Dubbed the “Genius Hacker,” Park Chan-am began his white hat hacker journey at the precocious age of 11, winning awards at domestic and international hacking competitions since his teenage years.

He has since served as a cybersecurity advisor for various Korean government agencies, including the National Police Agency, and has played a key role in the country’s defense against Democratic People’s Republic of Korea (DPRK)-affiliated cyberattacks.

At a seminar held this month as part of the 15th Information Security Day event hosted and organized by government agencies including the Ministry of Science and ICT and the Korea Internet and Security Agency (KISA), Park, now CEO of security firm Steelion, explained the changes in the security environment in the AI ​​era and the priority response tasks for information security organizations under the theme of “Major AI Threats and Security Priorities.”

“While AI is a new technology, the core of security ultimately lies in access control, supply chain management, and human verification,” he said.

Like many security experts, Park sees AI fundamentally changing the speed of cyberattacks. In the past, infiltrating a corporate network required significant time analyzing a range of software systems to find exploitable vulnerabilities — a task that the use of AI has significantly accelerated.

“In the past, it took at least four weeks to find vulnerabilities, but now it takes less than a day,” he said. “In the era of AI, all software installed within a company becomes a much more critical target for attacks.”

As a result, securing internal software and the software supply chain are paramount — and require a different perspective on accountability, Park noted.

“Clients often ask, ‘Isn’t this just a product made by the vendor?’” he said. “From the moment it is installed in the company system, that software is no longer a vendor issue but part of the corporate system.”

“We are now in an era where third-party issues can no longer be attributed solely to vendor responsibility,” he stressed.

MCP under threat

Park also sees authorization management as a key challenge security teams will face in the agentic era. For example, companies have been increasingly utilizing Model Context Protocol (MCP)-based AI agents to read emails, analyze documents, and connect internal systems with various business tasks. But as the workload handled by AI agents increases, every step an AI agent takes, reading external documents and interacting with internal systems, can become a potential attack vector.

Prompt contamination through malicious documents and the leakage of internal information via agents with excessive privileges are quite realistic scenarios, Park said. In particular, he pointed out that issues that previously ended as minor problems, such as residual privileges left by former employees or outsourced personnel, could escalate into major incidents as AI automatically links these elements together.

“When introducing AI, permissions must be designed before functions,” he said. “The entire MCP process must be approached as a single attack path.”

The ever-widening blast radius of AI testing

Local AI testing environments are becoming a dangerous security blind spot that information security leaders often overlook. Rapid experimentation with open-source AI, such as LLaMA-based models, on personal or work PCs often results in servers or ports being left open, and if vulnerabilities are discovered, intrusion pathways immediately open up.

“When the [Ollama] remote code execution vulnerability was discovered in 2024, there were over 1,000 servers exposed to the internet, but recently in 2026, it has been confirmed that over 300,000 servers from the same targets are exposed,” said Park, adding that “the act of testing AI itself can become a new security risk.”

Vulnerability management on notice

Security operations must also change, Park stressed, noting that the number of alerts that security personnel must handle has increased tenfold, and in some cases up to a hundredfold, making it virtually impossible to respond to all vulnerabilities using the same standards.

As a solution, Park sees the Common Vulnerability Scoring System (CVSS) being insufficient for determining priorities. Instead, he suggested that vulnerability response priorities be determined by utilizing the Exploit Prediction Scoring System (EPSS), which predicts the actual likelihood of exploitation, along with the US government’s Known Exploited Vulnerabilities (KEV) list.

For example, if a vulnerability’s CVSS score is 7.5, it is not classified as critical, so it is likely to be pushed down the priority list. But the response priority changes completely if the same vulnerability is listed on the KEV list, has been exploited in actual ransomware attacks, and the probability of an attack based on EPSS has skyrocketed from 1% to 90% within two months. “You must consider these factors together to identify the vulnerabilities that actually need to be patched first,” he stressed.

“Amidst the vast noise known as the AI s​lop, the criteria for deciding what to patch first is now becoming a core competency for security personnel,” he added.

Park also presented new defense techniques applicable to the AI ​​era, such as methods to detect automated attacks by analyzing behavioral differences between humans and AI attackers, and proof of work (PoW) challenges that intentionally impose computational load on AI attackers to slow down their attacks. A prime example is filtering out abnormal access by analyzing mouse movements, keyboard input, and scrolling patterns.

“It is a more realistic strategy to reduce the burden on security teams by filtering out at least some attacks, rather than trying to block them 100%,” he said.

Even in the age of AI, technology alone cannot ensure complete security, he noted. “While AI can scan for threats broadly and quickly, verifying and confirming them ultimately falls to humans,” he said. “Humans are still important.”

Microsoft has issued a warning about a recent surge in ACR Stealer activity that uses ClickFix-style social engineering to steal credentials, browser data, and sensitive business documents.

In a new report, Microsoft researchers detailed two separate campaigns observed between late April and mid-June 2026 that use different execution techniques for the same theft.

The campaign was seen tricking users into executing malicious commands to resolve a fake issue. Once the malware is executed, it extracts browser-stored credentials, session tokens, and documents, which can potentially allow attackers to access cloud services, impersonate users, and conduct follow-on intrusions across enterprise environments.

Nick Tausek, lead security automation architect at Swimlane, thinks attackers could be using two distinct chains to trick defense tuned on individual indicators.  “By changing the delivery and execution patterns, attackers can evade defenses tuned to one known chain and make related incidents appear disconnected,“ he said. “Security teams may split the activity across separate investigations, delaying recognition of the shared malware and objective.”

ACR Stealer is an information-stealing malware family Microsoft believes is offered through a malware-as-a-service (MaaS) model, with possible links to the Amatera Stealer.

WebDAV and MSHTA-based chains

Although both campaigns begin with ClickFix lures, Microsoft’s analysis shows they diverge after initial execution. One attack chain uses WebDAV-hosted DLLs, PowerShell, Python loaders, scheduled-task persistence, and even blockchain-based infrastructure called the “EtherHiding” technique, to complicate detection and command and control (C2) discovery.

The second chain uses MSHTA, heavily obfuscated PowerShell, stenography, and predominantly fileless, in-memory execution to minimize forensic trails.

“The most troubling part of ACR Stealer is the flexibility surrounding the theft. One chain invests in persistence and layered infrastructure, while the other favors memory execution and fewer forensic traces,” Tausek said. “Those approaches look different to defenders, yet both turn a simple ClickFix lure into stolen credentials, tokens, and business documents.”

Microsoft researchers said protections against these campaigns have now been added to Defender. “Microsoft Defender for Endpoint can help surface both campaigns through behavioral coverage for living-off-the-land execution, suspicious WebDAV and MSHTA activity, obfuscated PowerShell, scheduled-task persistence, in-memory payload execution, and browser credential theft,” they said.

Mitigations include ClickFix-targeted detections

The report highlighted that neither of the campaigns exploits any software vulnerability, depending solely on ClickFix-based social engineering.

Microsoft warned its Defender customers that ClickFix attacks are on the rise and shared XDR queries to identify suspicious commands executed through ClickFix-based activity observed while delivering the ACR Stealer.

Microsoft also recommended, as general defense, monitoring for suspicious PowerShell activity, MSHTA execution, WebDAV connections, and attempts to access browser credential stores, while also enabling Microsoft Defender SmartScreen and Attack Surface Reduction (ASR) rules to block common malware delivery techniques.

“The campaigns do not need to directly aid one another to be effective. Together, they create ambiguity and stretch limited SOC resources,” Tausek explained. Security teams need enough visibility to correlate endpoint, identity, and network activity as one evolving intrusion, he added.

Microsoft also shared a list of C2 addresses and payload hosting domains for defenders to add to their detection.

Google DeepMind CEO Demis Hassabis is pushing for the US AI industry to self-regulate, with the support of government, as a starting point for an international creating shared international standards. In a blog post, he called for a focus on artificial general intelligence (AGI) and national security. 

But it is precisely that focus on national security that may make the results of such an effort, assuming it happens, less than palatable outside of the US.

“The rapid progress we’re seeing in AI requires a new approach to testing frontier AI model capabilities that is dynamic, adaptable, and rigorous,” Hassabis wrote. “The US is well positioned, given its economic and technical standing, to take the first step in developing such a framework. It could establish a new Standards Body modelled on a federally overseen public-private partnership or self-regulatory organization, much like the Financial Industry Regulatory Authority (FINRA), with a board that includes independent leading technical experts and open-source representatives.”

He noted, however, that the funding would need to be substantial, and would most likely come from industry, to allow the new body to attract world-class technical talent and obtain the necessary compute resources for large-scale testing.

Hassabis proposed that the organization “be responsible for developing assessment protocols and working with appropriate federal agencies and the US National Labs to conduct testing in areas relevant to national security,” and that AI vendor participants be encouraged to adopt best practices such as publishing model cards with technical details, maintaining strong internal cybersecurity, vetting key personnel, and providing sufficient resourcing for safety and security research.

This is not the first time Hassabis has expressed worries about AGI. 

DeepMind was involved in an earlier US government initiative evaluating AI safety, alongside Microsoft and xAI (now SpaceXAI) working with the Center for AI Standards and Innovation (CAISI), a division of the US Department of Commerce. It allowed CAISI to conduct pre-deployment evaluations and targeted research to “better assess frontier AI capabilities and advance the state of AI security.”  

The rest of the world may have concerns

Analysts and consultants were mixed about the move, with most expressing concerns about whether an industry-focused group would prioritize the public’s best interests.

“Self-regulation is not viable because it implies everyone is able to regulate themselves and will do so in line with the best interests of the public. Most tech vendors don’t have the capacity to self-regulate. They would just prefer a set of rules within which they can operate,” said Gartner VP analyst Nader Henein. “For-profit organizations are required to do what is best for their shareholders, and external regulation ensures that those organizations are never in a conflict of interest where they have to choose between what is good for their shareholders and what is good for the public.”

And, said Sanchit Vir Gogia, chief analyst at Greyhound Research, given the international nature of AI models, an effort coordinated by the US government might alienate other countries. 

“National security is the proposal’s accelerator in Washington and its poison pill abroad: the framing that opens the only gate available at home invites foreign capitals to read the institution as an instrument of American strategy,” he pointed out.

“The map is already plural,” he said. “Brussels switches on enforcement powers over general-purpose models [starting in August 2026], London runs the AI Security Institute, and Beijing licenses on its own terms. California and New York have legislated for frontier models at home. The durable route is shared technical evidence with sovereign enforcement, sealed through mutual recognition rather than deference, with India and the other major non-Western markets holding authorship rather than seats.”

Gogia added that the rules enacted by even such a group may not address all of the key concerns of enterprise IT. A US government effort along the lines that Hassabis is proposing would result in testing that “sits close to intelligence and industrial policy, and those functions will not stay neatly separated. A model can pass every catastrophic-risk test and still fail the enterprise on privacy, reliability, and liability,” he noted.

Walmart’s former director of cybersecurity Steven Eric Fisher, who is now an independent cybersecurity consultant, said he found the proposal “well-intentioned, but it addresses a highly polarized topic at a time when commercial interests carry unprecedented political influence, which is not always applied benevolently.”

He added, “an exclusive US standard that is not globally respected or enforceable would likely fail to achieve its core purpose and would place US companies at a competitive disadvantage.”

Aman Mahapatra, chief strategy officer for Tribeca Softtech, a New York City-based technology consulting firm, said that a deep dive into how FINRA operates today is illustrative of what IT leaders can expect from this effort, assuming the industry adopts that model.

“When the CEOs of the five companies that would be regulated are also the primary drafters of the standards, the standards will reflect those companies’ interests. FINRA has an independent board, but the operational reality is that member firm perspectives dominate the working groups that write the actual rules,” he said. “There is no reason to expect an AI equivalent to work differently, and every reason to expect it to work worse, because AI standardization is happening faster than any industry has ever attempted to standardize itself, and speed is the enemy of independent oversight.”

Carmi Levy, an independent technology analyst, was even more emphatically opposed to the Hassabis proposal.

“Asking Big Tech companies to self-police is analogous to allowing foxes to guard the henhouse. It hasn’t worked to date, and it won’t work going forward. Expecting these organizations to somehow change their ways at this point in time represents the height of naïve thinking,” Levy said. “The framework proposed by Demis Hassabis is a self-serving roadmap for an industry bent on racing to the AI horizon regardless of the harms caused along the way. It is impossible to quantify the dangers to broader society should frameworks allowing self-regulation become the norm.”

Some love the proposal

An almost completely opposite stance came from Yuri Goryunov, CIO of consulting firm Acceligence, who applauded the proposed move.

“This is one of the rare setups where industry self-regulation has a real shot, and enterprise IT should be enthusiastically rooting for it,” he said. “It fails when harms are externalized, such as in social media content moderation. Or when the overseer outsources judgment to the overseen, such as the FAA’s delegation to Boeing before the 737 MAX. It works when everyone in the industry shares the catastrophic downside.”

He suggested, however, that the best precedent here isn’t FINRA, it’s INPO, the Institute of Nuclear Power Operations, which the nuclear industry created within months of the 1979 Three Mile Island partial reactor meltdown “on the logic that an accident anywhere is an accident everywhere. INPO peer-reviews every US plant, its evaluations move insurance premiums, and it sits on top of the NRC’s statutory floor. That is a public-private stack very close to what Hassabis is describing. Frontier AI has the same structure: one lab’s catastrophic failure brings regulation down on all of them.”

For enterprise CIOs and other IT executives, Goryunov said, that model has the potential for being a big win.

“Today, every enterprise duplicates the same AI diligence of red-teaming, eval suites, governance committees and each does so with less information than any certifying body would have,” Goryunov said. “A credible standards regime does for AI what UL did for electrical equipment and SOC2 did for cloud: it converts an unknowable risk into a procurable product and gives boards a defensible standard of care. That’s not red tape. That’s peace of mind with an audit trail.”

However, Mahapatra said, “the countervailing view is that the alternative to industry-led standards is probably not thoughtful legislation. It is probably no standards, or state-by-state fragmentation, or the current pattern of ex-post enforcement actions where regulators surface concerns years after harm has already occurred.”

Thus, he noted, “Hassabis is making the reasonable argument that imperfect fast standards are better than perfect slow ones, and there is genuine merit to that view for topics like agent identity, evaluation methodology, and interoperability, which are exactly the areas OpenClaw is also targeting.”

Google DeepMind CEO Demis Hassabis is pushing for the US AI industry to self-regulate, with the support of government, as a starting point for an international creating shared international standards. In a blog post, he called for a focus on artificial general intelligence (AGI) and national security. 

But it is precisely that focus on national security that may make the results of such an effort, assuming it happens, less than palatable outside of the US.

“The rapid progress we’re seeing in AI requires a new approach to testing frontier AI model capabilities that is dynamic, adaptable, and rigorous,” Hassabis wrote. “The US is well positioned, given its economic and technical standing, to take the first step in developing such a framework. It could establish a new Standards Body modelled on a federally overseen public-private partnership or self-regulatory organization, much like the Financial Industry Regulatory Authority (FINRA), with a board that includes independent leading technical experts and open-source representatives.”

He noted, however, that the funding would need to be substantial, and would most likely come from industry, to allow the new body to attract world-class technical talent and obtain the necessary compute resources for large-scale testing.

Hassabis proposed that the organization “be responsible for developing assessment protocols and working with appropriate federal agencies and the US National Labs to conduct testing in areas relevant to national security,” and that AI vendor participants be encouraged to adopt best practices such as publishing model cards with technical details, maintaining strong internal cybersecurity, vetting key personnel, and providing sufficient resourcing for safety and security research.

This is not the first time Hassabis has expressed worries about AGI. 

DeepMind was involved in an earlier US government initiative evaluating AI safety, alongside Microsoft and xAI (now SpaceXAI) working with the Center for AI Standards and Innovation (CAISI), a division of the US Department of Commerce. It allowed CAISI to conduct pre-deployment evaluations and targeted research to “better assess frontier AI capabilities and advance the state of AI security.”  

The rest of the world may have concerns

Analysts and consultants were mixed about the move, with most expressing concerns about whether an industry-focused group would prioritize the public’s best interests.

“Self-regulation is not viable because it implies everyone is able to regulate themselves and will do so in line with the best interests of the public. Most tech vendors don’t have the capacity to self-regulate. They would just prefer a set of rules within which they can operate,” said Gartner VP analyst Nader Henein. “For-profit organizations are required to do what is best for their shareholders, and external regulation ensures that those organizations are never in a conflict of interest where they have to choose between what is good for their shareholders and what is good for the public.”

And, said Sanchit Vir Gogia, chief analyst at Greyhound Research, given the international nature of AI models, an effort coordinated by the US government might alienate other countries. 

“National security is the proposal’s accelerator in Washington and its poison pill abroad: the framing that opens the only gate available at home invites foreign capitals to read the institution as an instrument of American strategy,” he pointed out.

“The map is already plural,” he said. “Brussels switches on enforcement powers over general-purpose models [starting in August 2026], London runs the AI Security Institute, and Beijing licenses on its own terms. California and New York have legislated for frontier models at home. The durable route is shared technical evidence with sovereign enforcement, sealed through mutual recognition rather than deference, with India and the other major non-Western markets holding authorship rather than seats.”

Gogia added that the rules enacted by even such a group may not address all of the key concerns of enterprise IT. A US government effort along the lines that Hassabis is proposing would result in testing that “sits close to intelligence and industrial policy, and those functions will not stay neatly separated. A model can pass every catastrophic-risk test and still fail the enterprise on privacy, reliability, and liability,” he noted.

Walmart’s former director of cybersecurity Steven Eric Fisher, who is now an independent cybersecurity consultant, said he found the proposal “well-intentioned, but it addresses a highly polarized topic at a time when commercial interests carry unprecedented political influence, which is not always applied benevolently.”

He added, “an exclusive US standard that is not globally respected or enforceable would likely fail to achieve its core purpose and would place US companies at a competitive disadvantage.”

Aman Mahapatra, chief strategy officer for Tribeca Softtech, a New York City-based technology consulting firm, said that a deep dive into how FINRA operates today is illustrative of what IT leaders can expect from this effort, assuming the industry adopts that model.

“When the CEOs of the five companies that would be regulated are also the primary drafters of the standards, the standards will reflect those companies’ interests. FINRA has an independent board, but the operational reality is that member firm perspectives dominate the working groups that write the actual rules,” he said. “There is no reason to expect an AI equivalent to work differently, and every reason to expect it to work worse, because AI standardization is happening faster than any industry has ever attempted to standardize itself, and speed is the enemy of independent oversight.”

Carmi Levy, an independent technology analyst, was even more emphatically opposed to the Hassabis proposal.

“Asking Big Tech companies to self-police is analogous to allowing foxes to guard the henhouse. It hasn’t worked to date, and it won’t work going forward. Expecting these organizations to somehow change their ways at this point in time represents the height of naïve thinking,” Levy said. “The framework proposed by Demis Hassabis is a self-serving roadmap for an industry bent on racing to the AI horizon regardless of the harms caused along the way. It is impossible to quantify the dangers to broader society should frameworks allowing self-regulation become the norm.”

Some love the proposal

An almost completely opposite stance came from Yuri Goryunov, CIO of consulting firm Acceligence, who applauded the proposed move.

“This is one of the rare setups where industry self-regulation has a real shot, and enterprise IT should be enthusiastically rooting for it,” he said. “It fails when harms are externalized, such as in social media content moderation. Or when the overseer outsources judgment to the overseen, such as the FAA’s delegation to Boeing before the 737 MAX. It works when everyone in the industry shares the catastrophic downside.”

He suggested, however, that the best precedent here isn’t FINRA, it’s INPO, the Institute of Nuclear Power Operations, which the nuclear industry created within months of the 1979 Three Mile Island partial reactor meltdown “on the logic that an accident anywhere is an accident everywhere. INPO peer-reviews every US plant, its evaluations move insurance premiums, and it sits on top of the NRC’s statutory floor. That is a public-private stack very close to what Hassabis is describing. Frontier AI has the same structure: one lab’s catastrophic failure brings regulation down on all of them.”

For enterprise CIOs and other IT executives, Goryunov said, that model has the potential for being a big win.

“Today, every enterprise duplicates the same AI diligence of red-teaming, eval suites, governance committees and each does so with less information than any certifying body would have,” Goryunov said. “A credible standards regime does for AI what UL did for electrical equipment and SOC2 did for cloud: it converts an unknowable risk into a procurable product and gives boards a defensible standard of care. That’s not red tape. That’s peace of mind with an audit trail.”

However, Mahapatra said, “the countervailing view is that the alternative to industry-led standards is probably not thoughtful legislation. It is probably no standards, or state-by-state fragmentation, or the current pattern of ex-post enforcement actions where regulators surface concerns years after harm has already occurred.”

Thus, he noted, “Hassabis is making the reasonable argument that imperfect fast standards are better than perfect slow ones, and there is genuine merit to that view for topics like agent identity, evaluation methodology, and interoperability, which are exactly the areas OpenClaw is also targeting.”

This article first appeared on CIO.

Google DeepMind CEO Demis Hassabis on Tuesday reiterated his push for an AI industry self-regulation effort, led by the US government, that is particularly focused on artificial general intelligence (AGI) and national security. 

But it is precisely that focus on national security that may make the results of such an effort, assuming it happens, less than palatable outside of the US.

“The rapid progress we’re seeing in AI requires a new approach to testing frontier AI model capabilities that is dynamic, adaptable, and rigorous,” Hassabis wrote. “The US is well positioned, given its economic and technical standing, to take the first step in developing such a framework. It could establish a new Standards Body modelled on a federally overseen public-private partnership or self-regulatory organization, much like the Financial Industry Regulatory Authority (FINRA), with a board that includes independent leading technical experts and open-source representatives.”

He noted, however, that the funding would need to be substantial, and would most likely come from industry, to allow the new body to attract world-class technical talent and obtain the necessary compute resources for large-scale testing.

Hassabis said he would propose that the organization “be responsible for developing assessment protocols and working with appropriate federal agencies and the US National Labs to conduct testing in areas relevant to national security,” and that AI vendor participants would be encouraged to adopt best practices, such as publishing model cards with technical details, maintaining strong internal cybersecurity, vetting key personnel, and providing sufficient resourcing for safety and security research.

This is not the first time Hassabis has expressed worries about AGI. He has already worked on a US government initiative evaluating AI safety, which involved DeepMind, Microsoft and xAI (now SpaceXAI) working with the Center for AI Standards and Innovation (CAISI), a division of the US Department of Commerce. It allowed CAISI to conduct pre-deployment evaluations and targeted research to “better assess frontier AI capabilities and advance the state of AI security.”  

The rest of the world may have concerns

Analysts and consultants were mixed about the move, with most expressing concerns about whether an industry-focused group would prioritize the public’s best interests.

“Self-regulation is not viable because it implies everyone is able to regulate themselves and will do so in line with the best interests of the public. Most tech vendors don’t have the capacity to self-regulate. They would just prefer a set of rules within which they can operate,” said Gartner VP analyst Nader Henein. “For-profit organizations are required to do what is best for their shareholders, and external regulation ensures that those organizations are never in a conflict of interest where they have to choose between what is good for their shareholders and what is good for the public.”

And, said Sanchit Vir Gogia, chief analyst at Greyhound Research, given the international nature of AI models, an effort coordinated by the US government might alienate other countries. 

“National security is the proposal’s accelerator in Washington and its poison pill abroad: the framing that opens the only gate available at home invites foreign capitals to read the institution as an instrument of American strategy,” he pointed out.

“The map is already plural,” he said. “Brussels switches on enforcement powers over general-purpose models [starting in August 2026], London runs the AI Security Institute, and Beijing licenses on its own terms. California and New York have legislated for frontier models at home. The durable route is shared technical evidence with sovereign enforcement, sealed through mutual recognition rather than deference, with India and the other major non-Western markets holding authorship rather than seats.”

Gogia added that the rules enacted by even such a group may not address all of the key concerns of enterprise IT. A US government effort along the lines that Hassabis is proposing would result in testing that “sits close to intelligence and industrial policy, and those functions will not stay neatly separated. A model can pass every catastrophic-risk test and still fail the enterprise on privacy, reliability, and liability,” he noted.

Walmart’s former director of cybersecurity Steven Eric Fisher, who is now an independent cybersecurity consultant, said he found the proposal “well-intentioned, but it addresses a highly polarized topic at a time when commercial interests carry unprecedented political influence, which is not always applied benevolently.”

He added, “an exclusive US standard that is not globally respected or enforceable would likely fail to achieve its core purpose and would place US companies at a competitive disadvantage.”

Aman Mahapatra, chief strategy officer for Tribeca Softtech, a New York City-based technology consulting firm, said that a deep dive into how FINRA operates today is illustrative of what IT leaders can expect from this effort, assuming the industry adopts that model.

“When the CEOs of the five companies that would be regulated are also the primary drafters of the standards, the standards will reflect those companies’ interests. FINRA has an independent board, but the operational reality is that member firm perspectives dominate the working groups that write the actual rules,” he said. “There is no reason to expect an AI equivalent to work differently, and every reason to expect it to work worse, because AI standardization is happening faster than any industry has ever attempted to standardize itself, and speed is the enemy of independent oversight.”

Carmi Levy, an independent technology analyst, was even more emphatically opposed to the Hassabis proposal.

“Asking Big Tech companies to self-police is analogous to allowing foxes to guard the henhouse. It hasn’t worked to date, and it won’t work going forward. Expecting these organizations to somehow change their ways at this point in time represents the height of naïve thinking,” Levy said. “The framework proposed by Demis Hassabis is a self-serving roadmap for an industry bent on racing to the AI horizon regardless of the harms caused along the way. It is impossible to quantify the dangers to broader society should frameworks allowing self-regulation become the norm.”

Some love the proposal

An almost completely opposite stance came from Yuri Goryunov, CIO of consulting firm Acceligence, who applauded the proposed move.

“This is one of the rare setups where industry self-regulation has a real shot, and enterprise IT should be enthusiastically rooting for it,” he said. “It fails when harms are externalized, such as in social media content moderation. Or when the overseer outsources judgment to the overseen, such as the FAA’s delegation to Boeing before the 737 MAX. It works when everyone in the industry shares the catastrophic downside.”

He suggested, however, that the best precedent here isn’t FINRA, it’s INPO, the Institute of Nuclear Power Operations, which the nuclear industry created within months of the 1979 Three Mile Island partial reactor meltdown “on the logic that an accident anywhere is an accident everywhere. INPO peer-reviews every US plant, its evaluations move insurance premiums, and it sits on top of the NRC’s statutory floor. That is a public-private stack very close to what Hassabis is describing. Frontier AI has the same structure: one lab’s catastrophic failure brings regulation down on all of them.”

For enterprise CIOs and other IT executives, Goryunov said, that model has the potential for being a big win.

“Today, every enterprise duplicates the same AI diligence of red-teaming, eval suites, governance committees and each does so with less information than any certifying body would have,” Goryunov said. “A credible standards regime does for AI what UL did for electrical equipment and SOC2 did for cloud: it converts an unknowable risk into a procurable product and gives boards a defensible standard of care. That’s not red tape. That’s peace of mind with an audit trail.”

However, Mahapatra said, “the countervailing view is that the alternative to industry-led standards is probably not thoughtful legislation. It is probably no standards, or state-by-state fragmentation, or the current pattern of ex-post enforcement actions where regulators surface concerns years after harm has already occurred.”

Thus, he noted, “Hassabis is making the reasonable argument that imperfect fast standards are better than perfect slow ones, and there is genuine merit to that view for topics like agent identity, evaluation methodology, and interoperability, which are exactly the areas OpenClaw is also targeting.”

Google DeepMind CEO Demis Hassabis on Tuesday reiterated his push for an AI industry self-regulation effort, led by the US government, that is particularly focused on artificial general intelligence (AGI) and national security. 

But it is precisely that focus on national security that may make the results of such an effort, assuming it happens, less than palatable outside of the US.

“The rapid progress we’re seeing in AI requires a new approach to testing frontier AI model capabilities that is dynamic, adaptable, and rigorous,” Hassabis wrote. “The US is well positioned, given its economic and technical standing, to take the first step in developing such a framework. It could establish a new Standards Body modelled on a federally overseen public-private partnership or self-regulatory organization, much like the Financial Industry Regulatory Authority (FINRA), with a board that includes independent leading technical experts and open-source representatives.”

He noted, however, that the funding would need to be substantial, and would most likely come from industry, to allow the new body to attract world-class technical talent and obtain the necessary compute resources for large-scale testing.

Hassabis said he would propose that the organization “be responsible for developing assessment protocols and working with appropriate federal agencies and the US National Labs to conduct testing in areas relevant to national security,” and that AI vendor participants would be encouraged to adopt best practices, such as publishing model cards with technical details, maintaining strong internal cybersecurity, vetting key personnel, and providing sufficient resourcing for safety and security research.

This is not the first time Hassabis has expressed worries about AGI. He has already worked on a US government initiative evaluating AI safety, which involved DeepMind, Microsoft and xAI (now SpaceXAI) working with the Center for AI Standards and Innovation (CAISI), a division of the US Department of Commerce. It allowed CAISI to conduct pre-deployment evaluations and targeted research to “better assess frontier AI capabilities and advance the state of AI security.”  

The rest of the world may have concerns

Analysts and consultants were mixed about the move, with most expressing concerns about whether an industry-focused group would prioritize the public’s best interests.

“Self-regulation is not viable because it implies everyone is able to regulate themselves and will do so in line with the best interests of the public. Most tech vendors don’t have the capacity to self-regulate. They would just prefer a set of rules within which they can operate,” said Gartner VP analyst Nader Henein. “For-profit organizations are required to do what is best for their shareholders, and external regulation ensures that those organizations are never in a conflict of interest where they have to choose between what is good for their shareholders and what is good for the public.”

And, said Sanchit Vir Gogia, chief analyst at Greyhound Research, given the international nature of AI models, an effort coordinated by the US government might alienate other countries. 

“National security is the proposal’s accelerator in Washington and its poison pill abroad: the framing that opens the only gate available at home invites foreign capitals to read the institution as an instrument of American strategy,” he pointed out.

“The map is already plural,” he said. “Brussels switches on enforcement powers over general-purpose models [starting in August 2026], London runs the AI Security Institute, and Beijing licenses on its own terms. California and New York have legislated for frontier models at home. The durable route is shared technical evidence with sovereign enforcement, sealed through mutual recognition rather than deference, with India and the other major non-Western markets holding authorship rather than seats.”

Gogia added that the rules enacted by even such a group may not address all of the key concerns of enterprise IT. A US government effort along the lines that Hassabis is proposing would result in testing that “sits close to intelligence and industrial policy, and those functions will not stay neatly separated. A model can pass every catastrophic-risk test and still fail the enterprise on privacy, reliability, and liability,” he noted.

Walmart’s former director of cybersecurity Steven Eric Fisher, who is now an independent cybersecurity consultant, said he found the proposal “well-intentioned, but it addresses a highly polarized topic at a time when commercial interests carry unprecedented political influence, which is not always applied benevolently.”

He added, “an exclusive US standard that is not globally respected or enforceable would likely fail to achieve its core purpose and would place US companies at a competitive disadvantage.”

Aman Mahapatra, chief strategy officer for Tribeca Softtech, a New York City-based technology consulting firm, said that a deep dive into how FINRA operates today is illustrative of what IT leaders can expect from this effort, assuming the industry adopts that model.

“When the CEOs of the five companies that would be regulated are also the primary drafters of the standards, the standards will reflect those companies’ interests. FINRA has an independent board, but the operational reality is that member firm perspectives dominate the working groups that write the actual rules,” he said. “There is no reason to expect an AI equivalent to work differently, and every reason to expect it to work worse, because AI standardization is happening faster than any industry has ever attempted to standardize itself, and speed is the enemy of independent oversight.”

Carmi Levy, an independent technology analyst, was even more emphatically opposed to the Hassabis proposal.

“Asking Big Tech companies to self-police is analogous to allowing foxes to guard the henhouse. It hasn’t worked to date, and it won’t work going forward. Expecting these organizations to somehow change their ways at this point in time represents the height of naïve thinking,” Levy said. “The framework proposed by Demis Hassabis is a self-serving roadmap for an industry bent on racing to the AI horizon regardless of the harms caused along the way. It is impossible to quantify the dangers to broader society should frameworks allowing self-regulation become the norm.”

Some love the proposal

An almost completely opposite stance came from Yuri Goryunov, CIO of consulting firm Acceligence, who applauded the proposed move.

“This is one of the rare setups where industry self-regulation has a real shot, and enterprise IT should be enthusiastically rooting for it,” he said. “It fails when harms are externalized, such as in social media content moderation. Or when the overseer outsources judgment to the overseen, such as the FAA’s delegation to Boeing before the 737 MAX. It works when everyone in the industry shares the catastrophic downside.”

He suggested, however, that the best precedent here isn’t FINRA, it’s INPO, the Institute of Nuclear Power Operations, which the nuclear industry created within months of the 1979 Three Mile Island partial reactor meltdown “on the logic that an accident anywhere is an accident everywhere. INPO peer-reviews every US plant, its evaluations move insurance premiums, and it sits on top of the NRC’s statutory floor. That is a public-private stack very close to what Hassabis is describing. Frontier AI has the same structure: one lab’s catastrophic failure brings regulation down on all of them.”

For enterprise CIOs and other IT executives, Goryunov said, that model has the potential for being a big win.

“Today, every enterprise duplicates the same AI diligence of red-teaming, eval suites, governance committees and each does so with less information than any certifying body would have,” Goryunov said. “A credible standards regime does for AI what UL did for electrical equipment and SOC2 did for cloud: it converts an unknowable risk into a procurable product and gives boards a defensible standard of care. That’s not red tape. That’s peace of mind with an audit trail.”

However, Mahapatra said, “the countervailing view is that the alternative to industry-led standards is probably not thoughtful legislation. It is probably no standards, or state-by-state fragmentation, or the current pattern of ex-post enforcement actions where regulators surface concerns years after harm has already occurred.”

Thus, he noted, “Hassabis is making the reasonable argument that imperfect fast standards are better than perfect slow ones, and there is genuine merit to that view for topics like agent identity, evaluation methodology, and interoperability, which are exactly the areas OpenClaw is also targeting.”

This article originally appeared on CIO.com.

At Amazon Web Services (AWS), artificial intelligence is already compressing security work that once took months into minutes.

In the old world, human red teams would find vulnerabilities, write reports, refine those reports, and eventually hand them to defenders, who would then begin building detections or fixes, Steve Schmidt, chief security officer at AWS, tells CSO. That process could take “two, four, six, eight, 10 months,” Schmidt says.

“Now with proper application of AI, we can have the detections built for the problems the red team finds in 15 minutes-ish,” he says. “I think the outside is about four hours.”

That kind of workflow offers a glimpse of what AI could make possible for the most sophisticated security organizations: AI agents testing systems, other agents generating defenses, and human security engineers validating results and refining the feedback loop.

But it also raises a more uncomfortable question for the rest of the cybersecurity industry: What happens to organizations that cannot build anything close to that?

The concern has become significant enough that the Trump administration recently directed agencies to expand access to AI-enabled cybersecurity capabilities for resource-constrained organizations, including rural hospitals, community banks, and local utilities.

The order reflects a growing fear that AI could deepen a divide that has existed in cybersecurity for years: the divide between organizations with money, expertise, and engineering depth, and those struggling to keep pace with basic security demands.

Yet security leaders and practitioners suggest the impact of AI will be more complicated than a simple widening gap. Some experts say AI is merely adding a new layer to a long-standing security poverty problem. Others argue AI could democratize capabilities once reserved for elite organizations. Still others see today’s divide as real, but potentially temporary, as models become cheaper, more open, and easier to run.

The class divide was already here

For Matt Warner, co-founder and CTO of Blumira, the premise that AI is creating a cybersecurity class divide misses a key point: The divide already exists.

“I would go even a step further and say that there has been a class divide for the last 10 to 15 years,” Warner tells CSO.

What AI changes, he argues, is not necessarily the existence of the divide but how stark it becomes. Larger organizations have money, people, and time to experiment with AI. Smaller organizations often do not.

“The big differences that we’re seeing, especially from where we sit in the world, is the difference is getting starker in having the resources to leverage AI and the time to leverage AI more than anything else,” Warner says.

That distinction matters because many smaller organizations are already overwhelmed. Warner pointed to resource-constrained local governments and small or midmarket organizations that are still far behind large enterprises in basic IT and security maturity.

“I can find you a county in Michigan with two IT people for 2,000 employees,” Warner says. “Those people don’t have time to leverage AI and even learn how to use AI because they’re mostly just trying to put out fires.”

That problem is not unique to AI. Smaller organizations have long struggled to patch systems, prioritize vulnerabilities, monitor environments, and respond to incidents with limited staff. AI may help eventually, but only if those organizations have enough capacity to adopt it.

Wendy Nather’s framework gets an AI layer

Anton Chuvakin, security advisor in the office of the CISO for Google Cloud, sees the AI divide as part of a much older problem.

“I feel like it sends me back to when Wendy Nather invented the security poverty line,” Chuvakin tells CSO, referring to Nather’s 2011 concept describing organizations that lack the money, expertise, capability, or influence to implement effective security.

Chuvakin is skeptical that AI fundamentally changes that model. “I don’t think AI necessarily breaks that model,” he says. “I think it just adds another dimension.”

Cybersecurity has always been shaped by unequal access to top talent, tools, and services, Chuvakin argues. Large organizations could afford better SIEM deployments, advanced DLP programs, threat hunters, application security experts, and incident response retainers. Smaller organizations often could not.

AI may become another scarce resource, but Chuvakin cautions against overstating the role of model cost alone. In his view, the bigger structural issue may be talent rather than tokens.

“Prices for people won’t drop, but prices for LLMs may drop,” he believes.

That means the organizations with the greatest advantage may not simply be those that can afford the most expensive models. They may be the ones that can afford the people who know how to use them — and, as the frontier-access debate below suggests, that talent gap may prove more durable than any gap in model access itself.

AI creates new costs — and new uncertainties

Nather herself, now senior research initiatives director at 1Password, sees AI affecting every dimension of the security poverty line: money, expertise, capability, and influence.

The financial challenges are not limited to whether an organization can pay for an AI tool. In some cases, organizations that cannot afford enterprise licensing may end up making tradeoffs around privacy.

“If an organization can’t afford an enterprise license for the models they’re using, then they can’t keep their data private,” Nather tells CSO. “So, they have to give up privacy because they can’t afford privacy.”

That’s a new twist on an old dimension of the poverty line: It’s not just that under-resourced organizations lack a capability, but that the capability they can afford comes bundled with a risk wealthier organizations don’t have to accept.

Token-based pricing adds another problem: unpredictability. “At this point, nobody knows how much they’re going to burn in tokens at any given time,” she says.

That makes budgeting difficult for organizations that cannot absorb surprise costs. Nather also warns that usage-based pricing is controlled by providers and can change over time, leaving customers with limited leverage.

“The charging practice is in the hands of the providers, and they can change it at any time,” she says.

For organizations already operating below the security poverty line, that uncertainty could make AI adoption harder, even if the technology itself becomes more capable.

Access to frontier models may be a temporary divide

Dave Baggett, SVP/GM of the security suite at Kaseya, agrees there is security class divide dynamic playing out today, particularly around access to frontier models.

“There’s definitely a haves and have-nots issue around Mythos specifically because most people don’t have it,” Baggett tells CSO. But he doesn’t think the divide will have a long-term impact. Open-weight models, quantization, mixture-of-experts architectures, and increasingly powerful commodity hardware, he argues, are closing the gap faster than most people expect.

While not every organization will build a frontier model, he says, more organizations may be able to run capable models locally or use cheaper systems that approximate what today’s elite models can do.

“What it says for finding vulnerabilities is at that point, open-source people can run this stuff,” Baggett says. “Then you’re back to having a symmetrical opportunity where the defenders who are writing the open source can run the same tools the attackers would and have them fix the issues.”

His bottom line is that the divide may be real but short-lived. “Right now, there certainly is a have, have-not schism, but it may not be there for long,” Baggett says — a view Chuvakin shares, though he frames it in terms of the model market rather than open source specifically.

“I don’t think it’s the lowering prices example, but it’s more like you’re a top-tier model maker, I’m a second-tier model maker. My model in a year would do what your model did a year ago,” Chuvakin says.

The real advantage is operational depth

Schmidt’s description of AI use at AWS points to another kind of divide: not access to AI, but the ability to operationalize it.

AWS uses multiple models for different tasks, Schmidt says. One model may discover vulnerabilities, while other models validate results or help build defenses. Humans remain accountable for evaluating what the systems produce.

“Because we believe really strongly in human accountability for the use of AI from end to end, we still have humans take a look at what the systems come up with to determine whether they are reasonable and appropriate,” he says.

That workflow requires more than a model. It requires corporate data, secure infrastructure, feedback loops, security engineers, data scientists, and AI specialists who can work together.

Schmidt also pushes back on the idea that running AI locally on powerful consumer hardware is a substitute for production-grade security infrastructure. “Often the value of the model is also dependent on its proximity to data so that the model can ingest, use, and reason about data,” he says. “As a security person, I do not want that to be on your laptop.”

Experimentation on a laptop is useful, Schmidt says, but it is not the same as a secure production environment.

“I want the data to be somewhere safe that I can control, that I can see, that I can reason about, not sitting on your laptop,” he says. “Experimentation in there, awesome. That’s great. But it is not a production infrastructure component.”

That distinction may define the emerging AI security gap. Many organizations may be able to access AI tools. Far fewer may be able to safely integrate them into real security workflows.

The democratization argument

Phil Venables, a partner at Ballistic Ventures and former CISO of Google Cloud, takes the most optimistic view.

Asked whether AI is widening the gap between well-resourced and under-resourced security organizations, Venables tells CSO, “No, I actually think it’s the exact opposite.”

The reason, he argues, is that AI packages expertise and automation in ways that can be delivered broadly. “One of the fantastic things about AI, and we’re already starting to see this, is [that it’s] a great democratizer of capabilities,” he says. “AI packages up expertise and automation capabilities at a level beyond what prior waves of technology have done, and it makes it available at scale into organizations that have not previously been able to afford these things.”

He points to red teaming as an example. Nearly every organization would like a world-class red team, but few can afford one.

“Pretty much every organization on the planet would love to have a world-class red team to constantly test their security to find and fix things before attackers do,” Venables says. “But very few organizations have ever been able to afford to build a high-end red team.”

AI agents, he argues, could make that kind of capability available more economically. The same pattern could apply to insider threat; third-party risk; software security; governance, risk and compliance; and security operations.

“So even the smallest and resource-constrained organizations can now have access to a higher-end capability,” he maintains.

Venables does see a danger zone, however: under-resourced security teams inside organizations with aggressive AI ambitions. Those teams may struggle to keep up as the rest of the business adopts AI rapidly. But for many small and midsize organizations, he believes AI could improve access to security capabilities they never had before.

A divide over AI — or over readiness?

For elite organizations, AI is already becoming a force multiplier. Security teams with deep engineering talent, mature data infrastructure, and strong governance can use AI to accelerate testing, detection engineering, vulnerability discovery, and risk management.

For smaller organizations, the picture is less clear. AI may eventually package scarce expertise into affordable services. Open models may reduce dependence on expensive frontier systems. But organizations below the security poverty line still face familiar constraints: too few people, too little time, limited expertise, unpredictable costs, and weak leverage over vendors.

The emerging divide may therefore be less about who has access to AI and more about who can turn AI into durable security outcomes.

That makes the question facing cybersecurity more complicated than whether AI will create haves and have-nots. The industry already had them.

The real question is whether AI becomes another technology that rewards the organizations already best positioned to use it — or the first major security advance in years that helps those below the poverty line finally catch up.

Apple execs are now acknowledging the company is going to need to raises prices to cope with higher component costs, particularly memory. Apple has been battling to avoid doing so, but warned this week that those efforts are “unsustainable.”

The price pressure is being felt across the tech industry. Omdia predicts the average selling price (ASP) of smartphones globally will increase by around 20% this year — partly because AI industry demands have pushed up RAM prices and partly because of rapid cost increases in processor production as a direct consequence of war in the Middle East. This impacts anything that uses memory or processors.

Apple must raise prices, CEO warns

“We’re doing our best to mitigate the huge increases that are being passed to us, and we’ve been trying to shield our customers from the increases, but the situation has become unsustainable,” current Apple CEO Tim Cook told the Wall Street Journal.

OK, so prices are going to go up. But how?

If Apple must raise prices, the way it does so matters. Will it increase prices across the board, or choose a subtle, strategic approach that protects margins while also maintaining rapidly accelerating market share growth?

Until now, Apple has been benefiting from the rising tide in memory prices. While competitors have been steadily increasing prices across the board, Apple introduced its first $599 laptop even as it eats into mid-range smartphone sales. 

To grow, or to grow less?

The PC industry is not growing (outside of Cupertino), and Apple and its investors already know that the inordinately high customer satisfaction ratings its platforms enjoy mean that when people switch to them they tend to stick around. That’s why it matters that Cook highlighted record switchers and first-time platform users during the company’s most recent fiscal call. Apple is growing new customers, people who are likely to sign-up for services, accessories, and future upgrades. Winning the customer is an investment in future performance.

When it comes to increasing prices, we’ve seen some gentle experimentation in the Mac mini line, where the entry-level $599 model was effectively replaced by a more expensive $799 SKU. There have been suggestions Apple could do the same with the $599 MacBook Neo, dumping the entry-level build in favor of the (better) $699 model. I’m not certain that’s the best approach. 

A matter of choice

I’d argue that the $599 price point is psychologically the best bit of marketing Apple has ever done as it draws new users to look at its platforms. I also think substantial numbers of people already shift up to the $699 version, if they can, making that entry-level price an incredibly effective marketing tool with which to woo customers. It looks like that approach is working; the MacBook Neo has led the Amazon Laptop Sales chart ever since it was introduced and is selling in huge quantities globally.

Francisco Jeronimo, IDC vice president for data and analytics, sees this, writing: “It will be interesting to see whether Apple raises prices across its entire portfolio or plays strategically by subsidizing its more affordable products to gain market share and grow its installed base.”

If Apple raises prices at the price sensitive, entry-level end of its market, it runs the risk of slowing its own resurgence. Given that a 10% price hike here is only worth around $60, the revenue generated might not justify blunting Apple’s offer to new users.

Pricing and strategy

But Apple isn’t defined by its entry-level markets. Its biggest earning opportunities continue to be generated by the aspirational middle class, who, while feeling the pinch of declining living standards in many markets, can still handle the price of a top-of-the-range iPhone Pro. A 10% price increase at that end of the market will generate a much more significant chunk of revenue – and since rival vendors are raising their prices at this end of the market, Apple can follow suit while still seeming affordable. It’s an approach that maintains growth while optimizing margins.

Apple generates roughly half its revenue from iPhone sales, with the Pro and Pro Max devices accounting for a significant chunk of that money. Consumers looking to buy one of Apple’s high-end devices are likely to be less price-sensitive than those making purchases at the low- and mid-points of the company’s market range. 

With that in mind, it makes more sense for the company to raise prices at the top, while working to maintain the cost of entry at the lower end. It could, of course, raise prices universally, but doing so makes less sense.

What next?

Whatever strategy Apple adopts, company leadership is clear that component pricing is hitting hard. “We definitely need memory pricing and supply to return to reasonable levels for consumer products. That’s the bottom line,” Cook said.

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The latest release of qvac-fabric-llm.cpp, the inference engine of the QVAC Fabric LLM, features TurboQuant integration for resource management in long-running inference sessions. Tether adopts the technology as a path to better efficiency when running large

language models on devices with limited compute resources.

TurboQuant is Google’s response to the Key-Value (KV) Cache’s capacity expansion during routine inference, which can reach up to 8GB for a 262,000-token context session using a 4B-parameter large language model.

Tether takes the stage as the first AI research team to ship the KV Cache compression algorithm to a publicly available local AI model. The Turboquant integration will be included in the latest version of the QVAC SDK (v0.12.0) and will be available through Fabric, the inference and fine-tuning engine of the SDK.

This enables developers to serve intelligent models via the qvac-fabric-llm.cpp and compute inferences with negligible loss in precision, while consuming up to 5x less VRAM regardless of context size.

Why does this matter?

When you use an AI assistant, the model records the results from your previous prompts in temporary memory on your device. This record is known as the Key-Value Cache. The KV Cache is akin to a notepad for jotting down key points in a discussion or while reading a book.

For an AI model, it serves as a reference point each time you ask a follow-up question. This makes it easy for the model to follow up on your conversation without having to re-run the whole thread, which would waste a lot of time.

Transformer-based AI models build their KV Cache by storing the “key points” and their identifier, token-by-token (equivalent to “word for word”) in square grids. When you ask a follow-up question, the model sorts the key point using their precise location in the grid and computes new inferences based on your new input (prompt/question).

The KV Cache is a memory optimization technique and ensures a smooth run throughout each usage session. However, as you continue your conversation with the AI assistant, the KV Cache grows larger and consumes more memory on your device.

For instance, a few hours of conversation that grows into a 262,000-token session could consume up to 8GB of VRAM, which is hardly available in user-grade devices. Despite being temporary, KV cache overload could limit how an AI application can be used, especially when running models locally on devices with limited compute capacity, which is the case for the majority of users. KV Cache bloat is a major bottleneck for local AI, pushing users to cloud-based AI.

As a solution, TurboQuant strategically reduces KV Cache memory bloat by converting high-precision data vectors into lower-bit integers. Similar to reducing the size of the handwriting on the notepad or using signs instead of plain words, it shrinks the space the KV cache occupies.

How Turboquant compresses KV Cache memory

TurboQuant drastically reduces the memory consumed per cached token by using Polar quantization (PolarQuant) and Quantized Johnson-Lindenstrauss (QJL) techniques to bypass traditional quantization methods that require storing full-precision constants for small blocks of data.

It pairs PolarQuant’s structural efficiency with QJL’s zero-overhead error correction to compress caches up to 3 bits, delivering up to 5x improvement in memory management.

PolarQuant is what reduces the handwriting on the notepad or converts it into signs that consume less memory. It maps the KV Cache data onto a fixed circular grid and uses polar coordinates rather than the standard Cartesian (X, Y, Z) coordinates to locate key points.

This limits the details required to locate data to Angle (meaning of the data) and Radius (weight or importance of the data), rather than the full locational layout. It avoids the expensive data-normalization steps by replacing square grids with circular grids, simplifying vector representation and data location. This is similar to rewriting “Add 7 apples, then add 4 apples” as “Add 11 apples total.”

When compressing KV Cache data with PolarQuant, there is a risk of reducing the data’s weight score (importance rating). This is where the QJL comes in; it acts as a mathematical error-checker that corrects for a possible loss in attention score (the importance given to the data) during quantization. QJL uses signed bits (+1 or -1) to balance quantization errors. This way, it keeps the attention score perfectly (or nearly) accurate by balancing low-precision data with high-precision queries.

TurboQuant on QVAC SDK: More possibilities for Local AI

TurboQuant is a major breakthrough for local and cloud AI, but especially for local AI, where computing overhead is a major bottleneck for routine use. Tether recognizes the technological brilliance of the algorithm and its potential for models built to operate on tight resources. By compressing what would normally consume 8GB of VRAM down to 1.6GB, TurboQuant frees up resources for your inference machine, expands your bandwidth, and imagination of what can be done with a local superintelligent setup.

The TurboQuant integration via qvac-fabric-llm.cpp is supported by the Vulkan backend. This offers important compatibility and performance advantages attributable to Vulkan’s agnosticism and TurboQuant’s direct GPU execution.

Vulkan support bridges the advantages that TurboQuant offers to a wider range of user-grade devices and vendors outside the NVIDIA ecosystem (AMD and NVIDIA are currently supported, with mobile GPUs planned). It enables users and developers to run highly optimized, compressed local inferences on a wide range of platforms, including personal computers and mobile device GPUs.

TurboQuant’s KV Cache compression happens directly on the device’s GPU and aligns with how a computer naturally handles operations. This means the maths is done on the GPU’s fastest, closest memory, ensuring that models served with Fabric achieve the full 5x reduction in KV cache size while maintaining performance and precision. This lets users run much longer contexts (over 262,000 tokens) without running out of VRAM capacity.

TurboQuant lets you do more, with fewer resources, and in everyday environments. From simple follow-up queries to reviewing files that run in multiple gigabytes on your personal computers or mobile phones, it expands the scale of what can be done with an AI application. In QVAC SDK, it complements other optimization techniques inherent in Tether’s AI framework to power native intelligent systems that support an infinite number of users and autonomous agents. In a ten-billion-strong society, such systems will form a secure, viable, and unstoppable foundation for building the most complex superintelligent units for everyday use, biotechnology, and more.

From a macro perspective, compression techniques that reduce the operational resources required by AI models are the industry standard. The ability to develop and integrate such techniques will significantly impact the success of local AI models and infrastructure.

Tether is committed to building AI solutions that run on any setup and let choose their own biases. Follow the QVAC revolution and contribute to Tether’s drive for open source AI.

The financial services industry is the fourth most-targeted sector globally, accounting for 12% of all observed activity. eCrime and nation-state adversaries spanning all motivations target these organizations due to their unique convergence of valuable assets, strategic intelligence, and geopolitical significance.

The CrowdStrike 2026 Financial Services Threat Landscape Report analyzes the key trends shaping the sector from April 1, 2025, through March 31, 2026. The report provides insights organizations need to anticipate threats and strengthen defenses as attacks continue to evolve.

Hands-on-keyboard intrusions against financial institutions increased 43% globally and 48% in North America over the past two years. As these threats accelerate, businesses must understand how adversaries operate in order to stop them.

eCrime pressure on financial services intensifies

eCrime activity targeting financial services escalated in 2025. Big game hunting (BGH) adversaries named 423 financial services entities on dedicated leak sites, a 27% increase year over year.

MUTANT SPIDER drove the highest volume of intrusions during the reporting period and likely sold access to ransomware operators. SCATTERED SPIDER resumed aggressive ransomware operations against insurance entities following a significant pause.

Additional eCrime activity included:

• CHATTY SPIDER conducted high-tempo data theft and extortion campaigns targeting legal and financial services organizations, leaking data from 41 victims.
• SOLAR SPIDER targeted financial institutions across Europe, the Middle East, South Asia, and Southeast Asia using transaction-themed lures to deploy remote access tools.
• PLUMP SPIDER has targeted Brazilian financial entities since at least 2023 in attempts to access payment systems and conduct fraudulent transactions.

Nation-state adversaries scale theft and deception

Democratic People’s Republic of Korea (DPRK)-nexus groups sustained operations targeting cryptocurrency and fintech entities. These adversaries stole $2.02 billion in digital assets in 2025, a 51% increase from 2024. Stolen funds directly support the regime’s military programs. PRESSURE CHOLLIMA stole $1.46 billion in cryptocurrency through trojanized software distributed via supply chain compromise — the largest single financial theft ever reported.

DPRK-nexus threat actors increased operational tempo and advanced their social engineering tradecraft against financial entities. FAMOUS CHOLLIMA doubled their operations, targeting cryptocurrency exchanges, fintech platforms, and traditional banks.

STARDUST CHOLLIMA tripled their operational tempo, using recruiter impersonation, malicious coding challenges, and synthetic video conferencing environments to target fintechs across North America, Europe, and Asia. 

China-nexus adversaries posed the most significant intelligence collection threat to financial services organizations, especially in South and Southeast Asia. These operations likely reflect interest in regional financial systems and economic intelligence.

Observed China-nexus tactics, techniques, and procedures (TTPs) include: 

• HOLLOW PANDA targeted financial institutions in South America and Southeast Asia.
• VAULT PANDA deployed KEYPLUG malware via DLL search-order hijacking.
• GENESIS PANDA targeted a Southeast Asia-based financial entity and a North American fintech organization using VShell implants and FScan utilities.
• MURKY PANDA deployed a Chinese operational relay box (ORB) network to access Microsoft 365 email accounts from more than 150 IP addresses in 36 countries; they targeted 340 organizations across 30+ sectors, including financial services.

The trends outlined in this report create significant operational risk for financial services businesses. Ransomware pressure, sustained intelligence collection, and continued digital asset theft often move quickly through trusted access paths. As AI capabilities advance, adversaries are likely to increase the sophistication, scale, and speed of their operations.

Defenders need intelligence-led visibility, continuous hunting, and the ability to act quickly with context. CrowdStrike Counter Adversary Operations combines threat intelligence, managed threat hunting, and trillions of telemetry events from the AI-powered CrowdStrike Falcon® platform to detect, disrupt, and stop evasive adversaries. 

Learn more: Download the CrowdStrike 2026 Financial Services Threat Landscape Report.

A widely active phishing-as-a-service (PhaaS) operation known as FlowerStorm has begun using a browser-based virtual machine to conceal credential theft code, marking what researchers say is an escalation in phishing-kit sophistication that could make attacks harder for traditional email and static-analysis tools to detect.

Researchers at Sublime Security said in April that they identified the campaign, which used KrakVM, an open-source JavaScript virtual machine recently published on GitHub, to obfuscate malicious code delivered via HTML attachments in phishing emails.

The campaign targets credentials and multi-factor authentication (MFA) codes for services including Microsoft 365, Hotmail, and GoDaddy, while also supporting adversary-in-the-middle (AiTM) interception techniques designed to hijack authenticated sessions.

“What makes this campaign notable is the adoption of KrakVM as a delivery wrapper within a month of the project’s public release,” the researchers wrote in a report.

The findings highlight how phishing operations are increasingly adopting techniques traditionally associated with sophisticated malware campaigns, including virtualized execution environments and layered obfuscation frameworks.

Browser-based VM used to hide phishing payloads

According to the report, victims receive phishing emails containing HTML attachments disguised as voicemail notices, invoices, or vendor communications. When opened in a browser, embedded JavaScript immediately launches a credential-harvesting workflow tailored to the victim’s environment.

The attack chain uses KrakVM to compile malicious JavaScript into encrypted bytecode, which is executed through a virtual machine running inside the browser.

“KrakVM compiles JavaScript into unreadable bytes,” the researchers wrote, adding that the virtual machine then interprets and executes the payload at runtime.

The approach adds multiple layers of obfuscation designed to complicate static analysis and evade traditional email-security tooling.

While virtual-machine-based obfuscation has long been used in malware packers and software protection systems, its adoption inside large-scale phishing kits appears far less common.

The campaign dynamically adapts to victims

After deobfuscation, the phishing payload loads infrastructure designed to impersonate Microsoft 365 and other login portals while dynamically adapting to targeted users.

According to the report, the malware can determine which authentication provider should be impersonated, preload victim email addresses into phishing pages, and customize branding elements such as company logos and backgrounds.

The phishing kit also enumerates MFA methods registered on victim accounts, including Microsoft Authenticator push notifications, TOTP codes, SMS authentication, and voice verification flows.

When the victim enters credentials, the kit forwards them to a command-and-control server, which attempts a real login against the target service. If the service prompts for MFA, the kit presents the victim with a matching prompt, captures the response, and forwards it to complete the attacker’s session.

Researchers said the framework supports real-time AiTM interception, allowing operators to relay authentication sessions while harvesting credentials and MFA tokens.

“A widely known unique feature of FlowerStorm is its capability for advanced AiTM and MFA interception,” the report said.

Detection challenges grow for defenders

The combination of VM-based obfuscation and AiTM-capable payload creates a detection gap for email security tools.

Sublime Security said its own Autonomous Security Analyst system identified the attack as malicious, partly because of the HTML attachment’s use of “heavily obfuscated JavaScript with custom virtual machine bytecode.”

The researchers also noted that both KrakVM and FlowerStorm appeared to operate close to their default configurations, suggesting the campaign did not require advanced technical sophistication from operators.

That raises concern that VM-based obfuscation techniques could spread quickly across phishing ecosystems if tooling becomes easier to operationalize, the report added.

The broader phishing ecosystem is evolving

The campaign has targeted sectors including local government, logistics, retail, communications, and real estate, according to the report. Researchers also identified infrastructure using domains designed to resemble court systems, enterprise portals, and Microsoft-related services.

Sublime published 153 indicators of compromise, including dozens of subdomains on cloud object storage services across regions, including Singapore, Bangkok, Frankfurt, Tokyo, Seoul, Jakarta, and Ashburn.

The researchers also identified domain naming patterns that overlap with prior FlowerStorm reporting, including German-language domains assembled from English words to mimic legitimate business names.

Sophos had documented FlowerStorm in December 2024, after the kit emerged following a disruption to the Rockstar2FA phishing service. The researchers said they had found no evidence linking the KrakVM developer to FlowerStorm operations.

The findings come as security teams face increasingly sophisticated phishing campaigns that blend credential theft, MFA interception, session hijacking, and anti-analysis techniques into unified attack chains.

“This campaign likely represents only the earliest use of KrakVM’s obfuscation capabilities,” the researchers wrote. “We anticipate more complex implementations as its adoption grows.”

Artificial intelligence (AI) is no longer the playground of hobbyists and programmers. From automating customer‑service transactions to optimizing supply‑chain decisions, AI is rapidly becoming the central nervous system of today’s enterprises. McKinsey surveys have found that nearly nine in ten organizations are now using AI regularly in at least one business function, compared with 78% the previous year.

But adoption rates are far lower when it comes to scaling AI programs throughout the enterprise. Only about one‑third of companies have advanced past the pilot stage. Two‑thirds of organizations use AI technologies in multiple functions and 64% believe AI has had a positive impact on innovation. Just 39% say they’ve seen a significant impact on the bottom line.

This research shows that AI has gone mainstream, but its benefits are still concentrated in the hands of a relative few. This reality makes even more compelling the case for establishing a formal center of excellence (CoE).

Per Microsoft’s cloud‑adoption framework, a CoE is a centralized team responsible for standardizing best practices across the organization. An AI CoE functions as an internal team of experts that helps business units realize valuable and successful AI outcomes while avoiding pockets of AI solutions built without governance or standards. A well‑managed AI CoE builds consensus for standards and pilots and provides business and technical guidance to help convert AI excitement into measurable business value.

An artificial intelligence Center of Excellence is a strategic capability, not an ivory tower nor a vanity project for the few AI early adopters. An AI CoE can dramatically improve the quality and safety of your organization’s AI adoption efforts. In this article, we will discuss why every organization needs an AI hub like a CoE. We’ll explore how to staff and structure your center and provide recommendations to help it adapt over time.

Why create an AI CoE?

An AI CoE “acts as the central nervous system” for your company’s AI strategy. Without a centralized AI organization:

• Siloed initiatives. Separate business units kick off AI pilots that aren’t centrally coordinated, leading to duplicated effort and fragmented results.
• Inconsistent governance. Data governance, model security and compliance efforts differ from project to project putting your organization at risk of regulatory infractions or biased models.
• Lack of standards. Groups spend time reinventing the wheel instead of building on reusable assets and shared toolkits.
• Difficulty scaling. Smaller pilots become roadmaps unto themselves because there’s no standardized process for prioritizing and scaling AI solutions.

An AI CoE helps solve these issues by setting forth an AI strategy, aligning AI efforts with your business objectives and offering governance, policy and expertise. Additionally, an AI CoE promotes responsible and ethical AI principles through fairness, privacy and transparency policies. According to IDC research, CoEs enable cross‑functional collaboration between data scientists, domain experts and chief finance officers to help translate AI prototypes into repeatable solutions that align AI spending with business strategy. IDC analysts also find that effective CoEs can provide talent development, knowledge sharing, partnerships and an innovation mindset.

The business case only strengthens when looking at the economics of generative AI specifically. IDC’s 2024 “Business Opportunity of AI” study, sponsored by Microsoft, revealed generative‑AI adoption rose from 55% in 2023 to 75% in 2024. Companies that deployed generative AI were also found to experience significant returns. Organizations see an average ROI of $3.7 realized for every dollar spent on generative AI. For high performers, ROI reached $10.3 for every dollar spent. The majority of companies spend less than eight months deploying generative AI solutions, with ROI seen after just thirteen months. The research highlights how well‑managed AI initiatives can produce exceptional returns and why centralized governance and expertise are critical.

In addition to mitigating risks, a properly established CoE also creates opportunities. According to IDC, successful COEs can provide workforce enablement, knowledge dissemination, strategic alliances, certification and training and a culture that fosters innovation and creativity. Leaders at Hitachi Vantara call their COE “enabling AI to go from theoretical exploration to practical implementation that optimizes processes, supercharges efficiency and unlocks data‑driven insights.”

When it comes to AI in financial services, BizTech Magazine found that while 81% of executives say they are currently using AI and will invest more money in it, only 25% of them have completely deployed monitoring and management tools even though 87% said they already have governance in place. This is a clear example of how businesses are adopting new technologies but lack operational maturity and how a centralized CoE can help bridge that final mile and make governance frameworks a reality.

Business impacts achieved through CoEs are faster AI adoption, more efficient resource utilization, better decision‑making, lower risk, stronger strategic alignment and better collaboration. Boards and investors are demanding it; according to IDC analysts, organizations with mature AI governance have seen increased returns on invested capital (ROIC) and view CoEs as critical source of competitive differentiation.

How to build and structure the CoE

An AI CoE should have an executive sponsor who can offer budget, authority and credibility to ensure standards are upheld. Create a steering committee of business and IT leaders and schedule frequent reviews to monitor progress. Then, identify a leader for your CoE who is devoted to its success and has deep AI skills, as well as reach across the enterprise. Hire a diverse team of business leaders, data scientists, machine‑learning engineers, governance and security professionals. This variety of backgrounds will help ensure AI initiatives meet technical and business needs, as well as regulatory and ethical standards.

Define the operating model and responsibilities

IDC analysts note that an important objective of a CoE should be to close the supply‑and‑demand gap for AI skills. This can be accomplished by creating a centralized team of employees from different business units or geographies who pool their collective knowledge and then disburse back out into the business. Hardy recommends that this cross‑functional team include not only deep technical experts such as data scientists, AI engineers and machine‑learning specialists but also business leaders as well as IT and cybersecurity professionals. These members can help ensure AI initiatives are applied to business problems and integrated into production environments securely. Team members may include data scientists, software engineers, business analysts, subject‑matter experts and project managers. Common skills include domain knowledge, programming and data skills, problem solving, communications and a team mindset. Skills required of the Center of Excellence leader include a strong knowledge of AI, along with a visionary but execution focused approach to work. Additional leadership traits include practicing radical candor with your teams and colleagues while staying agile and flexible in your decisions due to the rapidly changing nature of AI.

Skills gaps are another common challenge to AI scaling. According to Microsoft’s 2024 IDC-commissioned survey, 30 % of respondents stated their organizations don’t have specialized AI skills and another 26 % said their organization has too few employees with the skills necessary to learn and work with AI. By tapping into talent from across the business, the CoE can centralize hard-to-find expertise. It can also administer training initiatives to fill these gaps. The CoE should partner with HR leaders to create learning journeys, certification initiatives and mentorship programs to ensure the talent pool continues to grow with advancing AI technology.

Determine where the CoE sits in your company’s hierarchy. A centralized hub makes sense early in your AI journey to centralize knowledge and ensure consistent practices. But as your organization adopts AI, the CoE can evolve into a decentralized, enablement model that provides guardrails and allows product teams to own their own AI applications. Clearly establish your CoE’s primary functions. These may include:

• AI strategy and use‑case identification. Partnering with business leaders to identify and prioritize AI opportunities that will provide the most value to the organization.
• Skills development. Determining your current level of AI skills and implementing learning and hands‑on experimentation programs.
• Pilot projects. Leading targeted pilots to prove out AI methodologies and provide proof of business value.
• Standards and governance. Establishing governance frameworks and security standards, monitoring usage to ensure AIs are being used ethically and performing routine data security and compliance audits.
• Intake and prioritization. Establishing a formal process to accept requests and evaluate them based on potential business value, feasibility and resource demands.
• Reusable assets. Creating checklists, templates and code libraries to speed up future initiatives.
• Metrics and reporting. Measuring adoption, compliance and business value and using that information to foster continual improvement.

Integrate ethics and responsible AI

AI solutions should be ethical, which means they should be helpful and unbiased. The CoE should develop policies around responsible AI use, so models are transparent, unbiased and reflect company values. Teams should conduct audits of training data and model outputs to identify and reduce bias and the potential for inadvertent harm. AI governance should include privacy and data‑security principles.

Avoid bureaucratic bottlenecks

One pitfall of CoEs is that they tend to turn into gatekeepers and slow down innovation. Instead, Microsoft recommends shifting the CoE from being a gatekeeper to playing an advisory role once your AI adoption becomes more established. Build AI delivery into platform teams and allow product teams to execute against AI solutions under guardrails. The CoE can concentrate on things like setting standards, sharing knowledge and mentorship while teams on the frontline own the execution.

How to ensure continuous improvement

AI is still an emerging technology and science, which means that a one‑time standing CoE will become irrelevant almost immediately. It should evolve constantly, leveraging three primary levers:

• Feedback and learning loops. Establish processes to capture input from users and stakeholders in production and pilot environments. This feedback should be used to update models, training datasets, documentation and governance processes.
• Investment in skills and culture. Embed AI literacy into your culture by providing regular training and building communities of practice. Forums like these allow employees to share failures and best practices. Focus change‑management efforts on employees’ misperceptions about how AI will replace their jobs. Communicate how AI tools will make their jobs easier instead.
• Metrics‑driven evolution. Define a measurement framework that encompasses adoption, compliance and ROI metrics. Use these measurements to surface bottlenecks and opportunities for improvement. If your metrics indicate your central governance is slowing adoption, consider a more federated approach.

Researchers from IDC stress that “unlocking the power of frontier AI … requires building a culture of continuous learning.” COEs should implement processes like internal training initiatives, communities of practice and sandbox spaces for experimentation so that knowledge can continue flowing to employees as fast-changing AI capabilities develop. Benchmarking your progress is also key, say the analysts in their suggestions for how to measure COE success with AI. IDC recommends defining clear goals, building KPIs into projects, tracking completed initiatives, gathering feedback on customer satisfaction and looking at indicators for revenue growth and innovation.

Conclusion: A strategic capability, not a side project

An AI Center of Excellence won’t solve every challenge. It needs ongoing senior leadership sponsorship, cross‑functional teamwork and an openness to shift your operating model as your organization matures in its AI journey. However, when done right, it provides a framework for enterprise‑level AI enablement. It ensures AI initiatives are aligned to business priorities, sets standards and governance, develops your workforce and speeds up the responsible delivery of AI solutions.

AI Centers of Excellence are already helping organizations drive value across industries by scaling AI adoption, strengthening governance, accelerating experimentation and moving AI use cases from ideation to production.

• Financial services. Major banks are forming federated AI CoEs made up of divisional CoEs within business units like retail banking, wealth management and asset management. These cross‑functional teams customize AI for their functions, whether that’s portfolio optimization or customer support automation, backed by a centralized GenAI layer that provides unified governance, tools and evaluation frameworks. The federated approach limits redundancy, fosters collaboration and scales AI more broadly.
• Professional services and technology. With Microsoft’s guidance, NTT DATA developed an agentic AI CoE. The center offers a centralized environment for customers to design, deploy and operate AI agents spanning different cloud environments. Highlights include unified governance that’s aligned with its cloud center of excellence (CCoE) architecture, shared infrastructure to build agent‑based applications and coordination with Microsoft subject matter experts. The AI CoE serves as an engine for delivery, helping accelerate the path from experimentation to production at scale with security built in.
• Consulting firms. Capgemini applies the principles of an AI CoE to its suite of offerings to ensure consistent AI governance, reuse assets and tools and link AI projects to quantifiable business outcomes. Standardizing the how behind project execution allows Capgemini to decrease variation across customer projects and empower organizations to operate at speed without losing sight of enterprise needs.
• Enterprise experimentation. EY created an AI CoE focused on providing a secure sandbox environment. Teams can experiment with AI use cases, validate their feasibility and associated risk and fast‑track promising use cases to production. Centralized visibility and governance allow for consistent security and compliance standards, shortening the time between ideation and execution and preventing siloed adoption.

Taken together, these industry examples highlight how a CoE is less of a technology endeavor and more focused on creating institutional trust and capacity. Want more proof points on how COEs make an impact? Consider how at ECS, a provider of cloud, cybersecurity and artificial intelligence (AI) services, the data and AI COE unify more than 200 data professionals across the business, shares their collective expertise across town halls and events and manages strategic partnerships. The COE enables proposals, solutions and fosters a culture of creativity and innovation.

Over at Hitachi Vantara, the AI COE is tasked with transforming ideas into production‑ready prototypes and is already being recognized for improving efficiency across operations and creating new revenue streams from advanced machine‑learning models. The board wants ROI and organizations with mature AI governance and COEs can increase returns on invested capital and create new sources of revenue. These are just a few examples of how a well architected CoE turns strategy into tangible business value.

The race for AI supremacy is picking up speed. Organizations that invest time and resources into building an effective AI Center of Excellence will be best positioned to turn innovation into competitive advantage. The CoE is how your biggest ideas go from concept to business solution–helping leaders do their best work.

This article is published as part of the Foundry Expert Contributor Network.
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Serving in the military requires a precise, tactical mindset, and that’s exactly what Barry Hensley espoused during his 24 years in the US Army, where he rose to the rank of colonel.

The military “is where you earn your stripes, showing your soldiers your willingness to jump into a foxhole and pick up a weapon,” says Hensley, CSO of Brown & Brown, an independent insurance brokerage firm.

As a security leader in an industry that is constantly evolving, Hensley has leaned on that in-the-trenches approach as a key part of his leadership ethos, even as the CSO role has grown increasingly strategic.

“A security leader needs to be close enough to the tactical fight to effectively guide the organization’s strategic direction, align with business goals, manage risk and investments, and influence culture,” he explains. Business units need to have confidence in a security leader’s level of expertise in a specific security domain so the leader can properly represent the risks and investments required. 

“Rolling your sleeves up in the middle of a security event is never a bad thing,” Hensley adds. “It shows your willingness to lead from the front and/or support in the most stressful situations.”

And increasingly stressful those situations have become, as the spotlight on CISOs has never shone more brightly. Now alongside increasing responsibilities that include data protection and privacy, third-party and supply chain risk, and regulatory compliance and reporting, CISOs must confront the rise of AI — both in the hands of bad actors and throughout the enterprise. And as the CISO role evolves and grows, many are rising to embrace the opportunity.

According to Foundry’s latest Security Priorities Survey, 95% of top security leaders regularly engage with the board of directors multiple times a month, up from 85% in 2023.

This is helping advance cybersecurity initiatives. The CISO’s elevated prominence is also leading to new reporting structures, the survey found, with 31% of respondents reporting that the top security leader reports directly into the board of directors. Only one in five respondents said their security chief reports into the corporate CIO, “another sign that cybersecurity commands its own infrastructure and leadership outside of IT.”

CSO spoke with Hensley and other 2026 CSO Hall of Fame inductees, about how they are governing as AI initiatives become firmly rooted in the enterprise.

Implementing an AI security framework

AI is a core component of Brown & Brown’s security strategy: enhancing SOC operations, streamlining vulnerability management, determining the risks/rewards of third- and fourth-party partnerships, and boosting security application development, Hensley says. 

“For 2026, publishing an AI security framework is our top priority to enable the business to move fast — safely,” he says. His staff is partnering with the firm’s AI engineering and enablement teams to perform AI risk assessments and ensure that AI is fit for purpose and used responsibly through the company’s AI Governance Working Group.

“AI is top of mind for our leaders and a prominent topic with the board of directors, serving as a key consideration and differentiator for our business,” Hensley says.

Companies need governance frameworks that require security reviews before any AI capability is deployed, agrees Shaun Khalfan, senior vice president and CISO of PayPal. This ensures use cases are evaluated against security requirements, data sensitivity, operational risk, and business impact.

“This is why I am a strategic business advisor for major AI business decisions at PayPal,” says Khalfan, whose team is applying advanced risk detection technology and oversight, including machine learning models running in real-time that evaluate over a billion transactions per month. 

The work includes maintaining tight risk and business alignment, incorporating new products into existing compliance and risk frameworks, and adapting them to the unique characteristics of each product, he says. 

Move fast, keep risk at bay

Like Hensley, Jeff Trudeau, CSO of Chime, says the role is fundamentally shifting from a control function to a strategic partner in how the business adopts AI responsibly. At Chime, that means being embedded early in how AI is built and deployed, not reviewing it after the fact, Trudeau says.

“We’re focused on three areas: securing AI systems themselves, governing how AI is used across the company, and helping leadership make clear risk/reward decisions as we scale,” he says.

Noting that AI increases both speed and surface area, Trudeau says his role is to ensure the firm can move fast without introducing unacceptable risk. “That requires tighter integration with engineering, product, and data teams, as well as more direct engagement with executive leadership and the board on how AI changes our risk posture.”

Khalfan also characterizes himself as a strategic CISO with a strong operational and engineering foundation. He strongly believes that a well-defined security strategy aligned to business goals is essential for the success of any cybersecurity organization.

“Security cannot operate as a separate function; it must be embedded in how the business grows, innovates, and continues to earn trust,” he says, adding that “strategy without execution is just theory. We operate in a threat landscape that changes daily, and there are moments when tactical action is critical to managing immediate risk.”

Rapid AI adoption is a perfect example, he says. Echoing Trudeau, Khalfan believes the CISO must help the organization move fast while still protecting customers, data, infrastructure, and reputation.

“The best CISOs know how to balance both, thinking long-term while acting decisively in the short term,” he says. 

All roads lead back to trust and strong governance, he notes. “Trust is the foundation of both technology and business. You must build trust in the system across customers, merchants, partners, and infrastructure to ensure AI and agent-driven transactions are reliable, secure, and verifiable.”

AI is creating the greatest security challenges

For Trudeau, the biggest challenge of the burgeoning AI era is the pace of change. AI is accelerating how software is built, how attacks are executed, and how quickly systems evolve. Traditional security models, periodic reviews, and static controls don’t keep up, he says.

“We’re addressing that by shifting to more continuous, embedded security practices. That includes integrating security into development workflows, investing in detection and response capabilities that adapt in real-time, and building stronger data governance around how sensitive information is accessed and used by AI systems,” Trudeau says.

At the same time, the focus is on maintaining trust at scale. “As we introduce more AI-driven experiences, we have to be clear about how systems behave, how decisions are made, and where human oversight remains,” Trudeau says. “That’s as much a product and trust challenge as it is a technical one.”

AI is also impacting what Brown & Brown is seeing with phishing campaigns, notes Hensley. “AI is maturing in its ability to impersonate individuals, both voice and video, while quickly generating supporting documents to further convince teammates that a fraudulent request is genuine.”

A preview of Anthropic’s Mythos release shows that AI can now rapidly discover previously unknown vulnerabilities and automate their exploitation, Hensley says. “This changes the paradigm. Vulnerability management will likely become a higher priority for organizations as they cannot wait weeks to patch hosts based on a perceived risk tolerance of mitigating controls.”

Most organizations will have to empower their IT platform providers to deploy automation for near-real-time patching — while holding them accountable for the contracted service-level availability, he says.

Managing identity, data, and humans

AI is not the only challenge CISOs have to contend with. Khalfan says that identity, data security, and context are his most important challenges to solve for.

“Identity is becoming more complex, as humans, machines, APIs, and autonomous agents all interact with critical systems,” he says. “Knowing who — or what — is requesting access and ensuring the right level of trust and least privilege is fundamental.”

Context is the multiplier, Khalfan adds. “Security decisions without business context create unnecessary friction, and business decisions without security context create unnecessary risk. Security leaders must create systems that make both visible in real-time.”

To execute, his team focuses heavily on getting the fundamentals right: strong data governance, dynamic policy tuning, continuous validation of the control environment, frequent deployment of security improvements, and designing controls that are embedded into workflows rather than added afterward, Khalfan says.

“Security at scale is less about isolated controls and more about building resilient systems that continuously adapt,” he says.

As much as AI has added new trials, Hensley finds that the human element, along with the expanding attack surface, remain the greatest security challenges. This includes the arms race between attackers and defenders. “Sophisticated social engineering is at an all-time high, challenging our teammates to be not only vigilant but also often the first line of defense,” he says.

To stay ahead, “we are tackling from all angles, including security awareness training, enabling new advanced AI features in our security tools, and taking more proactive actions on behalf of our teammates based on risk/reward evaluations,” Hensley says.

Hall of Fame advice on meeting the current CISO moment

Meeting today’s cyber leadership challenges requires CISOs to lead from the front — something both Hensley and Khalfan practice. That means only adopting AI that is secure and trusted. “Security should not be the department of ‘no’; it should help business partners move faster with confidence, Khalfan says.

Leading from the front also means challenging the status quo, and viewing yourself as a business partner/risk advisor, Hensley says.

For Trudeau, it’s about being able to translate risk into business terms.

Stay close to the business. “If you do not understand how your company creates value, you cannot effectively protect it,” Khalfan says. “Security leaders need to speak the language of growth, customer trust, and operational resilience, not just technical risk.”

Trudeau agrees, saying that security leaders must align their work directly to business outcomes. “If security is seen as separate from growth, you’ll always be reacting instead of shaping decisions.”

Be the enabler. “The best CISOs help the business move faster and safer, not slower,” Khalfan says. “Your job is not to create friction everywhere; it is to create friction where the risk is highest and remove it where trust can be increased through better design.”

Engage early. “The earlier security is involved in product and AI development, the more leverage you have to influence outcomes without slowing teams down,” Trudeau notes.

Khalfan echoes that, saying that data security, identity, and observability are the foundations on which trusted AI systems are built. Business and cyber teams must work hand in hand to ensure those outcomes are achieved, he says. 

“Whether it is defending against AI-enabled threats, protecting AI infrastructure, or evaluating the risk and reward of AI innovation, security must be involved early, not after deployment,” he adds.

Stay proactively compliant. Khalfan says that PayPal’s security organization continually monitors and updates its governance and requirements based on the evolving regulatory frameworks.

Solve business problems. This is a sure-fire way to meet the today’s cyber challenges and raise your profile as CISO. “When security becomes a driver of trust, speed, and competitive advantage, your seat at the table becomes permanent,” Khalfan says.

For example, Khalfan drove company-wide bot protection initiatives, a collaborative, multi-team effort that enhanced fraud prevention. It greatly reduced fraudulent traffic at the top of the process, resulting in higher quality customer engagement, he says.

Talk the talk. If you want to understand how to secure AI, you need to actively use AI, Khalfan stresses. “Security leaders cannot govern what they do not understand. Hands-on experience creates credibility and better decision-making,” he says.

This often requires investing in fluency beyond security to understand how AI systems work, how your company builds products, and what leadership cares about, Trudeau says.

Build credibility through consistency. “As the scope of the role expands, especially with AI, leaders are looking for clear, pragmatic guidance, not theoretical risk models,” Trudeau says.

There’s no ‘I’ in team

A core part of raising to today’s challenges and elevating your CISO role requires security leaders to bring your teammates along. They will always be your greatest resource, Hensley says. 

“My military experience is part of my DNA and has shaped every part of my life, especially how I think of teammate development, building highly cohesive functioning teams, and prioritizing what is most important,” he says.

So many things in life will come and go, but your impact on others will impact generations, Hensley adds. They carry your values forward from culture, ethics, and standards.

“My legacy will be the teammates that I have served alongside through my career,” he says. “I encourage security leaders to focus on the impact you can make on your team every day — it will ultimately serve to elevate your profile and leave a lasting mark.”

Data centers are reshaping our landscape at breakneck speed. Every AI query, online purchase, and streaming video now runs through these massive facilities.

The United States Department of Energy is leading the way in developing public private partnerships (PPPs) essential to facilitating AI’s expansion. Meta, Google, Microsoft, and OpenAI are racing to lock in long-term agreements, while state and local governments scramble to rezone farmlands and wetlands, and to offer tax breaks in exchange for the promise of jobs.

While there’s a desire to quickly build out the much-needed infrastructure of the future, the approach being used isn’t working. The news is littered with reports of litigation around data centers, ranging from states pushing back on the build-out of data centers, to the NAACP suing xAI for pollution issues. What was once dismissed as scattered NIMBY pushback is maturing into a multi-level political challenge to the AI-era infrastructure buildout. And it’s accelerating. Opposition to data centers rose 125% in Q2 of 2025 alone.

What does this mean for CIOs?  The data center crisis is no longer a niche real estate or environmental issue, but a supply chain risk.

How contracts contribute to the crisis

Professors Carolyn Heinrich and Deanna Malatesta, co-authors of Contracting for Public Value, warn that the way both public and private organizations approach their data center contracts is a key cause of recent data center lawsuits.

“In the rush to build, both tech companies and governments are using transactional contracts,” says Heinrich, a professor of public policy, education, and economics at Vanderbilt University. “While transactional contracts are easier to negotiate and administer, they’re inadequate to govern the layered, interdependent relationships that data center partnerships require.”

Malatesta, a professor of public affairs at O’Neill School of Public and Environmental Affairs at Indiana University, adds insight. “Transactional deals are static,” she says. “Instead of promoting the partnership-like relationships needed to cope with uncertainty, conventional contracts undermine them.”

The results are costly change orders, escalating disagreements, and eroding public trust. Or worse, litigation. So their advice? Formal relational contracts.

The pivot to formal relational contracts

Formal relational contracts are defined as legally enforceable contracts that establish a commercial partnership within a flexible framework based on social norms and jointly defined objectives, and with continuous alignment of interests before commercial transactions.

“Unlike traditional contracts focusing on legal obligations and penalties, relational contracts emphasize collaboration, communication, and problem-solving by specifying mutual goals and establishing governance structures to keep the parties’ expectations and interests aligned over the long term,” says Heinrich. “And because they embrace that it’s impossible to predict every what-if scenario, they’re a sound option for data center contracts.”

The University of Tennessee’s field-based research shows how the theory of formal relational contracting can be put into practice through what UT researchers call a Vested business model, an approach used by companies such as Intel, bp, EY, IBM, and the Canadian government that provides a proven framework to create mutually beneficial agreements that drive innovation and adaptability — just what data center contracts need.

What a relational data center contract looks like

The UT’s research into highly collaborative, win-win relational contracts suggests contracting partners follow certain rules to structure complex contracts. Here, we highlight how a Vested formal relational contract would work for data centers.

Define clear outcomes, not transactions. Making the shift means contracting for mutually defined and measurable business outcomes rather than transactions or activities. For example, rather than specifying exact servers or cooling systems at signing, formal relational contracts focus on mutually defined desired outcomes such as uptime, ambitious energy efficiency, and local job creation. A Vested formal relational contract also changes the lens of metrics from transactional and activity level metrics to tracking what matters, like energy cost per compute unit, carbon intensity, and local hiring rates. These KPIs enable transparent, real-time performance management without micromanagement.

Focus on the what, not the how. A relational contract adopts a flexible scoping approach that embraces the many uncertainties that leave room for parties to collaborate on when new tech and processes emerge. “Unlike conventional contracts, relational contracts promote a broad perspective, where all parties involved have clear roles and responsibilities through an end-to-end taxonomy and workload allocation, rather than a conventional statement of work, which typically only includes scope and work for the supplier,” says Malatesta.

Plan a pricing model with incentives to optimize outcomes. Traditional contracts often create inherent perverse incentives. For example, public partners and citizens pay power bills while private partners build facilities that may be less likely to prioritize energy efficiency. Formal relational contracts align interests with a win-win outcome-based economic model, so parties win and lose together. If energy efficiency improves beyond targets, both partners share savings. If new revenue emerges, both reap the benefits.

Find clarity on insight vs. oversight governance. Traditional contracts emphasize oversight through audits and inspections, often resulting in penalties that signal distrust and invite adversarial behavior. Formal relational contracts embed a robust, insight-based governance structure that includes transparent data sharing, joint decision-making, structured adaptation through ongoing strategy reviews, and agreed conflict resolution processes.

What forward-looking CIOs should do now

The data center backlash isn’t a PR problem for tech companies that CIOs can watch from a comfortable distance. CIOs who get ahead of it will be the ones who stop treating infrastructure as a utility and start treating it as a strategic asset requiring the same scrutiny applied to any critical supplier relationship. Some things you can do to mitigate risk include:

• Treating compute capacity like a supply chain risk. The same discipline applied to post-Covid supply-chain risk management needs to be applied to compute infrastructure. Stress test your AI and cloud roadmap against scenarios to understand the impact of a planned provider’s facility being delayed from one to two years.
• Auditing your provider’s exposure. Directly ask your cloud and colocation providers which of their planned or in-construction facilities face litigation or community opposition? What percentage of your committed capacity sits in contested markets?
• Building regulatory awareness into infrastructure planning. Jurisdictions that have streamlined approvals and close coordination with utilities can convert projects into construction more quickly. Geographic diversification alongside awareness of local regulatory conditions may help decision-makers balance cloud availability and risk. 
• Getting ahead of the ESG story. Several companies, including Microsoft, OpenAI, Google, Oracle, and Amazon, signed a Ratepayer Protection Pledge, committing to provide or pay for all power generation needed for AI projects. CIOs should demand similar accountability from providers, and be able to articulate their own organization’s data center footprint and environmental impact to boards and stakeholders. 

Why this matters now

The question isn’t whether the litigation and community opposition will affect your organization’s technology strategy. It’s whether you’ll find out before or after your AI roadmap hits a wall.

Governments and tech can continue using 20th-century contracts, guaranteeing conflict and missed opportunities, or they can embrace formal relational contracting by building data centers designed for collaboration, innovation, and shared success. For the sake of our digital future, we can’t afford to get this wrong.