their response to this scenario — not a hypothetical, but this very real, lights-out event? Here’s what every leader needs to confront today:

Recovery is not just a technical exercise

The first assumption to break during a real crisis is the belief that recovery is purely technical.

Many organizations have done tabletop exercises and have a backup and recovery playbook, so they feel prepared. They can point to backup windows, retention schedules and immutability controls. The moment a true blackout happens, a different reality surfaces. The people who own the recovery steps either do not know each other, lack the authority to make decisions without supervisor approval or need guidance from offline systems.

The reality is that technical infrastructure almost always holds up better than human infrastructure. Organizations have built their recovery strategy around the assumption that someone competent will be awake, available and empowered when a cyber event happens.

Still, backups are only as good as their independence. Let’s be blunt: If your recovery infrastructure shares identity, authentication or network trust with your Microsoft tenant (such as Azure, Microsoft 365 or Teams), you don’t actually have a recovery plan; you have a false sense of one — and a liability. A recent survey found that while 90% of organizations express confidence in their ability to recover from a cyber incident, fewer than one in three ransomware victims fully recovered their data.

True resilience means immutable, air-gapped backups, untouchable by the same compromise. Anything less is an illusion. I talk to customers about their recovery plans constantly. The customers who have rehearsed all scenarios sleep soundly. Those who haven’t? They’re rolling the dice.

Most business continuity plans ignore ‘total blackout’

I’ve reviewed hundreds of business continuity plans. Almost all assume partial failures — a region, an application, a data center. But what if every system, in every country, goes dark simultaneously? That’s an entirely different playbook. If your team hasn’t run a drill for a global, simultaneous outage, you’re not prepared. The probability is low, but the cost of being unready is existential.

Connected devices, OT systems, field hardware, partner integrations — they all plug into your enterprise network. When the core collapses, it’s not just IT at risk. It’s operational technology, physical safety systems and in regulated sectors, potentially human lives. Understanding and testing those interdependencies is non-negotiable.

This is also where boards need to change the conversation. A study found that only 5% of companies have cybersecurity experts on their board of directors. Recovery time objectives (RTOs) should not be buried in technical appendices. It’s all jargon to boards. That makes translation essential. RTOs must be explained in terms of business impact. “We can recover in four hours” is a technical statement. “Every hour of downtime costs us $2.3M and creates regulatory exposure in three jurisdictions” is a board statement.

That is the level of clarity leaders need.

The most prepared organizations do not wait for an incident to educate the board. They bring the conversation forward proactively. They frame recovery in business terms: revenue, regulatory standing, customer trust and brand reputation.

The most effective framing is often simple. Show the most critical systems. Show what happens if each one is down for one hour, four hours, 24 hours and 72 hours. Show the current recovery capability against each and then show the gap.

If your board is not demanding real answers, your business continuity strategy is likely underfunded and your business is exposed. This is a risk conversation worth forcing because the consequences do not stay inside IT. They can show up in customer churn or missed revenue and ruin an organization’s reputation.

Threat intelligence must be actionable, not archived

Geopolitical attacks, hacktivist campaigns and nation-state targeting aren’t abstract threats. They are active risks, and that intelligence cannot languish in the security team’s inbox. Executive leadership must be looped in — and immediately — so gaps can be closed before they’re exploited. Too often, intelligence enters the security operations function and never reaches the teams responsible for recovery infrastructure or executive decision-making.

If a threat actor is targeting a specific class of backup agents, the team responsible for those agents needs to know now, not two weeks from now. If intelligence suggests destructive activity against a sector, recovery owners need to validate isolation, access paths and restoration procedures immediately. If geopolitical tension increases the likelihood of targeting, executive leadership needs to understand what exposure exists and what actions are being taken. The organizations that survive aren’t just the best at incident response. They’re the ones who anticipated, rehearsed and invested before the attack.

Part of investing in a recovery strategy requires closing the loop between signal and action. The most prepared organizations have already mapped their critical recovery dependencies to specific threat categories. When intelligence touches one of those categories, there is a named owner and a clear set of actions. No guessing or forwarding emails into the void is needed because the distance between the warning and the employees’ ability to do something is shortened.

Looking ahead, the conversation will continue to evolve beyond traditional cyber response. Because in an AI-enabled enterprise, the new question is whether the data within those systems can still be trusted. When AI systems make decisions based on enterprise data, the attack surface becomes the data’s accuracy. A threat actor who quietly corrupts a dataset over 90 days before a recovery event has done more damage than just downtime. They can poison the inputs driving decisions across the business.

Regardless of how AI will change threat intelligence and cyber response, these principles remain the same. Know your problem, whether structural or technological. Ensure your human infrastructure keeps pace with your technical infrastructure, with clear cross-functional ownership and the tools and knowledge to act autonomously. Communicate with your boards often — and correctly.

Let’s not wait for the next headline to ask, “Are we ready?” Have those conversations now. Test your assumptions. Close your gaps. Because in today’s threat landscape, resilience isn’t IT’s job — it’s everyone’s mandate.

This article is published as part of the Foundry Expert Contributor Network.
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Microsoft 365 Director Jeremy Chapman this month took to video to tell Windows admins that the days of delaying security patches are over.

Complex enterprise systems and historic incidents involving patch problems have caused many admins to hold fire on immediately applying security patches, in many cases deferring patch rollouts for two to four weeks or more to ensure stability. Microsoft argues that this cautious approach, though understandable, is no longer viable because AI is accelerating the discovery and exploitation of software vulnerabilities.

As a result, Microsoft has advised admins to act on patches within three days.

Independent experts agree with Microsoft’s diagnosis of the problems posed by AI-powered vulnerability discovery, but many say Microsoft’s three-day remediation window is unrealistic for large enterprises with heavy testing, change-control, and compatibility constraints.

Instead of taking a blanket approach, enterprises need to focus more on quickly resolving those vulnerabilities that are under active exploitation and relevant to their environments, according to critics of Microsoft’s revised approach.

Tighter patching deadlines

Microsoft’s revised vulnerability remediation advice comes in the wake of its work with Anthropic’s Project Glasswing and findings from Microsoft’s own MDASH multi-model agentic scanning harness. Tighter patching deadlines are configurable via Windows Autopatch and Microsoft Intune or update tooling options such as Microsoft Configuration Manager and Windows Server Update Services.

As IT environments become increasingly more complex, inadvertent issues can occur with what appears to be a simple patch.

Unique or complex deployments may not be compatible with a patch, resulting in potential data corruption, system shutdown, or the dreaded “Blue Screen of Death.” Multiple vendors in the operating system and the enterprise software and security market have released patches that have broken products and caused outages, so the issue goes well beyond Windows shops.

Increasing both the volume and the speed of patching is unsustainable for most security teams because organizations are already struggling with successful remediation as it is.

“Many organizations have patch windows, review cycles, and test environments to identify these issues prior to patching production environments,” says Scott Caveza, senior research manager at exposure management and vulnerability assessment firm Tenable. “Organizations lacking the resources for extended validation risk deploying faulty patches that cause downtime or force last-minute configuration changes.”

Caveza adds: “The mitigation steps will vary for each organization, but blindly relying on auto-updates without contextual validation is not a defensible security posture.”

CISA’s Known Exploited Vulnerabilities list and other industry data suggest that only a small fraction of disclosed vulnerabilities are confirmed as exploited in the wild.

“[Enterprises should focus on] identifying vulnerabilities with credible and functional PoCs, verified exploitation, or sustained attention from ransomware groups, threat actors, and botnets,” says Caitlin Condon, vice president of security research at VulnCheck. “Timely exploit intelligence helps organizations identify the bugs that require immediate attention, while allowing lower-risk issues to proceed through appropriate testing and change control.”

Other independent experts are more sympathetic to Microsoft’s argument that AI has made vulnerability discovery and exploit development faster than ever and, as a result, the risks of delaying patches are far greater.

“Organizations sometimes delay patches to protect the uptime of critical systems, and many updates still require a restart,” says Danny Jenkins, CEO and co-founder at endpoint protection technology vendor ThreatLocker. “Some teams also stay one update cycle behind because they are concerned that a new patch could introduce bugs or break an overlooked dependency. Unfortunately, delaying patches to preserve uptime is becoming much harder to justify.”

Jenkins adds: “Organizations should not leave critical systems exposed while waiting for the next maintenance window. Patches should still be tested, but that process needs to move quickly, with the highest priority given to vulnerabilities that are actively exploited or exposed to the internet. A controlled interruption is usually far less costly than a successful attack exploiting a known vulnerability.”

Wider cross-industry impact

Microsoft’s three-day recommendation reflects a fundamental change in the threat landscape. Other vendors might be expected to follow suit and that means CISOs need to revise their approach to vulnerability remediation.

“Organizations should expect faster disclosure-to-exploitation timelines to become the norm, which means security programs must emphasize automation, trusted software supply chains, and continuous visibility rather than relying on periodic maintenance windows,” says Mike Nelson, VP and field CTO at DigiCert.

AI is compressing the time between vulnerability discovery and exploitation, and the industry is moving rapidly from 30-, 60-, and 90-day patching windows toward a matter of days.

However a “blanket three-day requirement for every vulnerability is neither realistic nor safe for most large organizations,” says Jeff Williams, founder and CTO at Contrast Security.

Failing to patch opens up security threats, but rushing an inadequately tested patch into production creates operational risk.

“The goal cannot be to treat every CVE [vulnerability] as an emergency,” according to Williams. “It has to be identifying, within hours, which vulnerabilities are actually exploitable and require immediate action.”

Holistic remediation

Security teams are already facing significant pressure to patch faster and to remediate a rising tide of new vulnerabilities, yet many practitioners are losing ground. Verizon’s Data Breach Investigation Report, published earlier this year, found that the median time to patch had actually increased to 43 days.

Patch deployment in enterprise environments involves configuration changes, reviews, testing, and validation.

Enterprises need to become more proficient at exposure management so that they have a holistic view of their environment that’s necessary to identify which assets are at greatest risk.

“By pinpointing the misconfigurations, identity flaws, and specific vulnerabilities that pose the greatest risk to their environment, security teams can prioritize exactly what to patch first,” Tenable’s Caveza says. “The idea of ‘patch everything’ is really outdated, and ‘patch faster’ isn’t feasible with the rapidly increasing number of vulnerabilities disclosed each day.”

CISOs will have to re-engineer their vulnerability and exposure management processes. “The traditional model of scanning everything, assigning generic severity scores, and tilting at a massive and expanding backlog is no longer fast enough,” says Contrast Security’s Williams.

Organizations need to identify the small number of vulnerabilities that matter, protect against them immediately, and remediate them on a timeline the business can safely support.

Enterprises should prioritize on resolving “internet facing, remotely exploitable vulnerabilities and any of the CISA Known Exploited Vulnerability list,” says Jose Lejin, an IEEE senior member.

Businesses that cannot safely validate and deploy patches within three days still have options, including “compensating controls, reducing an asset’s exposure, or in some cases removing the component entirely, all of which shrink the exploitable risk and buy time to patch properly,” says Brad Hibbert, CSO of vulnerability management provider Brinqa.

AI agents can turn a clear description into working software, the engineer’s judgement is what makes the difference: deciding what to build, catching the tradeoff the agent didn’t know to weigh, and owning the call on whether the result is right.

That judgement has always been the hard part of engineering. It just used to be bundled into the act of writing code, where a skilled engineer did it while typing. As agents take on more of the typing, that judgement separates out and becomes the clear center of the role. Leaders who adapt early will get their metrics, their talent pipelines, and their delivery models working with this shift rather than against it.

Judgement is defining, constraining, and deciding

Judgement is all about defining the problem precisely enough that an agent builds the right thing. It’s setting the constraints the agent won’t infer on its own. It’s spotting the tradeoff buried three layers down that only shows up if you understand the system. And it’s looking at a finished implementation and knowing whether it’s genuinely good enough to ship.

This is the harder part of the job and the real driver of quality. It was easy to underrate when it lived inside day-to-day coding. Now it’s what separates a strong team from an average one.

The shift changes where time, growth, and metrics go

If the high-value activity is intent, review, and judgement rather than raw output, a few assumptions are worth revisiting.

Where engineers spend their time. Less of the day goes to producing boilerplate and mechanical implementation, and more goes to the reasoning that used to get squeezed to the edges: framing the problem and owning the judgement calls that determine quality.

How teams grow their people. Defining problems well, spotting risk, and critically evaluating work you didn’t write yourself have always been senior skills. When agents handle more of the mechanical work, those skills become learnable earlier. That puts the emphasis on leaders to teach the reasoning: why a choice gets made and how to weigh the tradeoffs that come with it.

What you measure. Lines shipped, tickets closed, and velocity charts all measured throughput of the old scarce resource. They say very little about the new one. The teams that adapt will start measuring the quality of intent going in and the reliability of judgement coming out, because that’s where the results now live.

Reinvest the time you get back

The tempting response is to treat the freed-up capacity as pure speed: same work, same tooling, just faster. That captures the easy win and misses the real one. If engineers spend their reclaimed time reviewing a rising volume of agent output with no better context than before, review quietly becomes the new constraint, and you’ve moved the problem rather than solved it.

The organizations that get ahead will invest the reclaimed capacity into the judgement layer: creating stronger specs and acceptance criteria before work starts, building review practices that test agent output against intent, and capturing the reasoning behind decisions where the next person can find it, so it doesn’t have to be reconstructed every time. That’s how the shift becomes an advantage for your team.

The through-line for leaders

The engineering job is moving up a level, from executing the work to directing and validating it. That’s a more strategic role, and it rewards clarity of thought over speed of output. Leaders who see the shift early can help their engineers grow into the work that’s now most valuable.

See how leading engineering organizations are operationalizing this shift at jira.dev.

CyCognito, a leading exposure management platform, today introduced Continuous AI Pentesting. The new capability bakes AI-driven offensive pentesting directly into the platform, leveraging the rich context it already maintains for every exposed asset. This enables CyCognito to deliver AI pentesting as a continuous service, circumventing the cost and coverage constraints that confine comparable solutions to periodic, narrowly scoped engagements.

With this new solution, CyCognito addresses a major shift in the security ecosystem, driven by the latest advances in AI. Today’s models, with more advanced ones on the way, have lowered the bar for attackers. An attack campaign that once required a group of skilled threat actors can now be carried out by a low-skilled individual, in a fraction of the time and at relatively low cost. This signals a tectonic shift that compels defenders to adopt the same technology to keep pace and close the security gaps in their own environment.

Continuous AI Pentesting: Solution architecture, at a glance.

“AI pentesting is rapidly becoming part of every security team’s toolkit, and a lot of it is already being done in-house,” said Rob Gurzeev, CEO and co-founder of CyCognito. “But running offensive AI isn’t the hard part. The challenge is scale. AI pentesting today is typically limited to the top 1% of priority assets. Meanwhile, the other 99% is where a lot of attacks actually start, where adversaries find the low-hanging fruit and use it as a foothold for lateral movement.”

To provide AI pentesting coverage across that overlooked 99%, CyCognito built a distinct architecture that centers on the Target Graph, a contextual graph that bridges the AI pentesting solution and CyCognito’s three core modules:

• Exposure Assessment maps the external footprint, attributes every asset to the right part of the organization, and enriches it with business and stack context.
• Exposure Validation runs more than 100,000 deterministic tests continuously, freeing the AI pentesters to focus on high-judgment work.
• Threat Intelligence draws on the history of existing and emerging vulnerabilities, along with attacker playbooks and statistical models trained on past engagements, to anticipate attacker activity.

Together, these layers increase the effectiveness of the pentesting agents, equipping them with the rich context and exploitability evidence, dramatically improving the efficiency of every run.

The architecture is also built to be constantly self-evolving. Every new risk scenario AI pentesters uncover can be hardcoded into the Exposure Validation module, joining the deterministic tests it already runs. This frees the AI agents to pursue new threats, and also consolidates learnings from agentic tests in a way that will benefit every CyCognito customer.

In the announcement for this new feature, the company also shared some of the vulnerabilities:

• Unauthenticated access to a production CRM: an exposed MCP server allowed anonymous, natural-language queries against three million rows of account, opportunity, and financial data, with no credentials required.
• A publicly readable RAG index: an AI agent stack enforced authentication only on its API, leaving the knowledge base behind it, which held customer data, contracts, and internal communications, open to anyone on the internet.
• A building’s access controls exposed to the internet: a system running door locks, card readers, and CCTV sat unsegmented on the public internet alongside the organization’s AI document tools and chatbot, leaving physical entry reachable by a remote attacker.

These examples are just some of the risk scenarios identified through the work on this new capability, now running with select design partners, including major enterprises and Fortune 500 companies. Internally, CyCognito refers to the project as Project Kineto, after the Kinetograph, the first motion picture camera.

“The name echoes our vision for what AI pentesting should be,” said Gurzeev. “Security testing has always been a snapshot. AI lets us turn it into continuous motion: an always-on stream of change-aware tests that runs across your entire attack surface at machine speed, with the skill of a seasoned security expert.”

To go deeper on Continuous AI Pentesting, read the full announcement post: https://www.cycognito.com/blog/new-continuous-ai-pentesting/

About CyCognito

CyCognito is an external exposure management platform that reduces risk by discovering, testing and prioritizing security issues. 

The platform scans billions of websites, cloud applications and APIs and uses advanced AI to identify the most critical risks and guide remediation. Emerging companies, government agencies and Fortune 500 organizations rely on CyCognito to secure and protect from growing threats. For more information, visit https://www.cycognito.com.

Contact

VP Marketing

Igal Zeifman

CyCognito

igal.zeifman@cycognito.com

You are one of the six billion people (75% of the world population) online today, and every click you make is routed through the data center. Data centers, whether knowingly or unknowingly, play a very critical role in your daily online activities. With an increasing population, increasing usage of online presence, and now omniscient AI, the demand for data centers has increased manyfold, and the trend seems similar to the year 2000, when telephone towers were built to accommodate increased digital presence.

To win the AI race, Hyperscalers (Google, Meta, Amazon, Microsoft, Alibaba, Oracle, IBM, Tencent) are spending huge amounts of money on data center development. In the USA, the hyperscalers are planning to spend $650 billion in 2026, which is around 70% higher than 2025 spending, according to Yahoo Finance.

As per McKinsey research, by 2030, companies will invest around $7 trillion in Capex on data center infrastructure globally. More than $4 trillion will go towards computing hardware investment. More than 40% of this spending will be invested in the United States.

Demand growth in data centers

McKinsey analysis shows that global demand for data center capacity can more than triple by 2030, with a compound annual growth rate (CAGR) of around 22 per cent. In the USA, data center demand could grow by 20-25 per cent at the same time.  

The data center industry is currently undergoing a violent transition. We are moving away from the era of “bespoke projects” — where every facility was a unique architectural feat — into an era of industrialized infrastructure. With global capital expenditure in the sector projected to hit $3 trillion by 2028, the “bottleneck” has shifted. It is no longer about securing the capital; it is about the physics of the supply chain.

During my tenure at Vantage, managing the intersection of data center construction management (DCCM) and infrastructure management (DCIM), I saw firsthand that the most successful players aren’t those with the deepest pockets, but those with the most integrated data threads. If your construction data in Procore doesn’t talk to your financial reality in Yardi, or your operational capacity in DCIM, you aren’t building a data center — you’re managing a $500 million blind spot.

The death of “sticks and bricks”

Traditionally, data center construction was treated as civil engineering. But for the modern CIO, a data center is a complex product assembly.

The challenges are systemic. We are facing 50-to-80-week lead times for critical “long-pole” items: extra-high-voltage transformers, switchgear, and the liquid cooling manifolds required for the next generation of AI chips. In this environment, the traditional reactive supply chain model is a liability.

To survive the $3 trillion inflow, we must adopt a hybrid-agile SCOR (supply chain operations reference) model. This means applying continuous flow logic to standardized components (like modular power skids) while maintaining agile responsiveness for the volatile IT layer.

The digital bridge: Construction management software  to ERP

The most significant opportunity for CIOs lies in financial-operational integration. In many organizations, there is a data chasm between the construction site and the corporate office. Construction teams live in the construction management software tracking tasks, trades, RFIs and payment submittals. Finance teams operate corporate offices with project management tools (worth remembering that email is a key tool besides spreadsheets and phone calls) tracking capex schedule, commissioning timeline, capital drawdowns and asset lifecycle management.

These systems are siloed; the CIO loses visibility into the total cost to serve. By integrating construction management into the financial system, we create real-time financial visibility of the build. We can see exactly how a three-week delay in a chiller delivery impacts the internal rate of return (IRR) of the entire asset. This isn’t just accounting; it’s strategic telemetry.

From BIM to DCIM: The lifecycle thread

The second bridge is the handoff from construction (BIM) to operations (DCIM). Historically, this handoff was a nightmare of PDFs and Excel sheets. By the time the operations team took the keys, the “as-built” design information was already out of date.

The opportunity today is to maintain a continuous data thread. The sensor data and asset tags established during the “make” phase in our SCOR model should flow directly into the DCIM. This allows us to perform virtual commissioning. Before a single server is racked, we should already have a digital replica of the airflow, power distribution, and cooling capacity.

The scientific inference: AI in the supply chain

As someone who has led data and AI initiatives, I’ve seen the hype. But in the supply chain, the application of AI must be pragmatic, not generative. We don’t need AI to write poems; we need it for predictive procurement. Most organizations manage their procurement in ERP or a mix of a few tools to manage the source-to-settle business flow. Adopting a system workflow improves data collection and the state of the procurement cycle, which in turn provides AI with the context to draw inferences for possible delays and anomalies in original specifications and change orders.

By applying machine learning to global logistics data, we can move from just-in-time to just-in-case modeling. AI can analyze geopolitical risks, shipping lane congestion, and raw material pricing to tell a CIO: “Order your switchgear 14 months early, or your Q3 2027 ‘Power On’ date is at risk.”

Bringing it all together: AI in the supply chain and finance

Why it matters: Approximately 70% of the capex is on this workflow and making timely decisions that directly impact the ready-for-service dates. The current challenge of reactionary adjustment in design to procurement to local fit-out is a significant drain on capex efficiency and cost of capital. Because single-project delivery delays have become so volatile, a massive structural shift is occurring in how digital infrastructure is funded. Single-project debt (special purpose vehicles or SPVs) is facing severe friction. To insulate themselves from RFS shocks, the largest institutional players are moving toward permanent platform capital — aggregating exposure across dozens of global assets simultaneously.

Navigating these complex multi-billion-dollar engineering projects distributed over a large geography is simply unmanageable without rethinking and re-engineering existing tools and processes.

The roadmap for the modern CIO

To lead this transformation, CIOs must move beyond the IT shop mentality and become master orchestrators of the supply chain. Here is the 1500-word reality condensed into three mandates:

1. Standardize the product: Stop designing bespoke facilities. Move toward DFMA (design for manufacturing and assembly). If 70% of your data center can be built in a factory and shipped as modules, you bypass the unpredictability of on-site labor.
2. Integrate the financial stack: If your construction management software and your ERP aren’t sharing a heartbeat, your data is lying to you. Force the integration between Procore and Yardi.
3. Own the long poles: Don’t leave the procurement of transformers and cooling units to general contractors. Use your balance sheet to secure these items years in advance. In 2026, inventory is the new currency.

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Outsourcing worked – until it didn’t.

After Akirolabs achieved early market validation and onboarded its first enterprise customers, outsourcing began to create strategic limitations around scalability, intellectual property (IP) ownership, security and delivery execution.

The challenges started after the first enterprise customers confirmed product-market fit. At that point, delivery speed became directly tied to business growth. Product quality expectations increased. Infrastructure and security requirements became stricter. Investors started asking difficult but fair questions about IP ownership, operational dependencies and long-term scalability.

Most importantly, engineering execution was no longer just an operational function – it became part of the company’s strategic advantage. That was the moment when the founders decided the company needed dedicated technology leadership to address these challenges. This is how I joined the company at the beginning of 2023. As VP of Engineering and a bit later as CTO, I led the transformation (usually known as insourcing, repatriating or backsourcing) from an outsourced model to an internal engineering organization while maintaining product delivery continuity and preparing the company for the next growth stage. The process took roughly a year and involved not only technical migration, but also organizational design, hiring, process development, infrastructure modernization and cultural transformation – everything from the ground up.

Building an internal engineering organization while still delivering

One of the biggest misconceptions about insourcing is that it is primarily a technical project. It is a leadership and execution challenge.

When I joined the company, there was effectively no internal engineering structure, limited visibility into the existing system and no clear long-term technical strategy. My first months were dedicated to understanding reality and I began with a comprehensive assessment of the codebase, operational risks, documentation quality and knowledge dependencies to determine the most viable transition strategy.

Very early in the process, I faced a critical strategic decision: whether to gradually assume ownership of the existing platform or rebuild it internally. To make that decision, I evaluated four distinct transition models ranging from limited management insourcing to a complete internal rebuild.

After assessing the technical, operational and long-term business implications of each approach, I selected the most demanding option: rebuilding the product internally while maintaining uninterrupted delivery for existing customers. Although riskier in the short term, a full rebuild offered the clearest route to complete IP ownership, architectural flexibility and long-term scalability.

At the time, this decision ran counter to the approach typically taken by startups in similar situations. Most organizations gradually assume ownership of an existing codebase to minimize short-term risk and preserve delivery capacity. My assessment was that the accumulated architectural debt, fragmented knowledge distribution and long-term maintenance risks would ultimately make a phased takeover more expensive and less scalable than a controlled rebuild. The strategy required significantly higher execution discipline, but it allowed us to establish complete ownership of the platform, eliminate inherited constraints and create an architecture capable of supporting enterprise-scale growth.

The next challenge was hiring.

In Germany, hiring can easily take four to six months – mostly due to a typical 3-month notice period, which is incompatible with startup timelines. We solved this by building a hybrid organization structure early: a lean internal core team combined with carefully selected contractors. Instead of hiring only narrow specialists, we prioritized experienced generalists capable of operating across architecture, infrastructure, security and compliance discussions. Later, we evolved toward a product engineering model, where engineers owned broader product outcomes rather than narrowly defined technical functions.

During the first three months, we established a core engineering team of four senior engineers. Over the following nine months, the organization expanded to roughly fifteen engineers while I strategically designed and executed the transformation of the platform’s architecture to meet the rigorous deployment and compliance standards of our first enterprise clients, including Raiffeisen Bank International and Bertelsmann. This structural overhaul allowed the company to meet the deployment, security and compliance requirements of enterprise customers that had previously been inaccessible under the outsourced model. At that point, we had already achieved complete coverage across backend, frontend, DevOps, QA and security.

I also intentionally kept processes lightweight during the transition. Instead of introducing heavyweight frameworks, we focused on clarity of priorities, fast decision-making and execution discipline. We used Kanban over Scrum, eliminated unnecessary meetings, shortened the remaining ones and emphasized engineering culture over process overhead.

Another major challenge was project estimation. Because dual-track development was unavoidable until the in-house platform reached production readiness, estimation accuracy had a direct impact on budget efficiency. Despite all challenges, my initial estimate ultimately proved remarkably close to the final delivery date, differing by only about a week. Accurate forecasting under conditions of parallel development streams, ongoing customer commitments and active team formation became a critical leadership challenge. Maintaining this level of predictability throughout the transition helped align engineering execution with business planning, hiring decisions and investor expectations.

The engineering transformation enabled capabilities that contributed to Akirolabs being recognized as an IDC Innovator in Procurement in 2023, named amongst the Top 27 AI Startups in Germany in 2024, Sifted’s 100 Fastest-Growing Startups in DACH & CEE 2025 and inclusion in 2024-2026 in ProcureTech100 annual recognition of procurement technology providers shaping the future of digital procurement.

Managing risk without slowing down the business

The hardest part of insourcing is not writing code, selecting the technology stack, designing architecture or configuring infrastructure. It is avoiding disruption while the company is changing underneath the product. I successfully orchestrated the concurrent overhaul of product architecture, cross-functional engineering recruitment, infrastructure modernization and live customer operations under exceptionally tight margins.

To reduce delivery risk, we approached the transition in layers.

First, we focused on infrastructure reliability and operational readiness before feature expansion. Cloud architecture, recovery testing, permission segregation and incident management processes were implemented early, not after launch. We also introduced multiple testing stages and dedicated QA functions after learning the hard way that a “developers-only” quality control approach does not scale for complex web platforms and business domains.

Second, we established a structured knowledge-transfer process to rapidly onboard engineers and reduce external dependencies.

Third, we became extremely disciplined about scope management. One of the most common reasons insourcing initiatives fail is uncontrolled change during the rebuild phase. Every new feature request increases uncertainty non-linearly. We learned to separate strategic improvements from distractions and protect the core delivery roadmap aggressively. Throughout the transition, we successfully maintained uninterrupted customer operations by utilizing planned maintenance windows, achieved a near-zero-downtime migration and permanently doubled product velocity immediately following the migration.

Beyond the technical migration itself, the transition established a repeatable operating model for scaling technology organizations beyond the product-market-fit stage. The framework combined organizational redesign, controlled knowledge repatriation, architecture modernization and enterprise-grade operational practices while maintaining uninterrupted customer delivery throughout the transformation. While the implementation was specific to Akirolabs, the underlying principles are broadly applicable to organizations seeking to transition from outsourced development to internal product ownership without disrupting business operations.

By the time the new platform reached production readiness, I had established not only a functioning engineering organization, but also a stable operational model: internal ownership, production-grade infrastructure, security processes, scalable hiring practices and clear technology and product roadmaps.

A positive side effect of the transition was the creation of internal UI/UX and Data Science capabilities, which later became strategically important for AI product initiatives and created a foundation for the third version of the product, which we released in mid-2025.

My technical restructuring and migration to a secure proprietary platform reduced architectural risk, established full in-house ownership and helped strengthen investor confidence during the company’s successful €5M fundraising round in 2024.

The transition created a stronger foundation for scale and supported the company’s continued expansion among enterprise organizations operating at Fortune 500 scale, including Ahold Delhaize, Workday, IFF, Deutsche Bahn and others.

Lessons learned for CTOs considering insourcing

Looking back, several decisions made the transition successful, and several mistakes made it harder than necessary.

The first lesson is simple: decisiveness in strategic transition is paramount to maintaining business momentum. Rapidly evaluating insourcing frameworks and defining clear boundaries with the external partner allowed us to mitigate operational downtime and execute a highly efficient migration ahead of critical market deadlines.

Second, hire more senior people and do it as early as possible. Strong technical leaders multiply execution capacity far beyond their individual contribution. In our case, the quality of the first hires influenced architecture quality, hiring standards, delivery discipline and engineering culture for the entire organization.

Finally, culture matters more than frameworks. Processes can be added later. Ownership mentality cannot.

The biggest long-term advantage of bringing development in-house was not simply faster execution, not better code quality or operational cost optimization by over 30% after the transition which we also achieved. It was an alignment. Product strategy, engineering decisions, customer priorities and business goals became part of the same conversation instead of being separated by organizational boundaries. For technology companies operating in highly competitive markets, that alignment becomes a compounding advantage over time.

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As the CSO grows in prominence, security leaders are increasingly earning a seat at the executive table, reporting directly to the CEO with the expectation to help drive business strategy and ensure organizational success.

Reporting to the CEO unlocks greater access and influence for security leaders, and while CSOs who report to their organization’s CIO still have clout, it’s a very different experience picking up the phone to speak directly with the CEO as a strategic partner.

Regardless of reporting structure, CSOs must clearly understand what they are being tasked to solve. That might sound simple, but making the leap to being a CEO’s direct report requires a new perspective, a different set of skills, and a business-level focus on metrics to do so.

We asked several current CSOs, CEOs, and IT staffing experts for advice on how security executives can best navigate a direct reporting relationship with their CEO. Offering insights below are George Gerchow, CSO at Bedrock Data and member of the IANS faculty; Matt Chiodi, CSO of Cerby; Chris Schueler, CEO at Cyderes; and Greg Fuller, vice president of the Technology Skills Suite at Skillsoft.

1. Understand how the CEO views your role

Most CEOs expect that, when you report directly to them, you fully own your functional area. Whether it’s cybersecurity, operations, or finance, they look to you as the expert in that domain. The CEO may have opinions, but ultimately, you are expected to lead and provide direction.

CEOs expect their CSO to be a true strategic partner, not just a risk reporter — connecting cybersecurity to revenue protection, regulatory compliance, customer trust, and operational resilience. In turn, CSOs should expect CEOs to treat governance as a strategic enabler, not a bureaucratic necessity.

2. Power up on skills vital to your organization at an executive level

On the technology side, AI and machine learning, cloud security, incident response, zero trust architecture, and governance, risk, and compliance (GRC) are the areas where threats evolve fastest and strategic leadership has the greatest impact. 

Equally important are “power skills”: communication, critical thinking, adaptability, and emotional intelligence. The ability to translate complex risk into business terms is what separates a strong CSO from a purely technical one. Skills, not titles, define effectiveness in the eyes of a CEO.

3. Take advantage of your direct access

Direct access to the CEO will enable you to influence strategy, shape resilience planning, and ensure cybersecurity is treated as a business imperative rather than a cost center. That authority is strongest when the CEO understands cybersecurity as a strategic lever, not just a technical function. 

While a direct reporting relationship gives you access to the CEO, it also comes with the responsibility to operate at that level. You need to provide clear, executive-level visibility into your cybersecurity program.

4. Brush up on business translation

A CSO who leads with business alignment will always carry more influence when they can translate risk into business language rather than technical jargon. Building programs that must survive an IPO, a FedRAMP audit, and real customer scrutiny forces you to tie security to revenue and trust.

The most valuable skill is translation — defining technical risk in terms of executive action and business impact that a CEO and a board can act on. You must build trust through transparency. These are the human skills that complement technology, creating a collaborative human-AI dynamic where leaders make faster, better-informed decisions. 

5. Treat conversations as risk assessment opportunities

Highly effective security leaders treat every business conversation as a risk conversation in disguise. That mindset is what largely separates a great CSO from a great technologist. Earn the CEO’s trust by speaking business first, security second. Translate every risk into revenue, reputation, or regulatory exposure.

Remember, a good CEO wants a translator, not an alarm system. They expect no surprises, a clear read on the risks that matter, and a security leader who helps the business move faster rather than slowing it down.

6. Define what a successful relationship should look like and put it in writing

Regardless of the reporting relationship, start by defining the end goal and putting it in writing. It will evolve over time, but having that initial clarity is critical. This is especially important when you’re new in a role and aiming to make your first 60, 90, or 120 days, and your first year, successful. In such cases, it’s essential to align early.

Do that collaboratively, and document it.

7. Prioritize trust and candor

The CEO needs to trust that the CSO isn’t sandbagging, and the CSO needs enough psychological safety to deliver bad news fast. When those conditions exist, security becomes a strategic asset — not a cost center.

To that end, focus on clear communication above all, and present yourself as part of a team, not a solo player. Stay calm under pressure during incidents, and treat people as peers rather than policing them. The leaders who last build trust before they need it.

8. Treat governance as a strategic competitive advantage

The strongest partnerships also share a commitment to governance as a competitive advantage.

Governance is the brakes that let you drive fast safely. When a CSO and CEO are aligned on that principle, the organization can innovate with AI while maintaining oversight and protecting against unnecessary risk. The result is an organization that does not just react to threats but builds resilience into how it operates.

9. Set clear goals and measure progress

Setting clear goals and measuring progress against those goals is essential. When expectations are clear, the areas you need to focus on become much clearer. It doesn’t solve every problem, but aligning early with your leadership, whether that’s a CEO or a CIO, can significantly reduce the pressure you may feel.

Also, never let your boss be surprised. This is where being clear on goals and consistently tracking both leading and lagging metrics becomes especially important, particularly in a direct reporting relationship with the CEO.

10. Be willing to endure challenge and discomfort

Finally, persistence and a willingness to endure discomfort for something that matters more than the pain itself are critical to surviving in this relationship. The role of a cybersecurity leader is often thankless. If you’re doing your job well, no one really notices.

Was ist Claude Mythos?

Claude Mythos ist ein KI-Modell, das von Anthropic entwickelt wurde und für Anwendungen in den Bereichen Cybersicherheit und Gesundheitswesen optimiert ist. Ursprünglich wurde Mythos 5 im April einer kleinen Gruppe geprüfter Technologiepartner zugänglich gemacht – im Vorfeld eines geplanten, breiter angelegten Rollouts.

Zu diesem Zweck rief das KI-Unternehmen das Projekt „Glasswing“ ins Leben, ein Konsortium, das Infrastrukturanbietern, Open-Source-Entwicklern und großen Technologieunternehmen einen begrenzten, kontrollierten Zugriff zu Mythos gewährt. Ziel der Initiative ist es, Verteidigern zu ermöglichen, Schwachstellen schneller aufzuspüren und zu beheben, als Angreifer sie identifizieren können. Das erscheint auch dringend notwendig, setzen diese doch zunehmend selbst auf KI-gestützte Werkzeuge.

Über welche Fähigkeiten verfügt Claude Mythos?

Die ersten 50 Partner von Project Glasswing konnten mithilfe von Mythos mehr als 10.000 Schwachstellen mit hohem oder kritischem Schweregrad in allen gängigen Betriebssystemen und Webbrowsern aufspüren.

So identifizierte das Modell Sicherheitslücken, die selbst den fähigsten Sicherheitsforschern jahrelang entgangen waren – etwa einen 27 Jahre alten Fehler in OpenBSD. Zudem hat Mythos bewiesen, dass es mehrere Schwachstellen miteinander verknüpfen kann.

Wie schränkt Anthropic den Zugang zu Claude Mythos ein?

Anthropic teilte bei der Vorstellung von Claude Mythos mit, es schränke die Verfügbarkeit des Spitzen-KI-Modells bewusst ein, da dessen Fähigkeiten von Angreifern leicht missbraucht werden könnten.

Im Juni wurde die Technologie dann für weitere 150 Organisationen freigegeben. Alle Mythos-Partner müssen hierbei aber zustimmen, dass ihre Daten 30 Tage lang zu Sicherheitsüberwachungszwecken gespeichert werden.

Am 15. Juni verhängte die Trump-Regierung allerdings Exportbeschränkungen für Claude Fable 5 und Claude Mythos 5. Diese sollten ausländischen Staatsangehörigen sowohl innerhalb als auch außerhalb der USA den Zugang verwehren. Am 30. Juni wurden die Beschränkungen jedoch bereits wieder aufgehoben.

Was ist Claude Fable?

Für einen breiteren Einsatzbereich bietet Anthropic Claude Fable 5 an. Das Modell basiert auf derselben technischen Grundlage wie Mythos. Es verfügt jedoch über strenge Sicherheitsmechanismen, die den Betrieb in als „riskant“ eingestuften Bereichen der Cybersicherheit einschränken. Alle als problematisch deklarierten Anfragen werden stattdessen automatisch an das ältere und weniger leistungsfähige Large Language Model (LLM) Opus 4.8 weitergeleitet.

Wie nutzen Security-Partner von Anthropic den Zugriff auf Mythos?

Cisco, einer der Projekt-Glasswing-Partner, hat seine „Foundry Security Spec“ als Open-Source-Lösung veröffentlicht. Hierbei handelt es sich um ein modellunabhängiges Framework für Sicherheitstests, das es anderen Anbietern und Sicherheitsexperten in Unternehmen ermöglichen soll, ähnliche Arbeitsabläufe zu entwickeln, ohne bei Null anfangen zu müssen.

Vor kurzem betonten Vertreter von Cisco auf einer Online-Veranstaltung, dass Verteidiger KI nutzen können, um Sicherheitsprobleme wesentlich schneller und in größerem Umfang zu identifizieren, zu bestätigen und zu beheben. Ältere Modelle, die nach dem Prinzip „eine Schwachstelle finden und patchen“ funktionieren, seien nicht mehr zeitgemäß. Dies liege daran, dass Angreifer KI einsetzen, um den Weg von der Entdeckung einer Schwachstelle bis zu deren Ausnutzung zu beschleunigen. Cisco wiederum setzt KI intern bereits in der Defensive ein, um 1,8 Milliarden Zeilen Code im gesamten Produktportfolio zu scannen.

Gleichzeitig betonen die Experten, dass kleinere Unternehmen keinen Zugriff auf eingeschränkte KI-Modelle benötigen, um ihre Sicherheit zu verbessern. In solchen Betrieben lasse sich stattdessen mehr erreichen, indem grundlegende Sicherheitsmaßnahmen optimiert werden. Hierzu zählen laut Cisco unter anderem Authentifizierung, Netzwerk-Segmentierung, Zero Trust und die Behebung aktiv ausgenutzter Schwachstellen.

Bieten andere KI-Anbieter etwas Vergleichbares zu Claude Mythos an?

Mythos ist das prominenteste Beispiel für Frontier-KI-Modelle. Mit ihnen kann die Suche nach Zero-Day-Lücken in einem Tempo und Ausmaß automatisiert werden, die weit über die Fähigkeiten menschlicher Teams hinausgeht.

Allerdings arbeiten auch etliche andere Anbieter an hochleistungsfähigen, auf Sicherheit ausgerichteten „Frontier“-KI-Modellen. Zudem gibt es andere leistungsstarke Open-Source-Modelle, die sich problemlos für die Cybersicherheitsforschung nutzen lassen. Claude Mythos ist also bei weitem nicht die einzige verfügbare Option.

So werden beispielsweise die Modelle GPT-5.4-Cyber sowie GPT-5.5 von OpenAI genutzt, um Schwachstellen zu erkennen und zu analysieren. Auch in der Malware-Analyse und der Bedrohungsmodellierung kommen sie zum Einsatz. Zugang zu diesen Technologien können Sicherheitsanbieter, Unternehmen und Forscher über das Programm „Trusted Access for Cyber“ (TAC) von OpenAI erhalten.

Zusätzlich hat das chinesische Cybersicherheitsunternehmen 360 Security Technology mit Tulongfeng ein System entwickelt, das als Gegenstück zu Anthropics „Mythos“ beschrieben wird.

Privat lassen sich leistungsstarke offene Modelle – darunter DeepSeek V3.2 von DeepSeek und Llama 4 von Meta – auf GPU-Infrastrukturen betreiben und in der Cybersicherheitsforschung einsetzen. Fugu des japanischen Anbieters Sakana AI ist eine weitere Option in dieser Kategorie.

Was kritisieren Cybersicherheitsexperten an Claude Mythos?

Kritiker aus dem Cybersecurity-Bereich räumen ein, dass Claude Mythos zweifellos hochentwickelt sei. Die Marketing-Behauptung, wonach es zuverlässig produktive IT-Systeme lahmlegen könne, übersteige jedoch die tatsächlichen Fähigkeiten.

Darüber hinaus beklagen sich Sicherheitsexperten – etwa in Podcasts – über die übertrieben restriktiven Sicherheitsmechanismen von Claude Fable: Bereits Anfragen, einen sicherheitsrelevanten Blogbeitrag zusammenzufassen oder sogar das Wort „Exploit“ zu buchstabieren, würden auf das deutlich schwächere Modell Opus 4.8 zurückgestuft. Dadurch würden selbst alltägliche Aufgaben in der Informationssicherheit unnötig erschwert.

Andere Experten warnen davor, dass Frontier-KI-Modelle anfällig für False Positives seien. Eher grundsätzlich ist die Kritik, dass das schnellere Aufspüren von mehr Schwachstellen das eigentliche Problem nicht löst, nämlich zuverlässig Sicherheitslücken zu beheben oder nicht-technische Angriffsvektoren wie Social Engineering zu verhindern.

Wie sollten CISOs auf Mythos reagieren?

Die Nachrichtendienste der „Five Eyes“ (USA, Großbritannien, Kanada, Australien und Neuseeland)  warnen davor, dass hochmoderne KI-Modelle wie Claude Mythos „sowohl offensive als auch defensive Cyber-Fähigkeiten grundlegend verändern werden“ – und zwar in einem Zeitraum von Monaten statt Jahren.

„Während KI uns dabei helfen wird, die Cyberabwehr im Laufe der Zeit zu verbessern, erhöht sie zugleich Geschwindigkeit, Ausmaß und Raffinesse von Cyberbedrohungen“, heißt es in der Erklärung der Gruppe. Unternehmen sollen daher KI nutzen, um ihre Abwehrmechanismen im Rahmen umfassenderer Strategien zur Stärkung der Cybersicherheits-Resilienz zu verbessern.

Die meisten Unternehmen seien jedoch bei weitem noch nicht darauf vorbereitet, was dies für ihre Bedrohungsmodelle bedeutet, warnt ein Experte. „Wir verfügen heute über KI-Systeme, die realistische Angriffswege über Software, Anbieter und kritische Infrastrukturen hinweg schneller abbilden können, als menschliche Angreifer sie erfassen können“, erläutert Joe Hubback, Partner und CISO beim Beratungsunternehmen Elixirr sowie ehemaliger McKinsey-Partner. Dadurch, dass „Fähigkeiten der ‚Mythos-Klasse‘ vor der breiten kommerziellen Einführung stünden, handle es sich nicht mehr um „ein Nischenproblem der Forschung“, ergänzt er. Vielmehr sei es jetzt ein Faktor, den jedes Unternehmen in sein Bedrohungsmodell einbeziehen müsse, so der Experte.

Auch ein Bericht der Cloud Security Alliance warnt davor, dass KI die Zeitspanne zwischen der Entdeckung einer Schwachstelle und deren Ausnutzung drastisch verkürzt habe. Damit seien herkömmliche Sicherheitsmodelle, die auf „Patchen und Reagieren“ basieren, überholt. Unternehmen sollten sich vielmehr auf anhaltende Wellen von Schwachstellen einstellen, die durch „Project Glasswing“ und andere Quellen mittels KI aufgedeckt werden.

„Die bei Mythos beobachteten Fähigkeiten werden schon bald breiter verfügbar sein. Dadurch wird sich die Anzahl sowie Häufigkeit komplexer, neuartiger Angriffe, denen sich Unternehmen gegenübersehen, drastisch erhöhen“, heißt es in der Warnung. Sicherheitsverantwortliche müssten daher ihre Verteidigungsstrategien auf einen „Mythos-ready“-Ansatz umstellen, der auf kontinuierlichem Schwachstellenmanagement, schnellerer Priorisierung und verbesserter Reaktion auf Sicherheitsvorfälle basiert. (tf)

Dieser Artikel basiert auf einem Beitrag von CSO.

Arista Networks is looking to simplify data protection at the edge of enterprise networks with a new security package that combines branch office security with SD-WAN connectivity in a single platform.

The company announced AI-driven Edge Threat Management (ETM) for VeloCloud SD-WAN, a platform that links typically separate products and capabilities including Arista’s next-generation firewall, IP reputation, external blocklists, intrusion prevention, URL filtering, application classification, geo-IP filtering, network address translation, deep packet inspection, and zone-based segmentation.

ETM provides perimeter protection at the WAN edge and is a software upgrade option to VeloCloud SD-WAN, according to Arista. It can help simplify branch operations with a common operating system, a uniform enforcement engine, and common end-to-end security policies, the vendor stated. The new ETM solution also leverages Arista’s AVA (Autonomous Virtual Assist) for AI-driven policy intelligence.

“Multi-vendor branch complexity creates the ultimate blind spot, and your adversaries are actively hiding in it,” wrote Brendan Gibbs, Arista’s vice president, AI, routing, and switching platforms, in a blog post about the new platform.

Sprawling multi-vendor infrastructure creates operational headaches and increases security risks, according to Gibbs. “When you have four or five different point solutions from different vendors stacked on top of each other, configuring them becomes a manual, disjointed process. In fact, industry data shows that up to 95% of network changes are still performed manually, which inevitably leads to configuration mistakes, the single biggest driver of network downtime and security policy gaps,” he wrote. 

“When security policies are decoupled from local network routing, critical blind spots emerge. An attacker doesn’t need to break your cloud-delivered SASE firewall; they just need to target the unmonitored local traffic gaps between your Wi-Fi AP, your LAN switch, and your SD-WAN edge router,” Gibbs wrote.

ETM is integrated into VeloCloud Orchestrator as a dedicated enterprise application. “This enables security operators to configure policies that build on the same source of shared network configuration while maintaining a dedicated management console for security policy configuration, provisioning, and reporting,” Arista stated.

ETM security policies are managed in VeloCloud Orchestrator. “Admins can build and assign reusable policies consisting of predefined objects and templates. This design makes updating security policies possible by a few simple clicks, while the associated changes are propagated throughout the network within minutes,” Arista stated.

VeloCloud Orchestrator is the central management, configuration, and monitoring hub for VeloCloud SD-WAN and SASE networks.

In addition, VeloCloud edge routers collect threat intelligence data from a variety of sources to determine in real-time the trustworthiness and identity of hosts inside and outside the network. Through integration with Arista Network Detection and Response and other web-based dynamic lists, administrators can identify suspicious hosts and build policies to block potentially harmful activities, the vendor stated.

Integration with Arista’s AVA policy assistant is aimed at simplifying management of branch security policies. AVA continuously analyzes configuration states and translates complex, multi-site security rules into plain English, Gibbs explained. For example, NetOps administrators can use AI with Ask AVA to predict “how specific traffic will be handled before committing to a deployment, preventing manual configuration errors that leave branches exposed,” Gibbs wrote.

Arista also touted support for network-wide segmentation policies. “The flexible security policy configuration within the Edge Threat Management policy management extends the security coverage from the data center to the branch,” the vendor stated. “Security operations administrators can build access policies that are enforced across a distributed network. The centralized design enables admins to configure and deploy consistent zone based policies across the entire distributed network.”

ETM is a significant addition to the Arista VeloCloud portfolio. Arista bought the VeloCloud SD-WAN platform from Broadcom a year ago and has been promising new technologies that expand the platform. ETM also could further the vendor’s stated plans to expand beyond its data center networking roots and compete more broadly with enterprise networking vendors such as Cisco, Palo Alto Networks, and Fortinet.

In the SASE and SD-WAN world, vendors such as Cisco, Palo Alto, Fortinet, Cato Networks, and Versa Networks are among the most balanced suppliers, with both SD-WAN and SSE contributing meaningful revenue streams, according to a recently published report from Dell’Oro Group.

“We forecast that SASE will remain on a double-digit growth path in 2026, with SSE-first rollouts remaining the most common entry point, and SD-WAN supported by branch modernization, software attach, and branch security refresh,” Dell Oro stated.

“AI is changing the SASE discussion from access and inspection to governance, data protection, and control over agents and machine traffic,” Mauricio Sanchez, senior director, enterprise security and networking at Dell’Oro Group, stated in the report. “A 21 percent Y/Y quarter shows that SASE is not waiting for a future AI refresh cycle; it is already absorbing the early security and networking requirements created by AI adoption,” Sanchez added.

ETM for VeloCloud SD-WAN will be available in Q4 of 2026 and will be available for all current VeloCloud hardware and virtual edge platforms.

The head of the US government’s AI testing institute, Chris Fall, has resigned about three months after taking charge of the Center for AI Standards and Innovation (CAISI), the federal organization responsible for evaluating advanced artificial intelligence models for safety and security.

Current National Institute of Standards and Technology NIST Director Arvind Raman will serve as acting CAISI Director following Fall’s departure while continuing to oversee the Commerce Department office responsible for the institute, the Daily Signal reported, citing two people familiar with the matter.

A Commerce Department spokesperson who spoke to the publication did not disclose a reason for the resignation.

Fall assumed leadership of CAISI in April after the Trump administration reorganized the former US AI Safety Institute under NIST. The institute develops methodologies for evaluating frontier AI models and works with AI developers on voluntary technical assessments covering areas such as cybersecurity, model misuse, reliability and other risks associated with increasingly capable AI systems.

The leadership change comes as governments and AI companies continue developing technical approaches for evaluating frontier AI models while enterprises expand deployments of generative AI and agentic AI across business operations.

In recent months, the Commerce Department has taken a more active role in AI policy involving advanced models, placing greater attention on how the federal government evaluates technologies with potential national security implications.

Continuity matters more than personalities

CAISI works with AI developers such as Anthropic, Google’s DeepMind and OpenAI on voluntary evaluations of frontier AI models and develops methodologies for testing model capabilities and risks. The institute does not regulate AI developers or certify commercial AI systems.

For enterprises, those evaluations are one source of technical information alongside vendors’ own testing, third-party security assessments and internal AI governance programs.

Sanchit Vir Gogia, chief analyst at Greyhound Research, said enterprises should focus less on the individual leading the institute and more on whether its technical work continues with the same level of consistency and transparency.

“Leadership churn at CAISI weakens the signal long before it weakens the science,” Gogia said. “The testing has not stopped. Its authority simply does not travel as cleanly once the leadership does not.”

According to Gogia, the more important question for enterprises is not whether the institute’s evaluation work will continue but whether the processes supporting those evaluations remain stable.

“The instinct is to ask whether the pipeline is breaking,” he said. “The more useful question is where the pipeline now sits.”

Enterprises still carry the burden of AI governance

Gogia said organizations should continue treating government-led AI evaluations as one input into their governance processes rather than as evidence that a model is inherently safe for enterprise deployment.

“A government evaluation was always a signal, never a certificate,” he said. “A signal loses value the moment its issuer becomes unpredictable.”

He said enterprises should instead monitor whether CAISI maintains consistent evaluation methodologies, continues publishing technical findings and preserves continuity within its research teams under interim leadership.

“The name on the door is not the signal. The behaviour underneath it is,” Gogia said.

Gogia also cautioned against linking Fall’s resignation to recent Commerce Department actions involving AI policy or export controls, noting that there is no public evidence connecting the two.

“CAISI evaluates; it does not enforce export controls, because it holds no such power,” he said. “This is not a testing body reaching for enforcement. It is enforcement reaching past the testing body.”

With Raman assuming the role on an interim basis, the next significant milestone for enterprises will be the appointment of a permanent director, and whether the institute’s evaluation programs continue without disruption, the analyst said.

Gogia said the successor’s mandate may prove more important than the individual selected.

“A CAISI result is not a safe harbour,” he said. “It informs an obligation; it does not discharge one.” NIST did not immediately respond to a request for comment.

Site reliability engineers (SREs) have the tough assignment of resolving thorny performance and reliability issues. But their primary mission is to provide devops teams with operational insights and to suggest implementation improvements on business system performance, security, and overall robustness.

Google introduced its SRE playbook in 2003, but it took some time for the role’s definition, tools, and techniques to become mainstream. Startups were the first to adopt observability for cloud-native applications and create dedicated SRE positions. As tools matured and SRE responsibilities became more clearly defined, larger enterprises assigned SREs to work as a bridge between devops and IT ops teams to improve resilience across a wider range of applications, APIs, and data pipelines.

SRE is a career path for multidisciplinary engineers with strong investigative instincts, sharp data analytics skills, and the temperament to perform under pressure. It has become a critical responsibility as tech became mission-critical for enterprises, and it is a growing role in the genAI era as more businesses deploy AI agents.

But the critical need for resiliency and greater technological complexity brings new challenges for SREs. According to the 2026 State of Production Reliability and AI Adoption report, 44% of respondents experienced an outage linked to ignored or suppressed alerts in the past year, and 35% report their engineers occasionally ignore or dismiss alerts due to alert fatigue. More than 70% of alerts received are not actionable, according to 57% of organizations.

So, is AI making the SRE’s role easier and helping businesses run more reliable technology operations? On the other hand, AI is also driving complexity, as companies deploy genAI tools and AI agents across more business functions and seek to automate more decision-making across operations.

AIops and agentic ops aid SREs

Over the past decade, SRE responsibilities have become somewhat easier through improvements in monitoring platforms, observability practices, tools for centralizing operational data, and AI applied in IT operations (AIops). But during the heat of resolving an outage or performance issue, it’s not easy to correctly identify what system triggered the issue versus other downstream systems impacted by it.

According to the Komodore 2025 Enterprise Kubernetes Report, 79% of production incidents originate from recent system changes, including deployments and changes to compute environments. But the other 21% of incidents stem from issues outside of the business’s control, including network failures, third-party changes, and cloud provider failures.

“SREs using AI capabilities succeed or fail in the moment an incident unfolds, when engineers are deciding what to investigate next,” says Itiel Shwartz, CTO at Komodor. “If the system streamlines root cause detection, connects signals to recent changes, and explains its reasoning in a way engineers recognize, it earns trust. If it adds uncertainty or demands extra validation, it gets sidelined, regardless of how bespoke the model behind it may be. What’s less obvious is what it takes to make AI for SREs work in production, and how different that reality is from prototypes, demos, or early internal builds.”

AIops is not a new capability, especially in using machine learning to correlate logs, metrics, and traces across monitoring and alerting systems. IT service management and SREs have been using AIops to reduce the mean time to resolve incidents and to perform accurate root cause analysis (RCA) efficiently. Agentic ops is the next wave of genAI operational capabilities, including tools for monitoring AI agents, managing their access rights, and detecting AI model accuracy drift.

 “AI is useful during major incidents because it can pull together a lot of context into a few clear sentences, which is exactly what an SRE needs in the moment,” suggests Shani Shoham, chief revenue officer at OpenObserve. “The complexity of architecture and the different tooling make it easier for AI than for a human, but autonomous resolution is still a way off.”

AI’s impact on people and burnout

The business pressure to keep systems up, secure, and performing well is a 24/7 stressful responsibility. According to The SRE Report 2025 from Catchpoint, 36% of SREs often or always experience elevated stress during an incident, and 28% said the stress persists even after the incident is resolved. AI capabilities may prove to be a game-changer in helping SREs avoid burnout and reduce stress.

“AI can improve RCA by taking in a much larger incident context than any engineer can hold at 3am, reasoning across traces, logs, metrics, deploys, config changes, alerts, ownership, and recent production behavior,” says Noam Levy, founding engineer and field CTO at Groundcover. “Beyond attempting a full RCA, its immediate value is distilling the signals that actually matter, reconstructing a clear timeline of cause and effect, and helping engineers separate correlation from likely causality. Once a fix is deployed, agents can also verify remediation by comparing pre- and post-fix behavior, but this depends on broad access to rich, correlated production signals and a cost model that does not discourage adoption or experimentation.”

Not only are incidents resolved faster and with less stress, but AI can also free up SRE time to focus on proactive work and create a career path for junior developers into SRE roles. Quais Taraki, CTO at EDB Postgres AI, adds, “AI reduces toil by automating repetitive tasks while accelerating incident resolution through copilots that correlate signals across distributed systems, allowing SREs to focus more on resilience strategies like chaos engineering and failure analysis.”

AI can have long-lasting operational impacts, especially for organizations looking to deploy more mission-critical technology and AI capabilities. Two longer-term benefits of AI for SREs are reducing the number of bridge calls needed for incident response and the number of engineers required in “war rooms” to coordinate root cause analyses.

“When something goes wrong, AI that guides SREs can do the full analysis, get to the root cause, and perform the remediation,” says Spiros Xanthos, founder and CEO of Resolve AI. “AI also helps avoid many escalations, and when escalations are needed, it targets the right people from the network, infrastructure, and the application teams. AI for SREs centralizes operational intelligence, exposes tribal knowledge, and can guide more junior developers.” 

AI agent reliability

While AI capabilities have been a net positive in helping SREs improve system reliability, the growth of AI code generators, vibe coding, and spec-driven development is adding to their workloads. According to one study, 41% of all global code is now AI-generated, and Gartner predicts that 40% of new enterprise production software will be created using vibe coding techniques by 2028.

But coding velocity is creating new issues for SREs as AI pull requests have 1.4 times more critical issues and 1.7 times more major issues, according to CodeRabbit. “AI-assisted development has created an unprecedented velocity of code reaching production, expanding surface area, edge cases, and failure rates faster than traditional SRE practices can absorb,” says Vinod Jayaraman, cofounder and CTO at NeuBird AI. “The speed of shipping has far outpaced the speed of understanding what breaks in production. To close this loop, SREs need enterprise agents that can capture precise diagnostic context, including correlated traces, service dependencies, and anomaly timelines, and structure it as actionable input for the engineers and AI coding tools responsible for the fix.”

The growing number of AI agents deployed to production creates new challenges. AI agents are not just code; they have multiple failure points. They are built using language models, connect to proprietary sources for context, and integrate with Model Context Protocol servers to support more complex workflows. Changes are ongoing and not deployment events, so the SRE’s job of identifying the source of performance and accuracy drifts isn’t trivial. 

“Traditional SRE was built for systems that fail in reproducible ways, but agents fail differently and drift when a model provider pushes an update, and behavior shifts silently with no baseline for comparison,” says Mohammed Aboul-Magd, vice president of product at SandboxAQ. “Most organizations can’t even answer the basics: how many agents are running, what they have access to, and whether they’re still doing what they were built to do.”

“Every time a senior engineer leaves, they take years of learned failure patterns with them, and the next outage starts from square one,” adds Ronak Desai, cofounder and CEO at Ciroos. “Using AI for compounding operational memory changes that, and every incident your system resolves, the AI learns it.”

SREs should take a leadership role in emerging best practices, including defining their standards for AI agent non-functional acceptance criteria, observability practices, and release-readiness criteria. SREs should update their service-level objectives (SLOs) and define error budgets for AI agents in production.

Ryan Downing, vice president and CIO of enterprise business solutions at Principal Financial Group, says, “Standard SLOs and error budgets give teams the guardrails, and AI helps interpret the telemetry against those targets, reducing noise so engineers can get to the real issue faster and automate parts of remediation before customers are impacted.”

AI raises the SRE’s business impact

The more dramatic shift in site reliability engineering is an evolution of its business scope. IT leaders focus on uptime, performance, and issue resolution, as well as understanding their impacts. Business leaders will look to IT and SREs to identify, determine root cause, and remediate a broader class of issues, including rogue AI agents and the impacts of rapidly deploying new agentic capabilities. 

“AI agents are handing SREs categories of problems they’ve never had to solve before, specifically failures defined in business terms, not technical ones,” says Blake Sherwood, distinguished technologist for AI and platform strategy at Smarsh. “Traditional reliability engineering is built around latency, errors, and crashes, but agents now fail due to skipped compliance steps or outcomes that looked fine technically but were wrong contextually. Most SRE teams aren’t wired for that yet.”

The question is whether SREs with AI-augmented tools can keep up with the velocity, complexity, and business urgency of deploying new AI business capabilities.

As AI becomes embedded in customer experiences, internal workflows, and throughout the supply chain, security leaders are being asked to do more than manage risk. They are being asked to help the business make more informed decisions and move faster.

At the same time, AI has evolved faster than the programs built to govern it.

The result is a widening gap between the pace of transformation and the ability of security, risk, privacy, compliance, and third-party risk teams to understand where the business is exposed.

Move Fast, Don’t Break Things

AI introduces risks like prompt injection and jailbreaks, but the issues keeping CISOs awake at night are more familiar: over-permissioned accounts, poor logging, credentials left in old repositories, sensitive data scattered across systems, and weak access controls. 

AI gives those risks more speed, reach, and impact. 

When AI agents are connected to enterprise data, workflows, vendors, and applications, the blast radius of existing weak spots expands quickly. A low-severity incident now becomes harder to detect, more difficult to remediate, and more consequential for the business. 

This is why boards and executive teams are looking to security leaders for proactive guidance. They want to know whether the business can adopt AI at scale without creating risk that undermines long-term value. 

“Tell us, in real time, which initiatives are safe to accelerate, where we’re exposed, what could slow down our transformation, and what we need to act on right now.”

The CISO mandate has evolved from risk reporting to innovation enablement. 

When Everything is a Risk, Nothing is a Priority

In many organizations, risk context is spread across multiple teams. Security, procurement, privacy, IT, and third-party risk each have their own view.   

That fragmentation creates blind spots. 

Consider an AI agent that can retrieve customer records, access internal knowledge bases, and trigger downstream workflows. Security may know the agent exists, IT may know where it’s deployed, and procurement may know who purchased it. 

Without a holistic view, however, it becomes difficult to determine whether the agent has the right permissions, if it is operating within policy, or how it could expose the business.

But visibility is only half the battle. As AI systems, identities, vendors, and data change at a dizzying scale, organizations need to understand whether policy is actually being followed in real time.

A control that was effective six months ago may no longer suffice after a new AI integration, a vendor update, or a change in permissions. 

Today’s systems are too dynamic to be governed by the same operating model that worked for yesterday’s tech stack. 

From Risk Review to Risk Decisioning

CISOs are now being asked to help the business decide—quickly and defensibly—what can move forward, what needs guardrails, and what should stop. Meeting that mandate requires a different approach: 

• Treat AI risk as part of enterprise risk, not a separate discipline. AI is embedded in the same decisions organizations already make about data, vendors, identities, controls, and business processes.
• Start with the business process and context, not the model. Understand what processes depend on this system, the data it touches, and what happens if it fails. 
• Move from one-time approval to continuous assurance. What matters isn’t whether an AI project passed review six months ago, but whether it is operating within the organizations policies and risk appetite today.
• Measure decision velocity. Demonstrate how quickly the organization is able to determine what moves forward, what needs guardrails, and what must stop.

When risk is connected across the business, priorities become clear. Security leaders can understand not just what needs to be addressed, but what matters most, who owns it, and what the business impact could be. 

When there is a shared understanding of approved use, teams can move faster without relying on ad hoc reviews, static questionnaires, or blanket restrictions. The goal is to make technology and third-party risk visible, prioritized, and actionable at the speed the business now operates.

That shift helps security leaders say “yes” with confidence.

Safeguard Transformation and Scale Innovation

I know the pressure many CISOs are carrying right now. Your scope is getting larger while resources continue to shrink. 

Your leadership is asking you to protect every facet of the organization, support growing risk and compliance requirements, and now, to be a key voice in guiding business strategy.  

When you have clarity on what risks truly matter and have the tools to take action, your risk program can become a driver of responsible and scalable innovation. 

OneTrust helps build risk and compliance programs aligned with the complexity and the speed of your business. Learn more about our integrated risk solutions.

Organizations running recent versions of WordPress are being asked to patch a newly detailed pre-authentication remote code execution (RCE) vulnerability affecting the platform’s built-in REST Batch API.

The flaw, dubbed wp2shell, enables attackers to execute arbitrary code against a default WordPress installation without requiring plugins, authentication, or special configuration.

Adam Kues of Searchlight Cyber first reported the issue and published a public checker to assess risks, holding back technical details until a patch was available and admins had enough time to apply it.

In a technical analysis published by Hadrian, researchers reconstructed the root cause from WordPress’ security patch released on July 17. The vulnerability, they said, resides in the core REST API batch endpoint “batch/v1,” where an indexing mismatch during request validation can cause request objects and their associated permission checks to become misaligned.

As a result, a crafted batch request can be processed under the wrong authorization context, ultimately allowing remote code execution.

“An attacker who reaches the bug gains unauthenticated code execution on the web server,” the researchers said. “In practice, that means full control of the site and its content, access to the database and whatever credentials or personal data it holds, and a way into the surrounding hosting environment.”

The issue affects WordPress 6.9.0 through 6.9.4 and 7.0.0 through 7.0.1. According to Hadrian, versions 6.9.5 and 7.0.2 contain the fix, while 6.8.6 includes the same hardening changes even though earlier 6.8.x releases are not affected by this specific vulnerability.

Because the vulnerable component is part of WordPress Core and the REST Batch API is enabled by default, any affected site exposing the REST API is potentially reachable by attackers. The researchers said this endpoint remains accessible through both “/wp-json/batch/v1” and the alternate /“?rest_route=/batch/v1” path, even on deployments without rewrite rules.

Hadrian recommended upgrading immediately to WordPress 6.9.5 or 7.02, verifying that updates completed successfully, and, where patching cannot be performed immediately, blocking unauthorized access to both REST Batch API endpoints at the web server and through WAF.

Organizations were also advised to inventory all WordPress deployments, including staging and forgotten campaign sites, and review whether unauthorized access to the REST API is necessary for their environment.

See also:

• What Anthropic Glasswing reveals about the future of vulnerability discovery
• Anthropic’s Mythos signals a structural cybersecurity shift
• Beware the ‘son of Mythos,’ security experts warn
• Mythos is a signal, not a siren: What frontier AI should change for CISOs

In one ransomware response I observed, the master operational dashboard remained green while the underlying environment told a very different story. It was a classic example of what we in the IT audit profession call the “watermelon effect”—green on the outside, red on the inside.

Beneath that dashboard sat an unmapped web of legacy technical debt, undocumented service accounts and shadow cloud instances. For years, presenting a green dashboard to the audit committee could give technology leaders a false sense of comfort. If a catastrophic breach occurred, it was generally treated as an unpredictable operational tragedy, managed via cyber insurance, a carefully calibrated public relations pivot and perhaps a quiet executive transition.

Today, that corporate shield is thinner than many technology leaders assume. For technology leaders in regulated or public-company environments, executive exposure is no longer only a theoretical debate. The regulatory environment has made plausible deniability much harder to sustain.

The erosion of the corporate shield

With the application of the European Union’s Digital Operational Resilience Act (DORA) for financial entities, alongside the broader NIS2 Directive for essential and important entities, cybersecurity governance has become harder to separate from board-level oversight. DORA places ultimate responsibility for ICT risk management on the management body of financial entities, while NIS2 requires management bodies to approve and oversee cybersecurity risk-management measures. In the United States, the U.S. Securities and Exchange Commission’s cybersecurity disclosure rules require public companies to disclose material cyber incidents and describe their cyber risk management, strategy and governance in annual filings. The new burden is not simply to operate controls; it is to show, after the fact, that leadership decisions matched the risk evidence available at the time.

The serious risk to a modern CIO is not simply the occurrence of a sophisticated security incident. The true danger is the inability to reconcile what leadership presented externally to investors, regulators and the board with what the internal evidence showed inside the environment.

When a serious crisis breaks, you may find yourself surrounded by corporate defense counsel, regulatory investigators and outside forensic lawyers all asking variations of the same uncomfortable questions: What did you know, when did you discover it and what specific actions did you take next?

When those questions are asked, a slide deck asserting that your security posture is “aligned with industry best practices” will not be enough. A post-incident review may recognize that sophisticated attacks occur. What creates greater exposure is evidence that known risks were ignored, understated or left outside structured governance. To survive that level of post-incident review, one of your strongest assets is a disciplined, independent evidence trail showing that risks were identified, challenged, escalated and acted on before the first indicator of compromise appeared.

Why point-in-time comfort letters fail regulatory scrutiny

The reality we face is that legacy compliance evidence often falls short under regulatory scrutiny. For years, the annual SOC 2 Type II report or a standardized ISO 27001 certification was brandished by technology teams as the definitive proof of a functional control environment. I have sat in dozens of scoping meetings where an engineering director pointed to a freshly minted compliance report as if it were a complete defense against scrutiny.

But a compliance report is a historical artifact—a retrospective evaluation of how specific controls operated during a defined window of time months in the past. It tells an investigator that on a random afternoon in Q2, your production change-management approvals conformed to a baseline policy. It says absolutely nothing about the configuration drift, unauthorized API keys or emergency patch bypasses that developers introduced the following weekend to hit a product release deadline.

Modern regulators, boards and investors are no longer satisfied by historical comfort letters alone. Under contemporary frameworks, especially regimes focused on operational resilience, static compliance evidence is no longer enough. The expectation of due care has shifted from a passive state of compliance to an active state of continuous challenge. Increasingly, post-incident reviews look for evidence that leadership identified system vulnerabilities, formally escalated material deficiencies, evaluated systemic risk to the business and tracked remediation progress with measurable rigor.

When an architecture fails, post-incident reviews often focus quickly on ownership, escalation and whether known risks were acted upon. If your defensive documentation consists entirely of static policy documents and green dashboards, you leave an evidentiary vacuum that can invite difficult questions about executive oversight. Post-incident reviews rarely turn on perfection. They turn on whether the organization can show a traceable chain of governance.

5 non-negotiable artifacts for your executive evidence engine

This reality requires a complete reframing of your relationship with your IT audit department. Historically, this dynamic has been defined by friction. Technology leaders frequently view my peers and me as compliance traffic cops—bureaucrats who interrupt core engineering sprints to demand evidence samples, user access reviews and system configurations.

It is time to view IT audit through a pragmatic lens: we are your independent evidence engine. We are one of the few corporate functions tasked with independently challenging your control environment, documenting where exceptions were escalated and showing how management responded. When an auditor identifies a control gap and partners with you to draft a management action plan, they are not creating a bureaucratic roadblock. They are helping you construct an evidence trail that can show risk was identified, escalated and acted upon.

To transform your IT audit function into an effective executive shield, you must shift focus away from superficial check-the-box exercises and collaborate on specific artifacts. The most effective exercise you can run with your audit leadership is to flip the timeline completely and ask: if this program were reviewed six months from now, which evidence would show we governed the risk before it failed?

1. Board-facing risk registers with escalation history: A risk register that sits unreviewed on an intranet page for 12 months is not a management tool; to an investigator, it can look like evidence that known risks were not actively governed. Your material technology, cybersecurity and dependency risks must be centrally logged. More importantly, this artifact must contain a clear, chronological escalation history showing exactly when the risk was presented to leadership committees and the board, along with related minutes, decisions or follow-up actions.
2. Granular risk acceptance records: You cannot remediate every vulnerability instantly. Business continuity, legacy software limitations and budgetary boundaries require you to accept certain operational exposures. When this occurs, ensure your risk acceptance records are airtight. A defensible record must document the specific technical variance, the precise financial or operational rationale for the delay, a definitive expiration date, explicit executive sign-off and the active compensating controls deployed to reduce the blast radius in the interim.
3. Tabletop and operational simulation records: Independent frameworks such as ISACA’s Digital Trust Ecosystem Framework can help structure this evidence, but boards and regulators will still look for proof that the testing actually happened. Your audit trail should contain comprehensive records of cyber incident, disaster recovery and third-party dependency simulations. These records must detail the scenario tested, the executive participants, the control failures identified during the drill and a formalized tracking schedule showing when those gaps were closed.
4. AI governance inventories and data-flow mappings: The rapid deployment of generative AI tools across enterprise operations has created a massive blind spot for technology executives. In one audit, we found developers using an unapproved public large language model to accelerate debugging with sensitive internal code. To protect yourself, work with your audit team to build an active enterprise AI inventory that maps data lineage, identifies model business owners, documents risk classification approvals and demonstrates active technical monitoring for unauthorized data exfiltration.
5. Synchronized disclosure-control handoffs: When a material security incident or system outage occurs, the clock begins ticking for regulatory reporting. Your incident response playbook must be technically linked to your corporate disclosure controls. The audit trail should show that a documented, synchronized handoff occurred between your technical response leaders, general counsel, chief financial officer and corporate communications team. This evidence helps show that your external statements match internal technical realities.

In the modern corporate ecosystem, technology leadership is no longer just an engineering challenge; it is an exercise in rigorous, evidence-based governance. The regulatory landscape has changed, and the expectation of continuous traceability cannot be avoided.

Open and direct collaboration with your IT audit team will not prevent a zero-day exploit, an unexpected cloud outage or a critical third-party vendor failure. That is not the purpose of enterprise risk management.

The true value is far more practical: when a serious incident puts your program under review, you will not be forced to defend your reputation with a feeling, an unverified assumption or a misleadingly green dashboard. Instead, you will have an independent record showing that risk was actively seen, appropriately challenged, properly escalated and responsibly managed. In today’s regulatory environment, that disciplined trail of evidence may be the difference between a failure that can be explained and one that begins to look negligent.

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