As Apple gears up to anoint John Ternus the new company CEO in September (while current leader Tim Cook takes a seat on the board) the company appears to be firing on all cylinders ahead of the leadership transition. 

What’s going well

Just look at the evidence: 

• Apple is building market share across its entire product range; even memory-driven price inflation doesn’t seem to have dampened demand for its hardware yet.
• While Apple had to raise prices, the company’s MacBook Neo remains seriously popular. It’s sitting atop Amazon’s US best-selling chart, which currently includes six Macs in the top 10. The Neo has topped this chart since its introduction.
• Apple’s iPhone 17 series continues to sell well, with recent market data showing sustained growth. Both Counterpoint and IDC tell us that iPhone shipments continue to increase, even as other vendor shipments slide.
• IDC analyst Francisco Jeronimo recently estimated that Apple’s upcoming foldable iPhone Ultra could grab 29.4% of global folding smartphone sales this year, rising to 34.9% in 2027.
• The company’s new 27 series of operating systems is attracting a great response as beta testers report that it is already solid, stable, and performing well.
• The AI narrative has really changed, with analysts no longer quite so starry-eyed at the prospects for the big frontier AI firms. Apple’s edge-AI-enabling approach is winning converts.

What’s coming up

The company’s newly-filed lawsuit against OpenAI may or may not succeed, but it will certainly help consolidate recognition of the importance of Apple’s designs and intellectual property in whatever hardware emerges from the AI firm. It also means both Apple and OpenAI are already competing in hardware, even though neither company yet offers anything that directly challenges the other. 

Apple has just set out its stall to brand-loyal fans in a big way and did so before OpenAI gets to woo the same set of customers with a wriggle of its Jony Ive-tinged talisman.

The stage is set for intense competition between the two. Though some say Apple’s needs to improve employee retention, if it does find proof of efforts to use recruitment to engage in industrial espionage, it’ll be easier to represent its own products as being the OG for new hardware. 

If nothing else, it means consumers will forever be asking, “If OpenAI’s designers are so good, why did it need to poach them from Apple?” Doubt is a weapon.

Managing perception

It doesn’t matter how the case goes, because there fight is already affecting consumer psychology. It also means that as Ternus prepares to take his seat atop the rainbow-colored Apple throne, we can already size him up. “A man is measured by his enemies,” Joe Abercrombie wrote in “The Trouble With Peace.”

Given the proximity of the leadership transition, it’s highly probable that Ternus signed-off on the litigation; in doing so he — and Apple — tell us to expect more of the same. 

Apple has, rightly or wrongly, decided that OpenAI will become its new existential bugbear, following in the footsteps of Microsoft Windows, Real Networks, Adobe Flash, Android, and Samsung, all of whom have been useful foils against which Apple has been able to build and maintain its identity.

Looking at that list, you’d be tempted to believe that nothing much is new. Apple has often defined itself by the enemies it sometimes keeps. What has been will be again, which in this case means even as OpenAI attempts to carve out an identity as a hardware manufacturer delivering solutions to compete with Apple and Google, Ternus’ team’s looks to drive a consensus-shaped wedge into the pro-LLM propaganda. 

That blow comes as Apple finally gets its act together around AI, and as the company prepares for a future in which the world’s most-used wearable device also becomes the wearable way to woo Siri AI.

My kingdom come

Rising market share, powerful solutions, an increasingly recognized and respected approach to AI, and an ideological crusade — these details constitute Apple’s place today and are Tim Cook’s coronation gift to Ternus. He’s passing along a strong and hyper-profitable baton that screams of timeliness and relevance even as the company gets set, ready, to go with a year or two of new product designs, new product families, and a 20^thanniversary iPhone.

This is Apple’s party. OpenAI’s name didn’t make the list. 

You can follow me on social media! Join me on BlueSky,  LinkedIn, Mastodon and subscribe to The Core.

Just in time for the iPhone’s 20th anniversary, Apple is moving closer to becoming a service company. It is set to launch its new deal with Klarna next week and when it does, Apple enthusiasts in the US will effectively be able to subscribe to their favorite Apple hardware, with the cost spread across up to three years.

This matters because when combined with Apple One and Apple’s Creator Studio subscriptions, the Klarna arrangement brings Apple closer to offering a full subscription model for hardware, software, and services. The only thing you don’t get under the new arrangement is AppleCare, for which you’ll allegedly need to pay extra.

Moving closer to hardware-as-a-service

Apple has slowly been transitioning toward hardware-as-a-service for almost a decade. Back then, Forrester analyst Frank Gillet predicted the company would eventually offer bundles of services and products for a monthly, all-in, fee. 

This isn’t quite where we are yet; you still need at least three subscriptions to get close. But, after the better part of a decade, Apple has moved much nearer to the hardware-as-a-service idea.

There are some products reportedly excluded from the arrangement, including MacBook Neo, Apple Watch SE, the entry-level iPad, and iPhone 16. Clearly, Apple sees those products as sufficiently affordable. 

Easy payments for RAM-ageddon

The new Klarna arrangement comes as Apple is forced to increase product prices as AI-driven memory price inflation becomes widely felt across every economy. In theory, I assume, Apple hopes to make its products available to cash-strapped consumers who need new hardware, while also navigating a time of deep economic tumult and uncertainty. It’s thought the company has previously rejected these plans to protect normal hardware sales, but normality is a kingdom we no longer seem to possess. Interesting times. Probable inflation incoming.

“Apple Upgrade lands at precisely the moment Apple needs it,” IDC analyst Francisco Jeronimo wrote in a note seen by Computerworld. “Having just pushed Mac and iPad prices up on the back of the memory shortage, with iPhone increases widely expected in September — as well as the new iPhone foldable expected at $2,500 — Apple’s real risk is that rising prices even further can impact the upgrade cycle.” 

New age, new shopping habits

The introduction of the scheme gives consumers a way to purchase the company’s popular high-end devices when they are introduced — no doubt,at higher cost — this fall. Plus, of course, if it’s good enough for GM, it’s good enough for Apple.

It’s all about attitude, too. From Apple’s perspective, it has done plenty of the groundwork required to convince its customers that subscription payments for things you value are no bad thing. 

Reluctance to embrace “Access Not Ownership’”purchasing models has dropped dramatically since Apple — and CEO Tim Cook — first began banging the drum for services income. Apple’s services stream has now become its second-biggest revenue driver after the iPhone. It has over 1 billion paid subscriptions, and an active hardware installed base of more than 2.5 billion devices globally.

A combination of changed customer habits and external threat means the stars are now aligned for hardware-as-a-service models. “Reframing a device as a low monthly payment protects that [upgrade] cadence and allows Apple to start marketing their products as device-as-a-service to consumers, which no other vendor was ever able to do,” Jeronimo wrote to me. 

There is a one-more-thing aspect to this: the products are effectively being leased, a new approach that will give Apple a stronger grip on EOL devices, helping it grab more of them for refurbishment, resale, and recycling. Over time, this will give the company a much stronger grip on the lucrative second-user market that exists around Apple equipment, even while for almost every consumer product we find the life we want is something we can rent, but probably can’t afford to own.

Managing future risk

The other solid reason to take a partnership approach is risk management. Apple had intended to develop its own buy-now, pay-later scheme via Apple Pay Later, but abandoned that plan as it became riskier with rising bank rates. “Also, by backing the program with Klarna rather than reviving the in-house subscription plan it shelved in 2024, Apple captures the demand upside without taking the credit risk onto its own balance sheet,” Jeronimo said.

You can follow me on social media! Join me on BlueSky,  LinkedIn, Mastodon and subscribe to The Core.

It’s difficult to ignore the fact that Apple seems to have turned its MacBook Neo into a weapon to promote platform growth, with enough performance under the hood to make competitors seem inferior.

And even as the PC industry moves to try to compete with Apple’s last huge Mac success, the company is already planning a powerful follow-up.

That points to the discipline Apple has applied to the Mac since the introduction of Apple Silicon. The company has built a clear product roadmap, strong entry-level pricing, and steady performance gains. This focus is now paying dividends, giving people the impetus to keep placing their trust in Apple and its Macs — even as the industry raises prices in the face of RAMageddon and price increases. 

The numbers don’t lie

“Apple’s recent price increase seems to be an inevitable response to these cost increases. In the second half of the year, other PC OEMs are expected to continue to raise prices, and the overall ASP increase is expected to continue,” Counterpoint said in a post Wednesday. The researcher tells us global PC shipments shrank 4% in the second quarter of 2026 as rising costs hit demand. The Mac maker, by contrast, moved in the opposite direction, generating 13% growth in the quarter — mainly on the back of the MacBook Neo introduction. 

Recent IDC data gives Apple 10.1% year-over-year growth and just under 10% (9.9% to be exact) of the worldwide PC market, even as the overall market declined 4.9%.

“With emerging supply chain and tariff challenges inflating memory prices…, Apple’s incredibly aggressive price-point for the MacBook Neo makes its release feel all the more like a gut punch to one of the PC market’s most valuable price tiers,” Futurum Research Director Olivier Blanchard said when the Neo was released. 

Neo 2.0 is already coming

In the immediate future, as competitors raise prices on the PCs that compete with Apple’s lower-cost device, Cupertino is already plotting the path toward MacBook Neo 2. Reports claim this will debut in March in new colors and use the A19 Pro chip from the iPhone 17 Pro, with performance boosted by slightly more unified memory (12GB, rather than 8GB). That’ll make it a much better Mac, likely with 10-15% performance gains and the ability to run Apple Intelligence, making it the best and most affordable AI PC in its class.

Just four months after the Neo’s rollout, Apple is already in position to leak rumors of an even more computationally capable follow-up, while competitors struggle to compete with the original on performance, build quality, and price. Still, the Neo might get more expensive, reporting warns, with the lowest-price 256GB model now gone, making the $599 Mac a mirage we can only wistfully hope to see again. 

That might matter less in context, as PC makers everywhere boost prices while RAM, chips, and storage prices head north, along with transport, logistics, and energy costs. “While [Apple] did raise prices in line with the broader market, it still remains well positioned against rivals facing the same cost pressures,” said Jean Philippe Bouchard, vice president for consumer devices at IDC. 

“As market conditions continue to worsen, the importance of supply chain management and capabilities are increasingly important,” Bouchard said. “The largest vendors, with their buying power and long-standing supplier ties, are best positioned to take share from smaller rivals.”

This was never about luck

This isn’t solely a market take about competition, it’s about planning.

Few in the industry seemed prepared for the massive memory price increases that hit this year. Apple clearly planned its low-cost Mac well before that happened, hoping to seize the PC market at the low-mid-range. This is precisely what it seems to have done, what it continues to do, and what it will continue to do.

The recent reports that it has a successor planned shows the breadth of the Mac company’s strategic vision, as Apple has quite clearly sought to fully exploit the failings of Windows and the internal contradictions of a value-conscious industry in stiff competition with itself.

With the first M-series Macs about to enter the replacement cycle, Apple has built a market it can capitalize on for at least a decade, meaning it already has a vision for PC sales that extends at least as far. That’s the kind of road map corporate purchasers want when they make platform deployment decisions, which is why Apple’s 10% share gains are the beginning of even more significant market change. 

You can follow me on social media! Join me on BlueSky,  LinkedIn, Mastodon and subscribe to my daily Apple-related news summaries at The Core.

It’s difficult to ignore the fact that Apple seems to have turned its MacBook Neo into a weapon to promote platform growth, with enough performance under the hood to make competitors seem inferior.

And even as the PC industry moves to try to compete with Apple’s last huge Mac success, the company is already planning a powerful follow-up.

That points to the discipline Apple has applied to the Mac since the introduction of Apple Silicon. The company has built a clear product roadmap, strong entry-level pricing, and steady performance gains. This focus is now paying dividends, giving people the impetus to keep placing their trust in Apple and its Macs — even as the industry raises prices in the face of RAMageddon and price increases. 

The numbers don’t lie

“Apple’s recent price increase seems to be an inevitable response to these cost increases. In the second half of the year, other PC OEMs are expected to continue to raise prices, and the overall ASP increase is expected to continue,” Counterpoint said. The researcher tells us global PC shipments shrank 4% in the second quarter of 2026 as rising costs hit demand. The Mac maker, by contrast, moved in the opposite direction, generating 13% growth in the quarter — mainly on the back of the MacBook Neo introduction. 

Recent IDC data gives Apple 10.1% year-over-year growth and just under 10% (9.9% to be exact) of the worldwide PC market, even as the overall market declined 4.9%.

“With emerging supply chain and tariff challenges inflating memory prices…, Apple’s incredibly aggressive price-point for the MacBook Neo makes its release feel all the more like a gut punch to one of the PC market’s most valuable price tiers,” Futurum Research Director Olivier Blanchard said when the Neo was released. 

Neo 2.0 is already coming

In the immediate future, as competitors raise prices on the PCs that compete with Apple’s lower-cost device, Cupertino is already plotting the path toward MacBook Neo 2. Reports claim this will debut in March in new colors and use the A19 Pro chip from the iPhone 17 Pro, with performance boosted by slightly more unified memory (12GB, rather than 8GB). That’ll make it a much better Mac, likely with 10-15% performance gains and the ability to run Apple Intelligence, making it the best and most affordable AI PC in its class.

Just four months after the Neo’s rollout, Apple is already in position to leak rumors of an even more computationally capable follow-up, while competitors struggle to compete with the original on performance, build quality, and price. Still, the Neo might get more expensive, reporting warns, with the lowest-price 256GB model now gone, making the $599 Mac a mirage we can only wistfully hope to see again. 

That might matter less in context, as PC makers everywhere boost prices while RAM, chips, and storage prices head north, along with transport, logistics, and energy costs. “While [Apple] did raise prices in line with the broader market, it still remains well positioned against rivals facing the same cost pressures,” said Jean Philippe Bouchard, vice president for consumer devices at IDC. 

“As market conditions continue to worsen, the importance of supply chain management and capabilities are increasingly important,” Bouchard said. “The largest vendors, with their buying power and long-standing supplier ties, are best positioned to take share from smaller rivals.”

This was never about luck

This isn’t solely a market take about competition, it’s about planning.

Few in the industry seemed prepared for the massive memory price increases that hit this year. Apple clearly planned its low-cost Mac well before that happened, hoping to seize the PC market at the low-mid-range. This is precisely what it seems to have done, what it continues to do, and what it will continue to do.

The recent reports that it has a successor planned shows the breadth of the Mac company’s strategic vision, as Apple has quite clearly sought to fully exploit the failings of Windows and the internal contradictions of a value-conscious industry in stiff competition with itself.

With the first M-series Macs about to enter the replacement cycle, Apple has built a market it can capitalize on for at least a decade, meaning it already has a vision for PC sales that extends at least as far. That’s the kind of road map corporate purchasers want when they make platform deployment decisions, which is why Apple’s 10% share gains are the beginning of even more significant market change. 

You can follow me on social media! Join me on BlueSky,  LinkedIn, Mastodon and subscribe to my daily Apple-related news summaries at The Core.

Oracle’s July 2026 Critical Patch Update, its largest ever, contains 1,449 new security patches spanning 32 product families, from Oracle Database and E-Business Suite to PeopleSoft, GoldenGate, Java SE, and Fusion Middleware.

Fusion Middleware was particularly hard hit, with new security patches for 355 security vulnerabilities, 219 of them remotely exploitable without authentication, meaning they can be exploited over a network without requiring user credentials. Ten of them scored a “perfect” 10.0 on the Common Vulnerability Scoring System (CVSS).

These included easily exploitable vulnerabilities allowing unauthenticated attackers with network access via HTTP to compromise Oracle Data Integrator, Oracle Access Manager, Oracle HTTP Server, Oracle Platform Security for Java, Oracle WebCenter Content, Service Delivery Platform, or Oracle Weblogic Server Proxy Plug-in,

No other products were found to have quite such extreme vulnerabilities, but there were plenty of others scoring almost as badly.

Two critical flaws in Oracle Database Server

The most severe flaw Oracle patched in its flagship database product is CVE-2026-61211, a vulnerability in the RDBMS component’s DBMS_CLOUD package with a CVSS score of 9.9.

This easily exploitable vulnerability allows a low-privileged attacker having Execute DBMS_CLOUD privilege with network access via Oracle Net to compromise the RDBMS, Oracle said in the patch update statement. “While the vulnerability is in RDBMS, attacks may significantly impact additional products (scope change). Successful attacks of this vulnerability can result in takeover of RDBMS,” it warned.

The flaw affects Database Server versions 19.3 through 19.31 and 23.4.0 through 23.26.2.

Sanchit Vir Gogia, chief analyst at Greyhound Research, said the 9.9 score should be read as serious but conditional. Exposure depends on configuration, he said: On customer-managed databases, DBMS_CLOUD is absent until installed, and grants and network access lists determine the radius from there. “Where DBMS_CLOUD is broadly granted and reachable, the emergency is real and the window is seventy-two hours; where it is absent, the accelerated database wave will do.”

Vibhum Dubey, a cybersecurity researcher and red teamer, said the flaw stood out to him because it checks several boxes that concern defenders.

“Database servers often hold an organization’s most valuable data, so even if exploitation is not publicly observed yet, I don’t think this is the kind of issue you leave until the next routine maintenance window if your environment is exposed,” Dubey said.

A second Database Server flaw, CVE-2026-47040, affects Connection Manager in Oracle Net Services, is remotely exploitable without credentials. Oracle’s risk matrix lists six Database Product vulnerabilities in this cycle as reachable over a network with no authentication required, the statement added.

CVE-2026-7383, an OpenSSL-related TLS vulnerability, affects two products, Database Server and Autonomous Health Framework, since both bundle the same third-party component. Oracle’s advisory notes the Database Server patch for that CVE also resolves 19 related OpenSSL CVEs bundled into the same fix.

Oracle GoldenGate received 27 new patches, nine of which do not require authentication to exploit, including CVE-2026-2332, a flaw in the Big Data and Application Adapters component tied to Eclipse Jetty, the statement added.

There were also two critical flaws in Oracle’s TimesTen in-memory database.

The remainder of the release spans E-Business Suite, WebLogic Server, PeopleSoft, Siebel, JD Edwards, Communications, Retail Applications, Utilities Applications, MySQL, Solaris and VM VirtualBox.

Volume repair

Gogia said the volume itself marks a shift.

“At 1,449 patches, against 481 in April 2026 and 309 a year earlier, patch load has outgrown the queue built to hold it,” he said. He recommended a tiered response: “The reachable and the reported inside seventy-two hours, the trusted core inside ten days, the rest by risk before the October release.”

He also flagged a specific risk in how organizations might triage E-Business Suite. “Oracle’s advisory concedes that E-Business Suite exposure sits partly in underlying Database and Fusion Middleware versions outside the E-Business Suite matrix. The fastest way to mis-prioritise this release is to patch by product logo instead of trust boundary.”

Third Tuesday, quarterly cycle

The July release is the third quarterly Critical Patch Update of 2026, and the first since the introduction in May of the monthly Critical Security Patch Update program.

Gogia said Oracle has effectively layered a second cadence on top of the existing one rather than replacing it.

“Quarterly Critical Patch Updates remain and stay cumulative; monthly Critical Security Patch Updates now sit on top,” he said, adding that enterprise adoption of the new rhythm remains low because of “certification obligations, regression exposure and scarce specialist hours.”

Dubey made a similar point about organizational readiness: “In large enterprises, patching is rarely a technical problem. It is an operational one. Database administrators, application owners, infrastructure teams, business stakeholders, and change advisory boards all have to align.”

Niyati Daftary, principal analyst at Gartner, said the release underscores a broader shift in how patching is approached.

“Patching is no longer a race to remediate every vulnerability. It is a discipline of identifying the exposures that matter most and reducing business risk as efficiently as possible,” she said, adding that organizations should prioritize based on exposure, business impact and exploitability, starting with internet-facing assets and mission-critical systems.

Daftary pointed to continuous threat exposure management and adversarial exposure validation as increasingly relevant frameworks, since CVSS scores “measure theoretical severity rather than actual enterprise risk.” Patching alone will not be sufficient, Daftary said, and organizations should continue investing in defense in depth, including behavioral threat detection and incident response.

Oracle’s next cumulative Critical Patch Update will come on Oct. 20, 2026, with smaller Critical Security Patch Updates on Aug. 18 and Sept. 15.

This article first appeared on CSO.

Oracle’s July 2026 Critical Patch Update, its largest ever, contains 1,449 new security patches spanning 32 product families, from Oracle Database and E-Business Suite to PeopleSoft, GoldenGate, Java SE, and Fusion Middleware.

Fusion Middleware was particularly hard hit, with new security patches for 355 security vulnerabilities, 219 of them remotely exploitable without authentication, meaning they can be exploited over a network without requiring user credentials. Ten of them scored a “perfect” 10.0 on the Common Vulnerability Scoring System (CVSS).

These included easily exploitable vulnerabilities allowing unauthenticated attackers with network access via HTTP to compromise Oracle Data Integrator, Oracle Access Manager, Oracle HTTP Server, Oracle Platform Security for Java, Oracle WebCenter Content, Service Delivery Platform, or Oracle Weblogic Server Proxy Plug-in,

No other products were found to have quite such extreme vulnerabilities, but there were plenty of others scoring almost as badly.

Two critical flaws in Oracle Database Server

The most severe flaw Oracle patched in its flagship database product is CVE-2026-61211, a vulnerability in the RDBMS component’s DBMS_CLOUD package with a CVSS score of 9.9.

This easily exploitable vulnerability allows a low-privileged attacker having Execute DBMS_CLOUD privilege with network access via Oracle Net to compromise the RDBMS, Oracle said in the patch update statement. “While the vulnerability is in RDBMS, attacks may significantly impact additional products (scope change). Successful attacks of this vulnerability can result in takeover of RDBMS,” it warned.

The flaw affects Database Server versions 19.3 through 19.31 and 23.4.0 through 23.26.2.

Sanchit Vir Gogia, chief analyst at Greyhound Research, said the 9.9 score should be read as serious but conditional. Exposure depends on configuration, he said: On customer-managed databases, DBMS_CLOUD is absent until installed, and grants and network access lists determine the radius from there. “Where DBMS_CLOUD is broadly granted and reachable, the emergency is real and the window is seventy-two hours; where it is absent, the accelerated database wave will do.”

Vibhum Dubey, a cybersecurity researcher and red teamer, said the flaw stood out to him because it checks several boxes that concern defenders.

“Database servers often hold an organization’s most valuable data, so even if exploitation is not publicly observed yet, I don’t think this is the kind of issue you leave until the next routine maintenance window if your environment is exposed,” Dubey said.

A second Database Server flaw, CVE-2026-47040, affects Connection Manager in Oracle Net Services, is remotely exploitable without credentials. Oracle’s risk matrix lists six Database Product vulnerabilities in this cycle as reachable over a network with no authentication required, the statement added.

CVE-2026-7383, an OpenSSL-related TLS vulnerability, affects two products, Database Server and Autonomous Health Framework, since both bundle the same third-party component. Oracle’s advisory notes the Database Server patch for that CVE also resolves 19 related OpenSSL CVEs bundled into the same fix.

Oracle GoldenGate received 27 new patches, nine of which do not require authentication to exploit, including CVE-2026-2332, a flaw in the Big Data and Application Adapters component tied to Eclipse Jetty, the statement added.

There were also two critical flaws in Oracle’s TimesTen in-memory database.

The remainder of the release spans E-Business Suite, WebLogic Server, PeopleSoft, Siebel, JD Edwards, Communications, Retail Applications, Utilities Applications, MySQL, Solaris and VM VirtualBox.

Volume repair

Gogia said the volume itself marks a shift.

“At 1,449 patches, against 481 in April 2026 and 309 a year earlier, patch load has outgrown the queue built to hold it,” he said. He recommended a tiered response: “The reachable and the reported inside seventy-two hours, the trusted core inside ten days, the rest by risk before the October release.”

He also flagged a specific risk in how organizations might triage E-Business Suite. “Oracle’s advisory concedes that E-Business Suite exposure sits partly in underlying Database and Fusion Middleware versions outside the E-Business Suite matrix. The fastest way to mis-prioritise this release is to patch by product logo instead of trust boundary.”

Third Tuesday, quarterly cycle

The July release is the third quarterly Critical Patch Update of 2026, and the first since the introduction in May of the monthly Critical Security Patch Update program.

Gogia said Oracle has effectively layered a second cadence on top of the existing one rather than replacing it.

“Quarterly Critical Patch Updates remain and stay cumulative; monthly Critical Security Patch Updates now sit on top,” he said, adding that enterprise adoption of the new rhythm remains low because of “certification obligations, regression exposure and scarce specialist hours.”

Dubey made a similar point about organizational readiness: “In large enterprises, patching is rarely a technical problem. It is an operational one. Database administrators, application owners, infrastructure teams, business stakeholders, and change advisory boards all have to align.”

Niyati Daftary, principal analyst at Gartner, said the release underscores a broader shift in how patching is approached.

“Patching is no longer a race to remediate every vulnerability. It is a discipline of identifying the exposures that matter most and reducing business risk as efficiently as possible,” she said, adding that organizations should prioritize based on exposure, business impact and exploitability, starting with internet-facing assets and mission-critical systems.

Daftary pointed to continuous threat exposure management and adversarial exposure validation as increasingly relevant frameworks, since CVSS scores “measure theoretical severity rather than actual enterprise risk.” Patching alone will not be sufficient, Daftary said, and organizations should continue investing in defense in depth, including behavioral threat detection and incident response.

Oracle’s next cumulative Critical Patch Update will come on Oct. 20, 2026, with smaller Critical Security Patch Updates on Aug. 18 and Sept. 15.

This article first appeared on CSO.

Oracle’s July 2026 Critical Patch Update, its largest ever, contains 1,449 new security patches spanning 32 product families, from Oracle Database and E-Business Suite to PeopleSoft, GoldenGate, Java SE, and Fusion Middleware.

Fusion Middleware was particularly hard hit, with new security patches for 355 security vulnerabilities, 219 of them remotely exploitable without authentication, meaning they can be exploited over a network without requiring user credentials. Ten of them scored a “perfect” 10.0 on the Common Vulnerability Scoring System (CVSS).

These included easily exploitable vulnerabilities allowing unauthenticated attackers with network access via HTTP to compromise Oracle Data Integrator, Oracle Access Manager, Oracle HTTP Server, Oracle Platform Security for Java, Oracle WebCenter Content, Service Delivery Platform, or Oracle Weblogic Server Proxy Plug-in,

No other products were found to have quite such extreme vulnerabilities, but there were plenty of others scoring almost as badly.

Two critical flaws in Oracle Database Server

The most severe flaw Oracle patched in its flagship database product is CVE-2026-61211, a vulnerability in the RDBMS component’s DBMS_CLOUD package with a CVSS score of 9.9.

This easily exploitable vulnerability allows a low-privileged attacker having Execute DBMS_CLOUD privilege with network access via Oracle Net to compromise the RDBMS, Oracle said in the patch update statement. “While the vulnerability is in RDBMS, attacks may significantly impact additional products (scope change). Successful attacks of this vulnerability can result in takeover of RDBMS,” it warned.

The flaw affects Database Server versions 19.3 through 19.31 and 23.4.0 through 23.26.2.

Sanchit Vir Gogia, chief analyst at Greyhound Research, said the 9.9 score should be read as serious but conditional. Exposure depends on configuration, he said: On customer-managed databases, DBMS_CLOUD is absent until installed, and grants and network access lists determine the radius from there. “Where DBMS_CLOUD is broadly granted and reachable, the emergency is real and the window is seventy-two hours; where it is absent, the accelerated database wave will do.”

Vibhum Dubey, a cybersecurity researcher and red teamer, said the flaw stood out to him because it checks several boxes that concern defenders.

“Database servers often hold an organization’s most valuable data, so even if exploitation is not publicly observed yet, I don’t think this is the kind of issue you leave until the next routine maintenance window if your environment is exposed,” Dubey said.

A second Database Server flaw, CVE-2026-47040, affects Connection Manager in Oracle Net Services, is remotely exploitable without credentials. Oracle’s risk matrix lists six Database Product vulnerabilities in this cycle as reachable over a network with no authentication required, the statement added.

CVE-2026-7383, an OpenSSL-related TLS vulnerability, affects two products, Database Server and Autonomous Health Framework, since both bundle the same third-party component. Oracle’s advisory notes the Database Server patch for that CVE also resolves 19 related OpenSSL CVEs bundled into the same fix.

Oracle GoldenGate received 27 new patches, nine of which do not require authentication to exploit, including CVE-2026-2332, a flaw in the Big Data and Application Adapters component tied to Eclipse Jetty, the statement added.

There were also two critical flaws in Oracle’s TimesTen in-memory database.

The remainder of the release spans E-Business Suite, WebLogic Server, PeopleSoft, Siebel, JD Edwards, Communications, Retail Applications, Utilities Applications, MySQL, Solaris and VM VirtualBox.

Volume repair

Gogia said the volume itself marks a shift.

“At 1,449 patches, against 481 in April 2026 and 309 a year earlier, patch load has outgrown the queue built to hold it,” he said. He recommended a tiered response: “The reachable and the reported inside seventy-two hours, the trusted core inside ten days, the rest by risk before the October release.”

He also flagged a specific risk in how organizations might triage E-Business Suite. “Oracle’s advisory concedes that E-Business Suite exposure sits partly in underlying Database and Fusion Middleware versions outside the E-Business Suite matrix. The fastest way to mis-prioritise this release is to patch by product logo instead of trust boundary.”

Third Tuesday, quarterly cycle

The July release is the third quarterly Critical Patch Update of 2026, and the first since the introduction in May of the monthly Critical Security Patch Update program.

Gogia said Oracle has effectively layered a second cadence on top of the existing one rather than replacing it.

“Quarterly Critical Patch Updates remain and stay cumulative; monthly Critical Security Patch Updates now sit on top,” he said, adding that enterprise adoption of the new rhythm remains low because of “certification obligations, regression exposure and scarce specialist hours.”

Dubey made a similar point about organizational readiness: “In large enterprises, patching is rarely a technical problem. It is an operational one. Database administrators, application owners, infrastructure teams, business stakeholders, and change advisory boards all have to align.”

Niyati Daftary, principal analyst at Gartner, said the release underscores a broader shift in how patching is approached.

“Patching is no longer a race to remediate every vulnerability. It is a discipline of identifying the exposures that matter most and reducing business risk as efficiently as possible,” she said, adding that organizations should prioritize based on exposure, business impact and exploitability, starting with internet-facing assets and mission-critical systems.

Daftary pointed to continuous threat exposure management and adversarial exposure validation as increasingly relevant frameworks, since CVSS scores “measure theoretical severity rather than actual enterprise risk.” Patching alone will not be sufficient, Daftary said, and organizations should continue investing in defense in depth, including behavioral threat detection and incident response.

Oracle’s next cumulative Critical Patch Update will come on Oct. 20, 2026, with smaller Critical Security Patch Updates on Aug. 18 and Sept. 15.

Just in time for the iPhone’s 20th anniversary, Apple is moving closer to becoming a service company. It is set to launch its new deal with Klarna next week and when it does, Apple enthusiasts in the US will effectively be able to subscribe to their favorite Apple hardware, with the cost spread across up to three years.

This matters because when combined with Apple One and Apple’s Creator Studio subscriptions, the Klarna arrangement brings Apple closer to offering a full subscription model for hardware, software, and services. The only thing you don’t get under the new arrangement is AppleCare, for which you’ll allegedly need to pay extra.

Moving closer to hardware-as-a-service

Apple has slowly been transitioning toward hardware-as-a-service for almost a decade. Back then, Forrester analyst Frank Gillet predicted the company would eventually offer bundles of services and products for a monthly, all-in, fee. 

This isn’t quite where we are yet; you still need at least three subscriptions to get close. But, after the better part of a decade, Apple has moved much nearer to the hardware-as-a-service idea.

There are some products reportedly excluded from the arrangement, including MacBook Neo, Apple Watch SE, the entry-level iPad, and iPhone 16. Clearly, Apple sees those products as sufficiently affordable. 

Easy payments for RAM-ageddon

The new Klarna arrangement comes as Apple is forced to increase product prices as AI-driven memory price inflation becomes widely felt across every economy. In theory, I assume, Apple hopes to make its products available to cash-strapped consumers who need new hardware, while also navigating a time of deep economic tumult and uncertainty. It’s thought the company has previously rejected these plans to protect normal hardware sales, but normality is a kingdom we no longer seem to possess. Interesting times. Probable inflation incoming.

“Apple Upgrade lands at precisely the moment Apple needs it,” IDC analyst Francisco Jeronimo wrote in a note seen by Computerworld. “Having just pushed Mac and iPad prices up on the back of the memory shortage, with iPhone increases widely expected in September — as well as the new iPhone foldable expected at $2,500 — Apple’s real risk is that rising prices even further can impact the upgrade cycle.” 

New age, new shopping habits

The introduction of the scheme gives consumers a way to purchase the company’s popular high-end devices when they are introduced — no doubt,at higher cost — this fall. Plus, of course, if it’s good enough for GM, it’s good enough for Apple.

It’s all about attitude, too. From Apple’s perspective, it has done plenty of the groundwork required to convince its customers that subscription payments for things you value are no bad thing. 

Reluctance to embrace “Access Not Ownership’”purchasing models has dropped dramatically since Apple — and CEO Tim Cook — first began banging the drum for services income. Apple’s services stream has now become its second-biggest revenue driver after the iPhone. It has over 1 billion paid subscriptions, and an active hardware installed base of more than 2.5 billion devices globally.

A combination of changed customer habits and external threat means the stars are now aligned for hardware-as-a-service models. “Reframing a device as a low monthly payment protects that [upgrade] cadence and allows Apple to start marketing their products as device-as-a-service to consumers, which no other vendor was ever able to do,” Jeronimo wrote to me. 

There is a one-more-thing aspect to this: the products are effectively being leased, a new approach that will give Apple a stronger grip on EOL devices, helping it grab more of them for refurbishment, resale, and recycling. Over time, this will give the company a much stronger grip on the lucrative second-user market that exists around Apple equipment, even while for almost every consumer product we find the life we want is something we can rent, but probably can’t afford to own.

Managing future risk

The other solid reason to take a partnership approach is risk management. Apple had intended to develop its own buy-now, pay-later scheme via Apple Pay Later, but abandoned that plan as it became riskier with rising bank rates. “Also, by backing the program with Klarna rather than reviving the in-house subscription plan it shelved in 2024, Apple captures the demand upside without taking the credit risk onto its own balance sheet,” Jeronimo said.

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As Apple gears up to anoint John Ternus the new company CEO in September (while current leader Tim Cook takes a seat on the board) the company appears to be firing on all cylinders ahead of the leadership transition. 

What’s going well

Just look at the evidence: 

• Apple is building market share across its entire product range; even memory-driven price inflation doesn’t seem to have dampened demand for its hardware yet.
• While Apple had to raise prices, the company’s MacBook Neo remains seriously popular. It’s sitting atop Amazon’s US best-selling chart, which currently includes six Macs in the top 10. The Neo has topped this chart since its introduction.
• Apple’s iPhone 17 series continues to sell well, with recent market data showing sustained growth. Both Counterpoint and IDC tell us that iPhone shipments continue to increase, even as other vendor shipments slide.
• IDC analyst Francisco Jeronimo recently estimated that Apple’s upcoming foldable iPhone Ultra could grab 29.4% of global folding smartphone sales this year, rising to 34.9% in 2027.
• The company’s new 27 series of operating systems is attracting a great response as beta testers report that it is already solid, stable, and performing well.
• The AI narrative has really changed, with analysts no longer quite so starry-eyed at the prospects for the big frontier AI firms. Apple’s edge-AI-enabling approach is winning converts.

What’s coming up

The company’s newly-filed lawsuit against OpenAI may or may not succeed, but it will certainly help consolidate recognition of the importance of Apple’s designs and intellectual property in whatever hardware emerges from the AI firm. It also means both Apple and OpenAI are already competing in hardware, even though neither company yet offers anything that directly challenges the other. 

Apple has just set out its stall to brand-loyal fans in a big way and did so before OpenAI gets to woo the same set of customers with a wriggle of its Jony Ive-tinged talisman.

The stage is set for intense competition between the two. Though some say Apple’s needs to improve employee retention, if it does find proof of efforts to use recruitment to engage in industrial espionage, it’ll be easier to represent its own products as being the OG for new hardware. 

If nothing else, it means consumers will forever be asking, “If OpenAI’s designers are so good, why did it need to poach them from Apple?” Doubt is a weapon.

Managing perception

It doesn’t matter how the case goes, because there fight is already affecting consumer psychology. It also means that as Ternus prepares to take his seat atop the rainbow-colored Apple throne, we can already size him up. “A man is measured by his enemies,” Joe Abercrombie wrote in “The Trouble With Peace.”

Given the proximity of the leadership transition, it’s highly probable that Ternus signed-off on the litigation; in doing so he — and Apple — tell us to expect more of the same. 

Apple has, rightly or wrongly, decided that OpenAI will become its new existential bugbear, following in the footsteps of Microsoft Windows, Real Networks, Adobe Flash, Android, and Samsung, all of whom have been useful foils against which Apple has been able to build and maintain its identity.

Looking at that list, you’d be tempted to believe that nothing much is new. Apple has often defined itself by the enemies it sometimes keeps. What has been will be again, which in this case means even as OpenAI attempts to carve out an identity as a hardware manufacturer delivering solutions to compete with Apple and Google, Ternus’ team’s looks to drive a consensus-shaped wedge into the pro-LLM propaganda. 

That blow comes as Apple finally gets its act together around AI, and as the company prepares for a future in which the world’s most-used wearable device also becomes the wearable way to woo Siri AI.

My kingdom come

Rising market share, powerful solutions, an increasingly recognized and respected approach to AI, and an ideological crusade — these details constitute Apple’s place today and are Tim Cook’s coronation gift to Ternus. He’s passing along a strong and hyper-profitable baton that screams of timeliness and relevance even as the company gets set, ready, to go with a year or two of new product designs, new product families, and a 20^thanniversary iPhone.

This is Apple’s party. OpenAI’s name didn’t make the list. 

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

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

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

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

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

AIops and agentic ops aid SREs

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

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

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

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

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

AI’s impact on people and burnout

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

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

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

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

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

AI agent reliability

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

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

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

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

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

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

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

AI raises the SRE’s business impact

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

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

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

Microsoft addressed 722 CVEs this month once the 427 Chromium upstream relays are set aside — roughly three times a normal cycle and one of the largest single months in recent memory. Two vulnerabilities arrive under active exploitation: an elevation of privilege in Active Directory Federation Services (CVE-2026-56155), and an elevation of privilege in SharePoint Server (CVE-2026-56164). A third, a BitLocker security feature bypass (CVE-2026-50661) is publicly disclosed but not yet exploited.

The July 2026 Patch Tuesday earns Patch Now recommendations for Windows, Office, Exchange, and SQL Server. SharePoint has two critical RCEs on top of its exploited zero-day, and Exchange Server returns with a critical on-premises spoofing flaw. Adding to our (dear) administrator’s efforts, SharePoint Server 2016/2019 and SQL Server 2016 all reach end of support today. The Readiness team has provided a handy infographic of the expected risk profile of this month’s Patch Tuesday updates.

Known issues

The July release note flags known issues against the following updates:

• BitLocker recovery prompt on first restart – the PCR7 recovery condition tracked since April remains live on the platforms that did not receive the Boot Manager servicing fix (Windows Server 2022 and Windows 10 22H2). Devices with BitLocker on the OS drive, the Group Policy “Configure TPM platform validation profile for native UEFI firmware configurations” set with PCR7 included, and Secure Boot State PCR7 Binding reported as “Not Possible” may be prompted for the recovery key on the first restart after installing this update. This month’s publicly disclosed BitLocker security feature bypass (CVE-2026-50661) keeps the component in focus.

• WSUS synchronization error details suppressed (Windows Server 2025 and 2022) – WSUS no longer displays synchronization error details in its error reporting, a deliberate change made to address the Remote Code Execution Vulnerability CVE-2025-59287. Sync still works, but administrators triaging a failed synchronization lose the detail pane and must fall back to the SoftwareDistribution logs.

Windows Update can still replace manually installed graphics drivers with older OEM versions from the catalogue (the four-part Hardware ID ranking issue acknowledged on the Hardware Dev Center). The two-part HWID pilot runs to September 2026.

Major revisions and mitigations

Between the June and July Patch Tuesdays, MSRC Security Update Guide notices updated 651 reported CVEs across six notification dates (15, 19, 26 June and 3, 8, 11 July), 532 of them routine Chromium upstream re-publications. Of the roughly 30 Microsoft revisions, almost all were cross-platform Office catch-up with no bearing on a Windows enterprise estate. No further action required for IT administrators for this Windows update cycle.

Windows lifecycle and enforcement updates

This is the deadline cycle June pointed at. The July end-of-support wave lands today, and it collides with the month’s heaviest patching. SharePoint and SQL Server take some of their most active security updates ever on platforms receiving their last.

• SharePoint Server 2016 and 2019, Project Server 2016 and 2019, SQL Server 2016 and InfoPath 2013 have all reached end of support. SQL Server 2014 ESU Year 2 reaches end of support today. SharePoint 2016/2019 take an actively exploited zero-day and two RCEs this cycle, and SQL Server 2016 takes a critical RCE, all as their final security update. Now is the time to get moving on updating these platforms.

The 2011 Secure Boot certificate expiries have now passed; devices that never took the Windows UEFI CA 2023 key updates under CVE-2023-24932 can no longer receive updated boot components, with the Windows Production PCA for the boot manager still ahead on 19 October 2026. Kerberos RC4 hardening (CVE-2026-20833) has been in enforcement since April 2026; the July 2026 update removes the RC4DefaultDisablementPhase rollback control that let administrators defer it, making enforcement final.

Microsoft’s July 2026 Patch Tuesday is a security-only release: 180 test-guidance entries, 14 of them high risk (June had one). Printing and graphics are the centre of gravity: win32kfull.sys, the kernel-mode window manager, is the most-patched binary (14 entries), and seven high-risk flags sit alongside it – the Print Spooler, four win32k entries, and two GDI+ metafile entries. NTFS is the second theme, with 10 entries, two high risk. Every entry reports no functional changes – it’s pure regression validation. The packages span Windows 11 26H1 back to Server 2012 ESU.

Printing and graphics (high risk)

The Print Spooler flag centres on shared printers, whose queue status must track jobs accurately; the win32k flags cover 32-bit application printing, font rendering in printed and exported output, on-screen rendering, and window management; the GDI+ flags cover metafiles.

• Share a printer from a print server, print from a separate client in varied sizes and formats, and cancel a job, confirming the queue reflects every state change
• Print from your 32-bit applications, and print text-heavy, graphics-heavy, and multi-page documents to physical and virtual (PDF or XPS) printers, repeating after orientation, scaling, and resolution changes
• Export documents with varied fonts to PDF and confirm fonts and layout survive; render EMF+ files that apply effects to very large images, and convert EMF files to WMF
• Open and close windows rapidly, drive common dialogs by mouse and keyboard, and close parents with children open – no orphaned windows

Storage and file systems (high risk)

Both NTFS high-risk flags target integrity – extended attributes, and volume recovery after an unexpected shutdown. File History carries its own high-risk flag on clients. A Windows Server 2025-only bundle across boot, BitLocker, and ReFS demands the full Secure Boot/BitLocker matrix. Eight entries hit Server 2025 alone, including WSL, GPU partitioning, and a scripted Windows Server Backup pass repeating recovery after rolling the date 90 days forward.

• Exercise NTFS extended attributes – older-system EAs, backup workflows that preserve them, concurrent same-file operations where supported – with antivirus, encryption, or storage filters active
• Simulate an unexpected shutdown during file activity, verify the volume mounts intact, run chkdsk, and confirm indexing, shadow copies, and backup still work
• Run a full File History pass: back up, modify and back up again, exclude folders, change frequency, move the destination
• On Server 2025, boot all four Secure Boot/BitLocker combinations, in standard and confidential VMs where supported

Devices, input and networking (high risk)

Three further high-risk flags land here: HID input (hidparse.sys with win32k) – touch, keyboard, mouse, touchpad, through disconnects and restarts; the WinSock bundle (afd.sys plus Bluetooth and multicast drivers); and IrDA. The heaviest ask is not high risk at all: the NetAdapterCx driver (24H2/25H2, Server 2025) wants 500-plus adapter enable-disable cycles under Driver Verifier.

• Run the connectivity suite: browsing, large downloads, mapped drives, an RDP session idle 30+ minutes, a Teams call, an hour of streaming, and localhost apps such as Docker or WSL
• Stress Bluetooth: pairing, 10+ minutes of audio, input after idle, and reconnection after sleep
• Where infrared hardware exists, transfer a file and run at least 100 connect-disconnect cycles
• Sweep the rest: DNS Server (zone data must stay under its configured database directory), the client resolver (five entries), DHCP Server (five entries), SMB, NFS, Message Queuing (five entries), RRAS administration, client VPN, and WinHTTP/WinINet consumers

Other windows components

Windows Installer itself is patched: testing should include application install, uninstall, repair, and force a rollback. Hyper-V wants virtual-switch traffic as part of its testing exercises with Virtual Filtering Platform policies enforced. Sixteen media-related security entries cover playback, HEVC and MPEG-TS, USB audio, and MIDI 2.0.

Shell hardening and LSA isolation

These two entries are a little different from the rest of the cycle: they ask you to confirm a security behaviour actively works, not just that nothing regressed. A pass here means the protection fired, so treat them as functional checks rather than box-ticking.

• Shortcut handling (windows.storage.dll; Windows 11 23H2 and earlier, plus Server 2022): drop a shortcut file carrying the Mark of the Web into a folder and confirm the system refuses to extract its icon and leaks no NTLM credential hash – include the zero-click paths, where the icon would otherwise render without you opening anything
• LSA isolation and KeyGuard (24H2/25H2, Server 2025): run the supplied PowerShell validation script, which turns on Virtualization-based Security if it isn’t already, exercises KeyGuard key operations in both required and best-effort isolation modes, and reports pass or fail – it needs TPM 2.0, UEFI with Secure Boot disabled, and PowerShell 7
• Run that script on a dedicated test machine, never a shared one: it enables test signing, disables automatic updates, and reboots without asking

Office & SharePoint

July’s Office wave is security-only; everything landed on 14 July, and nothing critical or non-security shipped in the 7 July preview. It’s an MSI-only cycle, so Click-to-Run estates can sit this one out.

• On MSI Office 2016, apply the client updates – Excel (KB5002886), Word (KB5002890), PowerPoint (KB5002867), and five further Office 2016 security updates (KB5002273, KB5002887, KB5002748, KB5002857, KB5002830) – then exercise macros, external data, embedded objects, and any line-of-business add-ins
• On SharePoint Server, patch 2016 (KB5002891, plus the KB5002892 language pack) and Subscription Edition (KB5002882), then check browser-based editing; the guidance lists SharePoint 2019 with a baseline but ships no 2019 package, so there is nothing to install there

Mind the rollback rules before you schedule the window: most client updates can be uninstalled, but the server updates cannot and always require a reboot.

Developer tools & databases

The developer estate gets a broad but low-drama sweep this month. Both .NET and SQL Server patch widely, but the ask is representative-application validation rather than anything exotic – install on the matching branch and confirm normal behaviour.

• .NET: install the SDK updates (8.0.423, 9.0.316, 10.0.302, x64 and x86) and the Framework rollups spanning 3.5 through 4.8.1 – which reach from Windows Server 2012 up to Windows 11 26H1 and Server 2025 – then run a representative set of applications and confirm they function normally
• SQL Server: the GDR updates span 2016 SP3 through 2025 – install each on its matching branch and test that each removes cleanly
• Check an encrypted client connection through the separately patched Windows SQL client (dbnetlib.dll), which ships outside the server branches

The Readiness team recommends the following priorities for your larger enterprise deployments:

• Start with printing and graphics: half the high-risk flags sit in the Print Spooler, win32k, and GDI+, so regress shared printers, 32-bit printing, PDF export, metafiles, and window management before anything else
• Take NTFS next – extended attributes and crash recovery both touch data integrity – and add a client File History backup-and-restore pass
• Give Server 2025 its wider matrix – the Secure Boot/BitLocker combinations, WSL, GPU partitioning, and the scripted backup pass – and work through the stress suites
• Run the scripted KeyGuard validation on any VBS estate, preferably on a dedicated machine.

Each month, we break down the update cycle into product families (as defined by Microsoft) with the following basic groupings:

• Browsers (Microsoft IE and Edge)
• Microsoft Windows (both desktop and server)
• Microsoft Office
• Microsoft Exchange and SQL Server
• Microsoft Developer Tools (Visual Studio and .NET)
• Adobe (if you get this far)

Browsers

Edge has had a busier month than usual. Microsoft addressed 46 Microsoft Edge (Chromium-based) CVEs this cycle. None critical, but heavily weighted to remote code execution (21 entries) and spoofing (13), led by CVE-2026-58289, a remote code execution flaw. A run of further RCEs (CVE-2026-57981, CVE-2026-56645, CVE-2026-57974) follows.

• Microsoft Edge – the Edge-specific fixes ship in the Edge stable channel (version 150.0.4078.65, released 9 July). The concentration of RCE and spoofing this month is worth a look for managed Edge estates rather than a routine wave-through.
• Chromium upstream – 427 CVEs relayed through MSRC this cycle, spanning the weekly Chrome release cadence since the June report: use-after-free, out-of-bounds read/write, type confusion, and inappropriate-implementation flaws across V8, Dawn, ANGLE, Skia, and Tint. The same fixes ship in the Chrome Stable channel; see the Chrome releases blog for the upstream notes.

The Chromium volume looks (quite) alarming but is routine plumbing: it flows to Edge through its own auto-update channel. Add these browser (Edge) updates to your standard release schedule for your managed environments.

Microsoft Windows

Windows carries the bulk of this month’s updates: 406 CVEs, 31 rated critical and 374 important. Elevation of privilege dominates by volume (226 entries), followed by remote code execution (70), information disclosure (70), denial of service (23), and a scatter of security-feature-bypass, tampering, and spoofing entries across the following feature groupings:

• DHCP – the standout network cluster: DHCP Server remote code execution (CVE-2026-50518, “Exploitation More Likely,” and CVE-2026-56159), with further critical DHCP Server and DHCP Client RCEs behind them (CVE-2026-48564, CVE-2026-50370, CVE-2026-54128). DHCP servers are the deployment priority.
• VMSwitch and Hyper-V – the Windows VMSwitch elevation of privilege (CVE-2026-57092) is one of the month’s highest-severity flaws, joined by two critical Hyper-V elevation-of-privilege entries (CVE-2026-50680, CVE-2026-54127), guest-to-host risk on virtualisation hosts.
• Network stack RCE – a Windows Server Network driver RCE (CVE-2026-56188, “Exploitation More Likely”), plus TCP/IP (CVE-2026-54999), the Reliable Multicast Transport Driver (CVE-2026-54982), and SSTP (CVE-2026-50694).
• Graphics – Windows GDI+ remote code execution (CVE-2026-50380) and a DirectX Graphics Kernel RCE (CVE-2026-50382), both reachable through document-rendering paths.
• Windows Media – a large cluster: three critical Media Foundation RCEs (CVE-2026-57090, CVE-2026-57094, CVE-2026-57087) lead 14 Windows Media and seven Media Foundation entries overall.
• Identity infrastructure – beyond the exploited ADFS flaw, Active Directory Domain Services takes a critical RCE (CVE-2026-49164) and Active Directory Certificate Services a critical elevation of privilege (CVE-2026-54121). Domain controllers take priority again.
• Print Spooler, WSUS, and MSMQ – critical RCE/EoP in the Print Spooler (CVE-2026-58608), Windows Server Update Services (CVE-2026-50444), and Message Queuing (CVE-2026-54992, “Exploitation More Likely”), all server-role attack surface.

The Windows Kernel is the most-patched component (28 CVEs, seven “More Likely”), followed by NTFS (21), Windows Runtime (17), Windows Media (14), ReFS (12), and Win32k (15 across its two entries). Add this Windows update to your Patch Now deployment schedule.

Microsoft Office

Microsoft released 96 Office CVEs this month: 19 critical, 76 important. Remote code execution leads (53 entries), ahead of information disclosure (27) and spoofing (10). SharePoint is the centre of gravity: it touches 39 of the 96 CVEs and supplies the family’s one actively exploited flaw.

• SharePoint Server: has been exploited (who would have guessed) and reaches end of support today. CVE-2026-56164, an elevation of privilege, is under active exploitation. Above it sit two critical remote code execution flaws, both “Exploitation More Likely” (CVE-2026-50522, CVE-2026-58644) and a critical security feature bypass (CVE-2026-55040). SharePoint Server 2016 and 2019 reach end of support on 14 July, so this exploited, critical-heavy set is the final security update those on-premises farms will receive.
• Office has experienced a long run of critical remote code execution entries across Office, Word, and PowerPoint (among them CVE-2026-55033 and CVE-2026-55127 in Word, CVE-2026-55043 in PowerPoint, and CVE-2026-55018 in Office), topped by CVE-2026-55045.

With an exploited zero-day, two RCEs, and an end-of-support deadline all landing on SharePoint in the same cycle, SharePoint environments are the priority. Add the July Office and SharePoint updates to your Patch Now schedule.

Microsoft Exchange and SQL Server

Both Exchange and SQL Server carry critical-rated security vulnerabilities this month. Exchange Server returns with an on-premises security update for Exchange Server Subscription Edition, the only on-premises release still supported after Exchange Server 2016 and 2019 reached end of support in October 2025; SQL Server takes two critical remote code execution flaws, one of them against SQL Server 2016, which reaches end of support on the same day.

• Exchange Server (on-premises) – CVE-2026-55008, a spoofing vulnerability rated critical and “Exploitation More Likely,” is the headline. Behind it, a remote code execution entry (CVE-2026-55005) and two elevation-of-privilege flaws (CVE-2026-55006, CVE-2026-55009) round out the on-premises set. A separate Exchange Online elevation of privilege (CVE-2026-54998, critical) is fixed service-side with no customer action.
• SQL Server – two critical remote code execution flaws: CVE-2026-54117 (SQL Server 2025) and CVE-2026-54118 (which reaches back to SQL Server 2016 SP3), with five further important elevation-of-privilege and information-disclosure entries behind them. The 2016 exposure matters because SQL Server 2016 reaches end of support on 14 July: a critical RCE on a platform taking its final update.

Both belong on the Patch Now schedule this month: the Exchange on-premises update for its critical spoofing flaw, and the SQL Server update for the two critical RCEs.

Microsoft developer tools

Microsoft released 24 CVEs across its developer tooling this month, all rated important. The weighting shifts from last month’s Visual Studio Code concentration toward .NET and ASP.NET Core, where a run of denial-of-service entries dominates the volume:

• ASP.NET Core and .NET – the two highest-severity entries are ASP.NET Core elevation-of-privilege entries (CVE-2026-47300, CVE-2026-47303), ahead of a .NET security feature bypass (CVE-2026-50528) and two .NET / .NET Framework remote code execution flaws (CVE-2026-50646, CVE-2026-50649).
• Visual Studio and VS Code – a GitHub Copilot / Visual Studio Code security feature bypass (CVE-2026-41109) and a second VS Code security feature bypass (CVE-2026-57102) lead here, with a VS Code remote code execution entry behind them (CVE-2026-50520) and a Visual Studio RCE (CVE-2026-47305).

Add these Microsoft updates to your standard developer update release schedule.

Adobe (and third-party updates)

Outside Microsoft’s own catalogue, July is quiet. Adobe issued no Acrobat or Reader security updates. So, the month belongs to Microsoft, and it is a heavy one: 722 CVEs, roughly three times a normal cycle and one of the largest on record. Worth noting that this lands in the same season Microsoft has been talking up AI-assisted vulnerability management, and the AI stack it is selling as the answer, Copilot and Azure OpenAI among them, sits in the centre of this patch cycle’s own critical-rated updates. The (AI) tooling may be getting smarter, but the patch pile is (definitely) not getting smaller. This may be the beginning of an accelerating curve of ever larger patch cycles. My feeling is that we are in the middle of the beginning of this coming patch surge.

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For a decade, Kubernetes was the right answer. It organized containers, scaled services horizontally and gave platform teams a shared vocabulary for running software in production. It abstracted away enough of the underlying complexity that engineers could stop thinking about servers and start thinking about services. Most cloud-native infrastructure today is built on top of it, directly or in spirit, and EKS made that model the default for the majority of enterprise teams running workloads on AWS.

The workload that defined that era was the stateless HTTP request, fast in, fast out, disposable. A user action triggers a request, the request hits a service, the service returns a response and the container is done. Kubernetes was optimized for that pattern down to the scheduler internals: Bin-pack containers onto nodes, autoscale on CPU and memory, evict and reschedule when something goes wrong. The whole system is tuned around the assumption that individual units of work are short, stateless and interchangeable.

That assumption no longer holds for the workloads that matter most right now.

The agent workload is structurally different

Agents are long-running, stateful processes. They reason across time, call external tools, spawn subprocesses, write and execute code, and make decisions that depend on what happened five steps earlier in the same task. A single-agent workflow might run for minutes or hours, touching a dozen external systems and generating intermediate outputs that subsequent steps depend on. The compute layer for that kind of work needs to do things the old model was never asked to do. That is the new pattern: Execution infrastructure designed around agent semantics rather than request semantics.

The Kubernetes community itself has acknowledged this mismatch. In March 2026, Kubernetes SIG Apps published an introduction to Agent Sandbox, a new CRD-based abstraction designed specifically for singleton, stateful agent workloads. The framing is direct: The ecosystem is moving from short-lived, isolated tasks to deploying multiple, coordinated AI agents that run continuously, and mapping those workloads to traditional Kubernetes primitives requires an entirely new abstraction. The fact that the Kubernetes maintainers built a dedicated primitive for this, rather than recommending teams compose one from existing resources, is itself the clearest signal that agent execution does not fit the old model.

What agent execution actually requires

Concretely, it requires four things. First, isolated execution environments that provision in milliseconds, not minutes, so each agent task gets its own sandbox for code execution and tool calls without blocking the reasoning loop. The difference between a two-second environment and a two-minute environment is not a performance optimization; it determines whether the architecture is viable at all. Second, durable state management across the full task lifecycle, so an agent can pause, hand off or resume without re-initializing from scratch and burning tokens to reconstruct context it already built. Third, coordination primitives for multi-agent work: The ability to spawn subagents, pass structured outputs between them and track task dependencies across a graph of concurrent processes. Production agent systems are rarely single agents; they are pipelines of specialized agents with handoffs that need to be reliable and inspectable. Fourth, credentials and secrets management that travel with the execution context, so agents can authenticate to external services securely without exposing credentials in the task definition, logs or the environment variables of a shared container.

The mismatch shows up fast in production

Kubernetes and EKS expose the mismatch quickly in practice. Pod eviction terminates an agent mid-task with no clean recovery path. Autoscaling reads CPU utilization as the load signal, but an agent holding a long inference connection looks idle to the scheduler even when it is doing the most consequential work in the pipeline. Provisioning a new environment takes 45 seconds to two minutes on a well-tuned cluster; agent workloads need that in under two seconds or the reasoning loop stalls and the user experience degrades visibly. These are not edge cases or misconfigurations. They are the normal operating conditions for production agent workloads running on infrastructure that was not designed for them.

The utilization data makes the broader cost picture even starker. The 2026 State of Kubernetes Optimization Report from CAST AI, drawn from analysis of over 23,000 production clusters across AWS, Azure and GCP, found average CPU utilization at 8 percent, down from 10 percent the year prior. Memory utilization fell from 23 to 20 percent. CPU overprovisioning jumped from 40 to 69 percent year over year. These numbers reflect clusters running traditional workloads, and the pattern is worsening, not improving, as environments scale. Agent workloads compound this problem further. An agent holding an open inference connection or waiting on a tool call registers as idle to a scheduler that reads CPU and memory as the only meaningful load signals. The infrastructure responds to the wrong metric, overprovisioning capacity for demand it cannot measure, while the actual bottleneck, environment provisioning latency and state continuity, goes unaddressed.

Security is not the same problem it was before

Agent workloads change the threat model at the infrastructure level. A compromised stateless service exposes a narrow surface defined by its API contracts. A compromised agent exposes every system it can reach, every credential it holds and every action it is authorized to take on behalf of the user. Agents generate and execute their own code, make non-deterministic tool-call decisions and accumulate context across long-running sessions. Standard container namespacing does not contain that kind of risk. Kernel-level isolation, default-deny network egress, scoped credentials per session and agent-aware observability are not optional hardening steps. They are baseline requirements for running agents in production.

What teams that ship agents have already figured out

Some of the clearest evidence for this shift comes not from infrastructure vendors but from product engineering teams running agents at scale on their own code. In late 2025, Ramp’s engineering team published a detailed account of building Inspect, their internal background coding agent. Each Inspect session runs in a sandboxed VM with a full-stack development environment and deep integrations across their observability, CI, and deployment tooling. The architecture requirements map almost exactly to the four primitives above. Filesystem snapshots keep sessions starting in seconds rather than minutes. Sessions are isolated and stateful. The agent can run tests, review telemetry, query feature flags and visually verify frontend changes in a real browser. And the whole system supports unlimited concurrency, so engineers can spin up ten parallel sessions exploring different approaches to the same problem without contention.

The results speak for themselves. Within months of launch, roughly 30 percent of all pull requests merged to Ramp’s frontend and backend repositories were written by Inspect. That level of adoption was not mandated. It happened because the execution environment was fast enough, capable enough and well-integrated enough that the agent was strictly better than a local workflow for a meaningful share of tasks. The key insight from the Ramp case is not about the model. It is about the execution layer. As their team put it, session speed should only be limited by model-provider time-to-first-token; everything else, like cloning and installing, needs to be done before the session starts. That is a statement about infrastructure, not intelligence.

The ecosystem is catching up, but defaults are sticky

None of that is a criticism of the tools. Kubernetes solved exactly the problem it was designed for, and it solved it well. The issue is that infrastructure defaults are sticky. Teams inherit them, build on top of them and optimize within their constraints long after the underlying workload has changed. The Kubernetes community’s own response, the Agent Sandbox project under SIG Apps, validates the thesis that a new abstraction is necessary. The new primitives the community is building include warm pools for near-zero cold starts, lifecycle management for suspending and resuming idle agents without losing state, and pluggable kernel isolation for secure execution of untrusted code. These are not incremental improvements to existing resources. They are net-new abstractions that acknowledge the old model does not stretch to fit.

But adoption of purpose-built agent infrastructure remains early. Enterprises building agent pipelines today are largely running a request-oriented orchestration model against an execution-oriented workload, and the mismatch shows up in task failure rates, runaway costs and debugging cycles that have no good tooling because the observability layer was also designed for stateless services.

The structural advantage is available now

The infrastructure to close that gap exists now. The prerequisite is recognizing that agent execution is a first-class compute pattern with its own primitives and its own requirements, not a variant of the stateless service model that defined the last decade. Teams that make that shift early will have a meaningful structural advantage. The ones that do not will spend the next two years wondering why their agent systems are unreliable at a scale that should be tractable.

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It’s been several years since generative AI and large language models (LLMs) took the world by storm. LLMs surpassed earlier natural-language systems at generating text, while diffusion models enabled generating images, music, and videos.

These generative AI models work well in the digital world, but on their own, they have limited capabilities to comprehend the three-dimensional physical world and other spaces. This includes the objects occupying an area, how they relate to each other, tracking movement, and answering complex questions requiring an understanding of dimensions, distances, motion, and collisions.

Spatial intelligence is an AI capability that allows models to reason about three-dimensional space. These models can generate 3D scenes of the world and other spaces. This content can then be displayed through traditional renderers, game engines, or AR/VR systems that use spatial computing techniques. But it’s the spatial intelligence model’s ability to connect natural language with 3D models that has the most applications in robotics, manufacturing, construction, and other physical environments.   

Dr. Fei-Fei Li, often called the godmother of AI, published a manifesto on how spatial intelligence is AI’s next frontier, contrasting it with LLMs. “While current state-of-the-art AI can excel at reading, writing, research, and pattern recognition in data, these same models bear fundamental limitations when representing or interacting with the physical world,” wrote Dr. Li. “Our view of the world is holistic—not just what we’re looking at, but how everything relates spatially, what it means, and why it matters. Understanding this through imagination, reasoning, creation, and interaction—not just descriptions—is the power of spatial intelligence.”

The concept of spatial intelligence isn’t new and was described in Howard Gardner’s book, Frames of Mind, in 1983. Recent breakthroughs, including the launch of World Labs’ Marble and its $1 billion funding round, and competing approaches from Google’s Genie 3 and Nvidia Cosmos, should put spatial intelligence and world models on more R&D road maps.

What are spatial intelligence models?

It’s important to develop AI literacy and understand the terminology and concepts related to the physical world and 3D AI technologies: 

• Spatial intelligence encompasses specialized approaches such as geospatial models for mapping the physical world and building information modeling (BIM) for modeling physical structures. It also extends to generative 3D, robotics, and physical reasoning applications.
• World models are a class of neural network architectures and are currently a prominent approach to building spatial intelligence.
• Digital twins are live, virtual replicas of physical assets that combine 3D models with real-time sensor data. Spatial intelligence, an emerging capability of digital twins, adds natural-language prompting, generative scenario exploration, and physics-aware reasoning.
• Spatial computing refers to digital content anchored in and interacting with physical space, sensed and rendered in three dimensions and delivered through AR/VR and mixed-reality systems.

“Spatial intelligence models go beyond pixels to understand the 3D structure of the world—how objects are positioned, how they move, and how they interact,” says David Fattal, founder and CTO at Leia. “This enables applications like more realistic video generation, spatial computing interfaces, and AI systems that can reason about physical environments. As real-world 3D data becomes more available, these models will become foundational to the next generation of visual AI.”

Monitoring the built environment

To better understand spatial intelligence, let’s consider physical infrastructure such as bridges and buildings. The American Society of Civil Engineers estimates a $9.1 trillion investment is needed from 2024 through 2033 to achieve a state of good repair. When maintenance and monitoring lag, it can lead to major failures such as the 2022 collapse of the Fern Hollow Bridge in Pittsburgh.

Spatial intelligence and the development of digital twins may help identify issues earlier and prioritize where investments are needed. “Spatial intelligence models serve as the 4D digital blueprints for our built environment, allowing us to visualize and predict the complex interactions between aging assets and the shifting ground beneath them,” says Patrick Cozzi, chief platform officer at Bentley Systems. “By synthesizing disparate geospatial data into a living digital twin, these models provide the foresight necessary to mitigate the hidden risks of structural fatigue and subsurface instability.”

There’s a significant challenge in bridge health monitoring and transitioning from manual, infrequent structural inspections to leveraging sensors, digital twins, and spatial intelligence. Cozzi adds, “This integration of continuous field data moves beyond static documentation, empowering agencies to evolve from reactive repairs to proactive, resilient asset management that safeguards the long-term integrity of our most critical public systems.”

Avoiding collisions

Bridges are largely static, but the real world is increasingly being occupied by autonomous systems such as self-driving cars, robots, and drones. And where there are moving systems, there is a risk of collisions.

“Spatial intelligence models are AI systems that reason about the physical world by combining vision, sensor data, and contextual cues to understand space, motion, and object relationships,” says Sudeep George, CTO at iMerit. “The value of spatial intelligence models lies not just in perceiving an environment, but in enabling machines to act within it safely and in real time. That is especially important in robotics and autonomous systems, where decisions must be made in complex, multimodal, fast-changing settings.”

To see one example, this tutorial for simulating robotic environments combines Nvidia Isaac Sim, an open source robotics reference framework, with spatial intelligence in Marble from World Labs. 

Today’s collision detection systems, such as those used in autonomous vehicles, typically rely on modules for sensing, perception, planning, and control. Spatial intelligence models may offer improvements by assessing the collision risks of unidentified objects or by tracking objects that move out of sensor view. For example, Waymo’s World Model, built on Genie 3, is a simulator that generates complex weather conditions and other critical safety events.

For an out-of-this-world example, James Urquhart, field CTO and technology evangelist at Kamiwaza, has delivered several examples of spatial intelligence applications, including one for satellite collision detection and conflict analysis. Urquhart says, “Models that specialize in these types of data sets, as well as the physics and geography of the real world, enable faster and more accurate decision-making for tasks that depend on them.”

Applying spatial intelligence

Recent spatial intelligence announcements include creating 3D worlds from image or text prompts with Marble and simulating water physics, lighting, weather, and animal behavior with Genie 3. But difficulties remain in bringing spatial intelligence to physical-world use cases.

“Spatial intelligence and world models are laying the groundwork for future AI agents that will be able to interact in and with our physical world,” says Jason Corso, cofounder and chief scientist at Voxel51. “These models are significantly more challenging to develop and test, largely because the data underlying their development is complex, and it’s hard to handle all of the combinatorics involved in the physical world.”

In addition to learning the models and prototyping with them, development and data leaders need to review the data assets that will feed spatial intelligence models. “Spatial intelligence models translate location signals into a structured understanding of the real world, but they’re only as reliable as the data beneath them,” says Dan Adams, executive vice president and general manager of Enrich at Precisely. “The real unlock isn’t the model—it’s the reference layer with persistent identifiers, confidence metadata, and source lineage that lets AI reason about places, not just match strings.”

Even once applications are developed, there will be infrastructure challenges in deploying them at the edge. Ali Kayyam, principal research scientist at BrainChip, says, “The key to unlocking spatial intelligence at scale is having the low-power, event-driven hardware that can run it at the sensor in real time where it matters most.”

Where to get started

My suggestions for developers looking to get hands-on with spatial intelligence and world models:

• To try out Marble, review their API documentation and case studies, and then experiment with a developer-focused React application.
• Review the Nvidia Cosmos developer hub, documentation, and cookbook of case studies and learning paths.
• You can get an overview of Genie 3, but access is currently restricted through Project Genie, which requires a Google AI Ultra subscription.

Enterprises are facing two simultaneous challenges with AI: The risks associated with it are evolving faster than governance frameworks, while the business benefits are often difficult to measure.

This is one of the key findings of The Value of AI, a study commissioned by SAP from Oxford Economics. Now in its second year, the study surveyed 2,600 executives from 13 countries worldwide.

High expectations, limited preparation

On average, the enterprises surveyed plan to spend around $28 million on AI (up from $26.7 million last year), and expect a 21% ROI (from 16% last year). Expectations for AI agents are particularly high, with ROI expected to reach 17% this year, up from 10% last year. Furthermore, 83% of respondents worldwide said agentic AI has the potential to fundamentally transform their organization. On the other hand, only 3% of respondents said their enterprises were fully prepared for the deployment of AI agents.

There are gaps, particularly when it comes to governance:

• Only 12% of respondents said their skills or processes were able to govern AI effectively,
• 38% do not have human-in-the-loop processes in place for oversight of AI agents, and
• only 63% have established permissions and access controls for agents.

Other concerns include weaknesses in the organization of AI deployment, poor data quality, insufficient employee training, and the widespread use of shadow AI.

Governance is the bigger challenge

In an interview, Sean Kask, Chief AI Strategy Officer at SAP, commented on the study’s key findings.

Mr. Kask, in the study’s foreword, you write that companies are currently facing two challenges simultaneously: The risks associated with AI are evolving faster than governance, while the business benefits are often difficult to measure. Which of these poses the greater problem for companies?

Sean Kask: Measuring the business value of IT investments has never been easy. The same applies to AI. That’s why I currently consider the governance issue to be the greater challenge. While traditional governance principles and best practices for secure software development remain important even in the age of large language models and agent-based AI, entirely new risks are emerging at the same time.

For example, as soon as companies roll out AI on a broad scale, they suddenly discover hundreds or even thousands of so-called shadow agents that employees are using without central oversight. Or they find that a significant portion of the workforce is copying content into private ChatGPT accounts. Such risks often only become apparent once AI is already being used productively.

According to your study, German companies invest an average of nearly $40 million in AI, more than companies in all other countries surveyed. Why is that?

Kask: I was less surprised by the amount of investment than by the fact that, overall, the level of investment and the return on investment achieved have developed very similarly across the various countries. There’s no clear answer as to why Germany invests more. In part, it’s likely simply because costs here are higher than in India, for example.

However, we’re also seeing a high level of AI adoption among German companies. SAP has a dashboard that allows us to track how our customers are using AI features. Germany is among the countries with particularly high usage. Added to this are the strong industrial base and the political impetus from Europe, which are driving the use of AI. Accordingly, companies there are making targeted investments in building the necessary expertise.

According to the study, 47% of German companies are satisfied with the return on investment from their AI investments. At the same time, 77% say they are still far from realizing AI’s full potential. Isn’t that a contradiction?

Kask: No, we see this pattern worldwide. Companies initially invest in a few AI use cases and realize: This works; we’re creating added value. Accordingly, they’re satisfied with their investment.

But this is precisely what leads them to identify further use cases. They explore AI agents and want to utilize them as well. However, it is exactly at this point that many encounter new challenges in implementation and scaling.

The study therefore primarily highlights a learning curve: The more experience companies gain with AI, the greater their awareness of its previously untapped potential becomes.

According to the study, only 33% of companies surveyed have KPIs at the executive board level that are directly linked to the implementation of AI. In your view, which metrics should supervisory boards and CEOs definitely be tracking?

Kask: For us, a key indicator is employee enablement. How many employees have already successfully completed training or upskilling programs related to AI? Without the appropriate skills, AI adoption will fall short of its potential.

Transparency is equally important. Companies should know which AI agents are actually in use within their landscape. SAP offers the SAP AI Agent Hub for this purpose, which automatically discovers and inventories agents from SAP and third-party environments. Customers have already been able to identify thousands of agents this way, which highlights the need for centralized governance and transparency.

In addition, companies should have a complete overview of all AI use cases. A robust business case should be in place for each use case. We often see two extremes: Either the executive board is under pressure to implement AI as quickly as possible and allocates a lump-sum budget for this purpose. Or management initially takes a wait-and-see approach. This leads to independent pilot projects springing up throughout the company, with individual departments procuring their own tools and entering into their own contracts.

At SAP, we therefore follow a clearly structured selection process. Each idea first undergoes an assessment of its expected business value. We then examine technical feasibility, data availability, and ethical and governance aspects. From management’s perspective, it is crucial to maintain transparency regarding all ongoing AI projects at all times and to consistently prioritize them based on their business value.

Agents, too, need a ‘hire-to-retire’ lifecycle

Even with the introduction of dozens or even hundreds of AI agents, governance becomes increasingly complex. What capabilities do enterprise platforms need to manage AI agents securely and in a controlled manner at scale?

Kask: We make a conscious effort not to anthropomorphize AI too much. Nevertheless, the analogy is helpful: Agents require a complete hire-to-retire lifecycle. This begins with the detection and registration of an agent. It is then integrated into the enterprise environment, granted the necessary permissions, and given access to the data sources it needs to perform its tasks.

Observability is just as important. Companies must be able to track what an agent is actually doing in the system at all times. In addition, they should track key performance indicators: Is the agent achieving the desired results? How efficiently is it working? How many tokens does it consume? How many processing steps does it require for a task?

Ultimately, this involves several key components: a complete inventory of all agents, appropriate governance, risk, and compliance (GRC) mechanisms, transparency regarding agent behavior, and continuous monitoring. This is the only way to ensure that AI agents consistently operate within defined parameters and deliver the desired business value.

In your estimation, which business processes will companies actually delegate entirely to AI agents over the next two to three years?

Kask: Currently, such agents work particularly well in clearly defined use cases. SAP will release more than 50 (currently 34) specialized AI agents.

One example is periodic financial reporting. In this context, journal entries must be made based on numerous rules stored in documents, emails, or previous transactions. The agent analyzes these various sources of information, derives a recommendation from them, and suggests the appropriate journal entry to the user.

Based on what we’ve heard from customer projects, employees at medium-sized companies currently spend about twelve hours per month on these tasks. With the help of an AI agent, this effort can be reduced to two to three hours.

Another area of application is production planning. If delivery dates change or new orders come in at short notice, the entire production plan must be adjusted. It is precisely these kinds of complex optimization tasks that are ideally suited for AI agents.

In principle, there are virtually no limits to the narrowly defined business processes in which agents can be deployed. However, they will not operate completely autonomously at first.

Trust in AI begins with a stable foundation

Many companies still struggle to trust AI agents. After all, large language models operate probabilistically and can produce false information. This is particularly problematic in financial processes. How do you build trust?

Kask: Trust begins with a stable foundation. ERP systems remain the reliable system of record. They operate deterministically, contain the business logic, and hold the relevant company data. AI agents build upon this foundation. They do not replace it.

Equally important is the human-in-the-loop principle. Employees must be able to understand what the agent is doing, verify its results, and intervene if necessary. That’s why employee training also plays a crucial role. They must understand how generative AI works and where its limitations lie.

Of course, language models can hallucinate. At the same time, we must not forget that humans are not infallible either. The key lies in the collaboration between humans and AI. This allows us to improve both the efficiency and the quality of many business processes.

Another important component is transparency. Our global AI ethics policy, for example, stipulates that users must always be able to recognize when AI is involved. In Joule, it’s possible to trace which data sources the agent used and which steps it went through in reaching its decision. This traceability is an essential prerequisite for trust.

What distinguishes an SAP agent from a general AI agent that merely accesses an ERP system?

Kask: The key difference is that Joule and the SAP agents are directly embedded in the ERP system. There, for example, we’ve built a knowledge graph that describes the semantic relationships between all tables, business objects, and data fields.

To put this into perspective: The SAP S/4HANA Knowledge Graph is based on approximately 452,000 ABAP tables, 7.3 million data fields, and thousands of analytical views. The semantic relationships between these artifacts are modeled in the Knowledge Graph and made available for AI applications.

For example, if a user wants to view all open purchase orders, the agent does not first have to laboriously search for the relevant information. It immediately knows which tables and objects are relevant and also understands the relationships between a purchase order, a purchase requisition, the responsible approvers, and other business objects. As a result, the agent not only works much more precisely but also requires significantly fewer tokens because it can greatly narrow down the search space.

If, instead, one attempts to simply overlay AI onto an existing system or extract data from a relational ERP system, many of these relationships are lost. In a sense, this destroys the semantic context that is crucial for precise answers.

That is why we view the ERP system as an enormous strategic advantage. It has been the system of record for decades and contains roughly 50 years of codified business and process knowledge. This knowledge forms the foundation for what we call the autonomous enterprise. The agents build upon this knowledge and continue to develop it.

In the future, SAP agents will also communicate bidirectionally with agents from other providers via standards such as Agent-to-Agent (A2A).

According to your study, AI currently creates the greatest added value in decision-making, customer interaction, and gaining new insights, rather than in traditional productivity gains. Will this change the way companies justify AI investments in the future?

Kask: In our study, productivity was simply rated slightly lower than, for example, gaining new insights. In the long term, however, productivity remains the ultimate goal. Europe, in particular, has been suffering from comparatively weak productivity growth for years.

At SAP, we therefore first evaluate every new AI feature based on its specific business value. For all agents and AI features that we include in our AI Feature Catalog, we first conduct a value analysis. We ask: What benefit does the feature offer the user? Does it contribute to higher revenue? Does it increase productivity? Only then is it developed further.

At the moment, the greatest added value often still lies in consolidating information from structured and unstructured data sources and making it accessible via natural language. The next step, however, is to translate these insights directly into more efficient business processes. That is precisely where the greatest productivity gains will be realized in the future.

If you could give CIOs just one or two pieces of advice for the transition from generative AI to AI agents, what would they be?

Kask: In my view, the biggest mistake would be to try to transform the entire company all at once or to attempt to perfectly prepare all the data right from the start.

Instead, you should consider what kind of agent can create significant added value, and then implement it. Of course, this agent needs access to consistent and context-rich enterprise data. That’s exactly what we’re working on at SAP with technologies like the knowledge graph, which maps the semantic relationships within enterprise data.

In addition, with data products and the SAP Business Data Cloud, we provide tools that make data from various sources usable for AI agents. Thanks to zero-copy and data fabric approaches, information from legacy systems, Snowflake, or ERP systems can be consolidated without first having to extensively replicate the data. For a procurement agent, this makes it possible to provide exactly the relevant data for the specific use case.

The key point is this: Companies do not have to wait until they have fully migrated to the cloud or consolidated their entire data landscape. With the technologies available today, data can already be made usable for specific AI agents, managed in a controlled manner, and used to quickly generate initial business value. On the other hand, those who wait for the perfect starting point run the risk of falling behind.

This article is adapted from one first published by Computerwoche.

OpenAI’s first device will be a screenless home AI system that can play music, control appliances, and respond to messages and questions, according to Bloomberg. I can’t help but ask what makes this device different from Apple’s HomePod with SiriAI?

The OpenAI product is intended to be the first of a family of solutions and is expected to use the recently-introduced GPT-Live large language model (LLM). The latter is an advanced model capable of processing information swiftly and of providing natural responses to conversations. 

A potential gold mine for hackers

This expertise might help it deliver more accurate responses to requests, though the information could also become a gold mine for data brokers, hackers, and advertisers if there turns out to be any way they can get their hands on it. It’s not yet known how — or even if — OpenAI proposes protecting user privacy within its systems. 

The device, which is still under development, is explained as being a home companion that also includes a built-in camera and sensors so it can gather contextual information about where you are, becoming an expert on you and your needs. It also features autonomous mechanical elements that physically shift on their own, intended to give the product a “personality,” rather than being a boring black box.

Can we live without this?

While OpenAI’s development is not yet complete and things could change before it reaches market, based on Bloomberg’s report I’m not terribly clear how unique it is going to be. After all, as LLM support is introduced in existing smart speaker systems from Apple, or even Amazon, what unique features does this device bring that consumers can’t live without? More particularly, what problems does it solve and why does it exist?

Manufacturing economics

What’s also unclear is how far along OpenAI is on the road to mass manufacturing the device. Apple’s recent lawsuit against OpenAI confirmed the challenger is speaking with Apple’s own manufacturing partners as well as hiring hundreds of Apple engineers. Despite the talent war, I consider it unlikely OpenAI will be able to lock in the kinds of manufacturing deals it needs to bring the product to market at an acceptable price. 

That suggests either that its inaugural “home companion” will seem incredibly expensive (as so many of the products Jony Ive has designed since leaving Apple seem to be), or that OpenAI will sell these things at a subsidy. 

Bloomberg suggests the systems will cost $200 to $300. That seems low given the current component market, expected design quality and the technology used if the plan is to make something good. And it leaves me wondering how deeply investors will underwrite hardware sales, given the eye-watering losses the company is already making.

Apple’s trade secrets case

Given ongoing speculation that Apple plans something similar in the form of a hybrid HomePod/iPad equipped with AI and limited mobility, the recent lawsuit strongly suggests Apple feels some of OpenAI’s plans cross the line into using proprietary technologies and ideas Cupertino has spent years pursuing. Apple’s lawsuit seems to bring much more meaningful evidence than just an argument concerning product design. 

Betting the farm on Ive

We also don’t know the extent to which consumers will be open to semi-sentient AI devices lurking in their lives. While Apple can lean into its loyal customer base and broaden its offering with rock-solid promises concerning user privacy, OpenAI has less to bring to the launch party.

That means it is attempting to pivot millions who use its services into investing in its hardware. It presumably hopes that it will be able to drive that transition by using the design involvement of acclaimed Apple designer Jony Ive as a form of magic talisman. 

The challenge is that while Ive is a big name in Apple history, Apple users are extremely loyal and may react against the involvement of their favorite designer. It’s like finding out someone you thought was on your team actually supported someone else. 

It will be different outside Apple, where less loyal cohorts might see the product introduction as a chance to put a design from Ive through its paces without signing up to a Mac, iPhone, iPad, or HomePod. 

For the rest of us, the question will be whether OpenAI’s Ive-designed product channels the successful design ethic of the iMac, or that of the far less successful hockey puck mouse. Like (timely World Cup klaxon) France against Spain, OpenAI’s investors have to hope the best version of Ive’s design principles show up, because their risked fortunes potentially depend on it. 

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It’s been several years since generative AI and large language models (LLMs) took the world by storm. LLMs surpassed earlier natural-language systems at generating text, while diffusion models enabled generating images, music, and videos.

These generative AI models work well in the digital world, but on their own, they have limited capabilities to comprehend the three-dimensional physical world and other spaces. This includes the objects occupying an area, how they relate to each other, tracking movement, and answering complex questions requiring an understanding of dimensions, distances, motion, and collisions.

Spatial intelligence is an AI capability that allows models to reason about three-dimensional space. These models can generate 3D scenes of the world and other spaces. This content can then be displayed through traditional renderers, game engines, or AR/VR systems that use spatial computing techniques. But it’s the spatial intelligence model’s ability to connect natural language with 3D models that has the most applications in robotics, manufacturing, construction, and other physical environments.   

Dr. Fei-Fei Li, often called the godmother of AI, published a manifesto on how spatial intelligence is AI’s next frontier, contrasting it with LLMs. “While current state-of-the-art AI can excel at reading, writing, research, and pattern recognition in data, these same models bear fundamental limitations when representing or interacting with the physical world,” wrote Dr. Li. “Our view of the world is holistic—not just what we’re looking at, but how everything relates spatially, what it means, and why it matters. Understanding this through imagination, reasoning, creation, and interaction—not just descriptions—is the power of spatial intelligence.”

The concept of spatial intelligence isn’t new and was described in Howard Gardner’s book, Frames of Mind, in 1983. Recent breakthroughs, including the launch of World Labs’ Marble and its $1 billion funding round, and competing approaches from Google’s Genie 3 and Nvidia Cosmos, should put spatial intelligence and world models on more R&D road maps.

What are spatial intelligence models?

It’s important to develop AI literacy and understand the terminology and concepts related to the physical world and 3D AI technologies: 

• Spatial intelligence encompasses specialized approaches such as geospatial models for mapping the physical world and building information modeling (BIM) for modeling physical structures. It also extends to generative 3D, robotics, and physical reasoning applications.
• World models are a class of neural network architectures and are currently a prominent approach to building spatial intelligence.
• Digital twins are live, virtual replicas of physical assets that combine 3D models with real-time sensor data. Spatial intelligence, an emerging capability of digital twins, adds natural-language prompting, generative scenario exploration, and physics-aware reasoning.
• Spatial computing refers to digital content anchored in and interacting with physical space, sensed and rendered in three dimensions and delivered through AR/VR and mixed-reality systems.

“Spatial intelligence models go beyond pixels to understand the 3D structure of the world—how objects are positioned, how they move, and how they interact,” says David Fattal, founder and CTO at Leia. “This enables applications like more realistic video generation, spatial computing interfaces, and AI systems that can reason about physical environments. As real-world 3D data becomes more available, these models will become foundational to the next generation of visual AI.”

Monitoring the built environment

To better understand spatial intelligence, let’s consider physical infrastructure such as bridges and buildings. The American Society of Civil Engineers estimates a $9.1 trillion investment is needed from 2024 through 2033 to achieve a state of good repair. When maintenance and monitoring lag, it can lead to major failures such as the 2022 collapse of the Fern Hollow Bridge in Pittsburgh.

Spatial intelligence and the development of digital twins may help identify issues earlier and prioritize where investments are needed. “Spatial intelligence models serve as the 4D digital blueprints for our built environment, allowing us to visualize and predict the complex interactions between aging assets and the shifting ground beneath them,” says Patrick Cozzi, chief platform officer at Bentley Systems. “By synthesizing disparate geospatial data into a living digital twin, these models provide the foresight necessary to mitigate the hidden risks of structural fatigue and subsurface instability.”

There’s a significant challenge in bridge health monitoring and transitioning from manual, infrequent structural inspections to leveraging sensors, digital twins, and spatial intelligence. Cozzi adds, “This integration of continuous field data moves beyond static documentation, empowering agencies to evolve from reactive repairs to proactive, resilient asset management that safeguards the long-term integrity of our most critical public systems.”

Avoiding collisions

Bridges are largely static, but the real world is increasingly being occupied by autonomous systems such as self-driving cars, robots, and drones. And where there are moving systems, there is a risk of collisions.

“Spatial intelligence models are AI systems that reason about the physical world by combining vision, sensor data, and contextual cues to understand space, motion, and object relationships,” says Sudeep George, CTO at iMerit. “The value of spatial intelligence models lies not just in perceiving an environment, but in enabling machines to act within it safely and in real time. That is especially important in robotics and autonomous systems, where decisions must be made in complex, multimodal, fast-changing settings.”

To see one example, this tutorial for simulating robotic environments combines Nvidia Isaac Sim, an open source robotics reference framework, with spatial intelligence in Marble from World Labs. 

Today’s collision detection systems, such as those used in autonomous vehicles, typically rely on modules for sensing, perception, planning, and control. Spatial intelligence models may offer improvements by assessing the collision risks of unidentified objects or by tracking objects that move out of sensor view. For example, Waymo’s World Model, built on Genie 3, is a simulator that generates complex weather conditions and other critical safety events.

For an out-of-this-world example, James Urquhart, field CTO and technology evangelist at Kamiwaza, has delivered several examples of spatial intelligence applications, including one for satellite collision detection and conflict analysis. Urquhart says, “Models that specialize in these types of data sets, as well as the physics and geography of the real world, enable faster and more accurate decision-making for tasks that depend on them.”

Applying spatial intelligence

Recent spatial intelligence announcements include creating 3D worlds from image or text prompts with Marble and simulating water physics, lighting, weather, and animal behavior with Genie 3. But difficulties remain in bringing spatial intelligence to physical-world use cases.

“Spatial intelligence and world models are laying the groundwork for future AI agents that will be able to interact in and with our physical world,” says Jason Corso, cofounder and chief scientist at Voxel51. “These models are significantly more challenging to develop and test, largely because the data underlying their development is complex, and it’s hard to handle all of the combinatorics involved in the physical world.”

In addition to learning the models and prototyping with them, development and data leaders need to review the data assets that will feed spatial intelligence models. “Spatial intelligence models translate location signals into a structured understanding of the real world, but they’re only as reliable as the data beneath them,” says Dan Adams, executive vice president and general manager of Enrich at Precisely. “The real unlock isn’t the model—it’s the reference layer with persistent identifiers, confidence metadata, and source lineage that lets AI reason about places, not just match strings.”

Even once applications are developed, there will be infrastructure challenges in deploying them at the edge. Ali Kayyam, principal research scientist at BrainChip, says, “The key to unlocking spatial intelligence at scale is having the low-power, event-driven hardware that can run it at the sensor in real time where it matters most.”

Where to get started

My suggestions for developers looking to get hands-on with spatial intelligence and world models:

• To try out Marble, review their API documentation and case studies, and then experiment with a developer-focused React application.
• Review the Nvidia Cosmos developer hub, documentation, and cookbook of case studies and learning paths.
• You can get an overview of Genie 3, but access is currently restricted through Project Genie, which requires a Google AI Ultra subscription.

Red Hat OpenShift 4.22, an update to the company’s hybrid cloud application platform, is now generally available. The release focuses on cutting cloud infrastructure costs, simplifying operations of virtualized workloads, and securing sensitive data.

Announced July 14, Red Hat OpenShift 4.22 continues to harden the platform foundation to meet growing security standards, helping reduce the manual effort of compliance and risk mitigation, Red Hat said. The introduction of a minimal Red Hat Universal Base Image (UBI) strips away non-essential packages to reduce the overall attack surface. With Red Hat OpenShift sandboxed containers 1.12, OpenShift 4.22 makes support for confidential containers on bare metal generally available.

The OpenShift 4.22 release also introduces confidential AI as a technology preview. With confidential AI, organizations can isolate and run highly sensitive workloads and proprietary AI algorithms inside a cryptographically isolated slice of memory and CPU, providing data privacy even during runtime execution, according to Red Hat.

OpenShift 4.22 also brings new Red Hat OpenShift Virtualization capabilities. A new Ethernet virtual private network integration with user-defined networks allows teams to connect containerized and virtualized workloads to external infrastructure. Volume groups now can be used to execute multi-volume snapshots for VMs, providing a crash-consistent backup mechanism that simplifies disaster recovery. And the introduction of two-node OpenShift with fencing provides a highly resilient and resource-efficient option for constrained edge environments, Red Hat said.

In addition, OpenShift 4.22 offers new platform capabilities designed to optimize resource usage and lower operational overhead. The Red Hat build of Karpenter, an open source auto-scaler that right-sizes compute instances for Kubernetes clusters, is now generally available for Red Hat OpenShift Service on AWS with hosted control planes. And customers running Red Hat OpenShift Service on AWS with hosted control planes now can integrate AWS EC2 Spot Instances for fault-tolerant workloads to save on costs.

Finally, Red Hat OpenShift 4.22 introduces the JobSet operator to streamline large-scale distributed training runs and LLM fine-tuning. This framework coordinates multiple related jobs as a single unit, maximizing the use of expensive GPU compute.

Message a business on WhatsApp this week and you may be greeted by software. On June 3, Meta made its Business AI agent available to companies everywhere, a bot that answers questions, recommends products, books appointments, qualifies sales leads and hands you to a human when it gets stuck. It comes bundled in WhatsApp’s premium business tiers, and the largest companies pay for it by the token. After almost two years of testing in markets like India and Mexico, it is now live worldwide.

I build AI agents for a living, and this is a good one. It also sits on top of the largest messaging network ever built. WhatsApp passed three billion monthly users last year. Mark Zuckerberg says people now hold more than a billion threads a day with business accounts across Meta’s apps. Paid messaging on WhatsApp crossed a two-billion-dollar annual run rate in the fourth quarter of 2025, and click-to-WhatsApp ad revenue grew sixty percent year over year. Meta has spent a decade trying to turn all of that talking into buying, and the agent is its most capable attempt yet.

So, picture the moment the agent finishes taking your order. What happens next?

The model Meta keeps pointing at

In Hangzhou or Shenzhen, the answer is that your order shows up, often within the hour. China fused messaging, payments and shopping into single apps more than a decade ago. WeChat carries roughly 1.4 billion users, an in-app store layer with hundreds of millions of monthly shoppers, and a wallet most of the country pays with. Korea built its own version, where KakaoTalk made chat the default way to send a gift. This is the world Meta gestures at when it imagines what WhatsApp could be.

And yet WeChat, the purest “messaging app does commerce” story, is not actually China’s shopping champion, even though it arrived first and is still the bigger app. People do not open a messaging app to browse and shop. The buying went instead to Douyin, the Chinese app run by TikTok’s owner ByteDance, whose endless video feed is engineered to make you want things you were not looking for. WeChat had the users and the wallet, and it still lacked the two things that actually move commerce: A feed that creates demand and a way to deliver the goods. A chat window is neither.

The moat was never the storefront

Amazon learned the same lesson from the other side. Its moat was never the website. It was the warehouses, the trucks and the two-day promise (then one-day, then same-day) that rivals could not match. In 2025 Amazon passed the US Postal Service to become the largest parcel carrier in the country by volume, moving 6.7 billion packages. Roughly 180 million Americans pay for Prime. The storefront is the part everyone sees; the fulfillment network is the part that wins.

Asia’s commerce leaders made the same bet. Coupang built Korea’s Amazon by pouring billions into logistics: Order by midnight, and it arrives before 7 a.m., weekends included. Seven in ten Koreans now live within ten minutes of a Coupang warehouse. Even Alibaba, which grew up as an asset-light marketplace that owned no trucks, eventually concluded it had to build a logistics arm to keep pace.

Speed sells, too. In China, McKinsey found, live shopping converts viewers into buyers at rates approaching 30 percent, roughly ten times an ordinary web page, because the fulfillment behind it delivers the impulse before it cools. The conversation creates the want, but the warehouse turns it into a sale.

Even where messaging rules

Korea shows what a messenger can and cannot win. KakaoTalk is the country’s WhatsApp, and it owns one kind of commerce completely: gifting. Koreans send presents straight from the chat window, close to 200 million of them in 2025, which is nearly all of the country’s mobile gifting. But notice what kind of commerce that is. A gift voucher or a coffee coupon needs no warehouse. The moment a purchase becomes a physical thing that has to arrive fast, the winner is no longer the messenger but Coupang and its dawn-delivery network. KakaoTalk owns the commerce that fits inside a message; Coupang owns the commerce that needs a truck.

Japan makes the same point in the negative. LINE is about as dominant a messenger as exists anywhere, reaching 97 million people, close to 78 percent of the country. If messaging reach alone turned into commerce, LINE would own Japanese retail. Instead, it shut down its own payments service in 2025 and handed the wallet to a rival, while the actual shopping stayed with Rakuten and Amazon Japan. The most-used chat app in the country could not turn that reach into owning what people buy.

Every market tells the same story: A chat app does not win physical commerce. Whoever owns the warehouse does.

What Meta is missing

Which brings us back to the WhatsApp agent, where Meta starts further ahead than WeChat ever did. Through Instagram and Reels it owns the demand-making feed WeChat never had, the agent gives it the sales conversation, and in the West, paying by card is universal. Only the last pillar is missing. Meta has no warehouses, no trucks, no delivery promise of its own and the few times it reached for the pieces around the sale, it pulled back: Its own wallet, Meta Pay, never became something people use, and in 2025 it wound down in-app checkout for Facebook and Instagram Shops, sending buyers back to merchants’ own sites to pay, ship and handle returns. Even Marketplace, its billion-user listings surface, mostly stays out of the transaction itself.

And in the West, that last pillar is already spoken for. The West did fuse commerce, just not around chat. Amazon long ago combined the storefront, the payment, its own branded credit cards and the expensive part, the warehouses and the trucks, into one app that owns the American purchase from search to doorstep. That is the same kind of vertical integration China’s commerce giants built, with players like Alibaba and JD racing into a market where no Amazon yet stood in the way. In the US, that lane was filled years ago.

The other half

None of this makes the agent a mistake. It is already a booming ad business for Meta, and maybe that is all Meta wants it to be: Commerce’s front door, sending the shopper onward and billing the merchant for the introduction.

But goods are only half of commerce, and the other half never needed a warehouse. Remember what KakaoTalk won: Gifting, the one kind of buying that ships nothing. Services are the same, only far bigger. A haircut, a dental cleaning, a training session, a plumber’s visit, a tutor’s hour: None of it sits in a fulfillment center. The transaction is a booking, not a box.

And a booking is exactly what the agent is built to take. Look at the feature Meta put in its own announcement, right beside answering questions and recommending products: It books appointments. For a salon, a clinic or a one-person studio, that is a front desk. Give it the two pieces still missing, a calendar to hold the schedule and a way to take payment inside the chat, and WhatsApp stops being where those businesses message customers and becomes where they run the day.

None of it needs a warehouse, and none of it is Amazon’s to defend. Does that put Meta on a collision course with Square and Mindbody?

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Spring is a reliable and popular framework for building web and enterprise Java applications. In this article, you’ll learn how to unit test each layer of a Spring MVC application, using built-in testing tools from JUnit 5 and Spring to mock each component’s dependencies. In addition to unit testing with MockMvc, Mockito, and Spring’s TestEntityManager, I’ll also briefly introduce slice testing using the @WebMvcTest and @DataJpaTest annotations, used to optimize unit tests on web controllers and databases.

Also see: How to test your Java applications with JUnit 5.

Overview of testing Spring MVC applications

Spring MVC applications are defined using three technology layers:

• Controllers accept web requests and return web responses.
• Services implement the application’s business logic.
• Repositories persist data to and from your back-end SQL or NoSQL database.

When we unit test Spring MVC applications, we test each layer separately from the others. We create mock implementations, typically using Mockito, for each layer’s dependencies, then we simulate the logic we want to test. For example, a controller may call a service to retrieve a list of objects. When testing the controller, we create a mock service that either returns the list of objects, returns an empty list, or throws an exception. This test ensures the controller behaves correctly.

We’ll use Spring MVC to build and test a simple web service that manages widgets. The structure of the web service is shown here:

Steven Haines

This is a classic MVC pattern. We have a widget controller that handles RESTful requests and delegates its business functionality to a widget service, which uses a widget repository to persist widgets to and from an in-memory H2 database.

Get the source: Download the source code for this article.

Unit testing a Spring MVC controller with MockMvc

Setting up a Spring MVC controller test is a two-step process:

• Annotate your test class with @WebMvcTest.
• Autowire a MockMvc instance into your controller.

We could annotate all our test classes with @SpringBootTest, but we’ll use @WebMvcTest instead. The reason is that the @WebMvcTest annotation is used for slice testing. Whereas @SpringBootTest loads your entire Spring application context, @WebMvcTest loads only your web-related resources. Furthermore, if you specify a controller class in the annotation, it will only load the specific controller you want to test. Testing a single “slice” of your application reduces both the amount of compute resources required to set up the test and the time required to run a test.

For example, when we test a controller, we’ll mock just the services it uses, and we won’t need any repositories at all. If we don’t need them, then we needn’t waste time loading them. Slice tests were created to make tests perform better and run faster.

Here’s the source code for the Widget class we’ll be managing:

package com.infoworld.widgetservice.model;
import jakarta.persistence.Entity;
import jakarta.persistence.GeneratedValue;
import jakarta.persistence.GenerationType;
import jakarta.persistence.Id;

@Entity
public class Widget {
    @Id
    @GeneratedValue(strategy = GenerationType.AUTO)
    private Long id;
    private String name;
    private int version;

    public Widget() {
    }

    public Widget(String name) {
        this.name = name;
    }

    public Widget(String name, int version) {
        this.name = name;
        this.version = version;
    }

    public Widget(Long id, String name, int version) {
        this.id = id;
        this.name = name;
        this.version = version;
    }

    public Long getId() {
        return id;
    }

    public void setId(Long id) {
        this.id = id;
    }

    public String getName() {
        return name;
    }

    public void setName(String name) {
        this.name = name;
    }

    public int getVersion() {
        return version;
    }

    public void setVersion(int version) {
        this.version = version;
    }
}

A Widget is a JPA entity that manages three fields:

• id is the primary key of the table, annotated with @Id and @GeneratedValue, with an automatic generation strategy.
• name is the name of the widget.
• version is the version of the widget resource. We’ll use this value to populate our eTag value and check it in our PUT operation’s If-Match header value. This ensures the widget being updated is not stale.

Here’s the source code for the controller we’ll be testing (WidgetController.java):

package com.infoworld.widgetservice.web;

import java.net.URI;
import java.net.URISyntaxException;
import java.util.List;
import java.util.Optional;
import com.infoworld.widgetservice.model.Widget;
import com.infoworld.widgetservice.service.WidgetService;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.http.HttpStatus;
import org.springframework.http.ResponseEntity;
import org.springframework.web.bind.annotation.DeleteMapping;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.PathVariable;
import org.springframework.web.bind.annotation.PostMapping;
import org.springframework.web.bind.annotation.PutMapping;
import org.springframework.web.bind.annotation.RequestBody;
import org.springframework.web.bind.annotation.RequestHeader;
import org.springframework.web.bind.annotation.RestController;

@RestController
public class WidgetController {
    @Autowired
    private WidgetService widgetService;
    @GetMapping("/widget/{id}")
    public ResponseEntity getWidget(@PathVariable Long id) {
        return widgetService.findById(id)
                .map(widget -> {
                    try {
                        return ResponseEntity
                                .ok()
                                .location(new URI("/widget/" + id))
                                .eTag(Integer.toString(
                                               widget.getVersion()))
                                .body(widget);
                    } catch (URISyntaxException e) {
                        return ResponseEntity
                          .status(HttpStatus.INTERNAL_SERVER_ERROR)
                          .build();
                    }
                })
                .orElse(ResponseEntity.notFound().build());
    }
    @GetMapping("/widgets")
    public List getWidgets() {
        return widgetService.findAll();
    }
    @PostMapping("/widgets")
    public ResponseEntity createWidget(@RequestBody Widget widget)
    {
        Widget newWidget = widgetService.create(widget);
        try {
           return ResponseEntity
                   .created(new URI("/widget/" + newWidget.getId()))
                   .eTag(Integer.toString(newWidget.getVersion()))
                   .body(newWidget);
        } catch (URISyntaxException e) {
            return ResponseEntity
                    .status(HttpStatus.INTERNAL_SERVER_ERROR)
                    .build();
        }
    }

    @PutMapping("/widget/{id}")
    public ResponseEntity updateWidget(@PathVariable Long id,
                                          @RequestBody Widget widget,
                         @RequestHeader("If-Match") Integer ifMatch) {
        Optional existingWidget = widgetService.findById(id);
        return existingWidget.map(w -> {
            if (w.getVersion() != ifMatch) {
                return ResponseEntity.status(HttpStatus.CONFLICT)
                                     .build();
            }

            w.setName(widget.getName());
            w.setVersion(w.getVersion() + 1);

            Widget updatedWidget = widgetService.save(w);
            try {
                return ResponseEntity.ok()
                        .location(new URI("/widget/" + 
                                      updatedWidget.getId()))
                        .eTag(Integer.toString(
                                      updatedWidget.getVersion()))
                        .body(updatedWidget);
            } catch (URISyntaxException e) {
                throw new RuntimeException(e);
            }
        }).orElse(ResponseEntity.notFound().build());
    }

    @DeleteMapping("widget/{id}")
    public ResponseEntity deleteWidget(@PathVariable Long id) {
        Optional existingWidget = widgetService.findById(id);
        return existingWidget.map(w -> {
           widgetService.deleteById(w.getId());
           return ResponseEntity.ok().build();
        }).orElse(ResponseEntity.notFound().build());
    }
}

The WidgetController handles GET, POST, PUT, and DELETE operations, following standard RESTful principles, so we’re going to write tests for each operation.

The following source code shows the structure of our test class (WidgetControllerTest.java):

package com.infoworld.widgetservice.web;

@WebMvcTest(WidgetController.class)
public class WidgetControllerTest {
    @Autowired
    private MockMvc mockMvc;

    @MockitoBean
    private WidgetService widgetService;
}

I omitted the imports for readability, but the important thing to note is that the class is annotated with the @WebMvcTest annotation, and that we pass in the WidgetController.class as the controller we’re testing. This tells Spring to only load the WidgetController and no other Spring resources. The @WebMvcTest annotation includes other annotations, but the important one for our tests is @AutoConfigureMockMvc, which will cause Spring to create a MockMvc instance and add it to the application context. That lets us autowire it into our test class using the @Autowired annotation.

Next, we use the @MockitoBean annotation to use Mockito to create a mock implementation of the WidgetService, after which Spring will autowire it into the WidgetController class. This lets us control the behavior of the WidgetService for the WidgetController test cases we’re writing. Note that starting in Spring Boot version 3.4, @MockitoBean replaced @MockBean. Everything you know about @MockBean translates to using @MockitoBean—with some improvements.

Unit testing GET /widgets

Let’s start with the easiest test case, a test for GET /widgets:

@Test
void testGetWidgets() throws Exception {
    List widgets = new ArrayList();
    widgets.add(new Widget(1L, "Widget 1", 1));
    widgets.add(new Widget(2L, "Widget 2", 1));
    widgets.add(new Widget(3L, "Widget 3", 1));

    when(widgetService.findAll()).thenReturn(widgets);

    mockMvc.perform(get("/widgets"))
            .andExpect(status().isOk())
            .andExpect(jsonPath("$.length()").value(3))
            .andExpect(jsonPath("$[0].id").value(1L))
            .andExpect(jsonPath("$[0].name").value("Widget 1"))
            .andExpect(jsonPath("$[0].version").value(1));
};

The testGetWidgets() method creates a list of three widgets and then configures the mock WidgetService to return the list when its findAll() method is called. The WidgetControllerTest class statically imports the org.mockito.Mockito.when() method that accepts a method call, which in this case is widgetService.findAll(), and returns a Mockito OngoingStubbing instance. This OngoingStubbing instance exposes methods like thenReturn(), thenThrow(), thenCallRealMethod(), thenAnswer(), and then().

Here, we use the thenReturn() method to tell Mockito to return the list of widgets when the WidgetService’s findAll() method is called. The @MockitoBean annotation causes the mock WidgetService to be autowired into the WidgetController. So, when the getWidgets() method is called in response to a GET /widgets, it calls the WidgetService’s findAll() method and returns our list of widgets as a web response.

Next, we use MockMvc’s perform() method to execute a web request. This diagram shows the various classes that interact with the perform() method:

Steven Haines

The perform() method accepts a RequestBuilder. Spring defines several built-in RequestBuilders that we can statically import into our tests, including get(), post(), put(), and delete(). The perform() method returns a ResultActions instance that exposes methods such as andExpect(), andExpectAll(), andDo(), and andReturn(). Here, we invoke the andExpect() method, which accepts a ResultMatcher.

A ResultMatcher defines a match() method that throws an AssertionError if the assertion fails. Spring defines several ResultMatchers that we can statically import:

• status() allows us to check the HTTP status code of response.
• content() allows us to check the content headers of the response, such as Content-Type.
• header() allows us to check any of the HTTP header values.
• jsonPath() allows us to inspect the contents of a JSON document.

After MockMvc performs a GET to /widgets, we expect the HTTP status code to be 200 OK. We can then use the jsonPath matcher to check the body results, using the following JSON path expressions:

• $.length(): The $ references the root of the JSON document. If the response is a list, then we can call the length() method to get the number of elements in the list.
• $[0].id: JSON path expressions for a list use an array syntax starting at 0. This expression gets the ID of the first element in the list.
• $[0].name: This expression gets the name of the first element and compares it to “Widget 1”.
• $[0].version: This expression gets the version of the first element and compares it to 1.

Unit testing the GET /widget/{id} handler

Here’s the source code to test the GET /coffee/{id} widget:

@Test
void testGetWidgetById() throws Exception {
    Widget widget = new Widget(1L, "My Widget", 1);          
    when(widgetService.findById(1L))
           .thenReturn(Optional.of(widget));

    mockMvc.perform(get("/widget/{id}", 1))
            // Validate that we get a 200 OK Response Code
            .andExpect(status().isOk())

            // Validate Headers
            .andExpect(content()
                      .contentType(MediaType.APPLICATION_JSON))
            .andExpect(header().string(HttpHeaders.LOCATION,
                                       "/widget/1"))
            .andExpect(header().string(HttpHeaders.ETAG, "\"1\""))

            // Validate content
            .andExpect(jsonPath("$.id").value(1L))
            .andExpect(jsonPath("$.name").value("My Widget"))
            .andExpect(jsonPath("$.version").value(1));
 }

This test method is very similar to the testGetWidgets() method, but with some notable changes:

• The GET URI is defined using a URI template. You can specify any number of variables enclosed in braces in the URI template and then send a list of arguments that will replace those variables in the order they appear in the template.
• We check that the returned Content-Type is “application/json”, which is a constant in the MediaType class. We access the content using the content() method, which returns a ContentResultMatchers instance that provides various methods, including contentType(), which allows us to validate the content headers.
• We check for specific header values using the header() method. The header() method returns a HeadersResultMatchers instance, which can check for header String, long, and date values, as well as checking to see whether or not specific headers exist. In this case, we use constants defined in the HttpHeaders class to check the location and eTag header values.
• We check the body of the response using JSON path expressions. In this case, we do not have a list of objects, so we can access the individual fields in the JSON document directly. For example, $.id retrieves the id field value in the root of the document.

Unit testing a GET /widget/{id} Not Found code

Next, we test the GET /widget/{id}, passing it an invalid ID so that it returns a 404 Not Found response code:

@Test
void testGetWidgetByIdNotFound() throws Exception {
   when(widgetService.findById(1L)).thenReturn(Optional.empty());

   mockMvc.perform(get("/widget/{id}", 1))
            // Validate that we get a 404 Not Found Response Code
            .andExpect(status().isNotFound());
}

The testGetWidgetByIdNotFound() method configures the mock WidgetService to return Optional.empty() when its findById() is called with a value of 1. We then perform a GET request to /widget/1, then assert that the returned HTTP status code is 404 Not Found.

Unit testing POST /widgets

Here’s how to test a Widget creation:

@Test
void testCreateWidget() throws Exception {
    Widget widget = new Widget(1L, "Widget 1", 1);
    when(widgetService.create(any())).thenReturn(widget);

    mockMvc.perform(post("/widgets")
            .contentType(MediaType.APPLICATION_JSON)
            .content("{\"name\": \"Widget 1\"}"))

            // Validate that we get a 201 Created Response Code
            .andExpect(status().isCreated())

            // Validate Headers
            .andExpect(content().contentType(
                                      MediaType.APPLICATION_JSON))
            .andExpect(header().string(HttpHeaders.LOCATION, 
                                       "/widget/1"))
            .andExpect(header().string(HttpHeaders.ETAG, "\"1\""))

            // Validate content
            .andExpect(jsonPath("$.id").value(1L))
            .andExpect(jsonPath("$.name").value("Widget 1"))
            .andExpect(jsonPath("$.version").value(1));

The testCreateWidget() method first creates a Widget to return when the WidgetService’s create() method is called with any argument. The any() matcher matches any argument and, because the createWidget() handler will create a new Widget instance, we will not have access to that instance when the test runs. We then invoke MockMvc’s perform() method to the ”/widgets” URI, sending the content body of a new widget named “Widget 1”, using the content() method. We expect a 201 Created HTTP response code, an “application/json” content type, a location header of “/widget/1”, and an eTag value of the String “1”. The body of the response should match the Widget we returned from the create() method, namely an ID of 1, a name of “Widget 1”, and a version of 1.

Unit testing PUT /widget

This code runs three tests for the PUT operation:

@Test
public void testSuccessfulUpdate() throws Exception {
    // Create a mock Widget when the WidgetService's findById(1L) 
    // is called
    Widget mockWidget = new Widget(1L, "Widget 1", 5);
    when(widgetService.findById(1L))
                      .thenReturn(Optional.of(mockWidget));

    // Create a mock Coffee that is returned when the 
    // CoffeeController saves the Coffee to the database
    Widget savedWidget = new Widget(1L, "Updated Widget 1", 6);
    when(widgetService.save(any())).thenReturn(savedWidget);

    // Execute a PUT /widget/1 with a matching version: 5
    mockMvc.perform(put("/widget/{id}", 1L)
                    .contentType(MediaType.APPLICATION_JSON)
                    .header(HttpHeaders.IF_MATCH, 5)
                    .content("{\"id\": 1, " +
                             "\"name\": \"Updated Widget 1\"}"))

            // Validate that we get a 200 OK HTTP Response
           .andExpect(status().isOk())

            // Validate the headers
           .andExpect(content()
                        .contentType(MediaType.APPLICATION_JSON))
           .andExpect(header().string(HttpHeaders.LOCATION, 
                                      "/widget/1"))
           .andExpect(header().string(HttpHeaders.ETAG, "\"6\""))

           // Validate the contents of the response
           .andExpect(jsonPath("$.id").value(1L))
           .andExpect(jsonPath("$.name")
                               .value("Updated Widget 1"))
           .andExpect(jsonPath("$.version").value(6));
}

@Test
public void testUpdateConflict() throws Exception {
   // Create a mock coffee with a version set to 5
   Widget mockWidget = new Widget(1L, "Widget 1", 5);

    // Return the mock Coffee when the CoffeeService's 
    // findById(1L) is called
    when(widgetService.findById(1L))
                      .thenReturn(Optional.of(mockWidget));

    // Execute a PUT /widget/1 with a mismatched version number: 2
    mockMvc.perform(put("/widget/{id}", 1L)
                    .contentType(MediaType.APPLICATION_JSON)
                    .header(HttpHeaders.IF_MATCH, 2)
                    .content("{\"id\": 1, " + 
                             "\"name\":  \"Updated Widget 1\"}"))
             // Validate that we get a 409 Conflict HTTP Response
            .andExpect(status().isConflict());
}

@Test
public void testUpdateNotFound() throws Exception {
   // Return the mock Coffee when the CoffeeService's 
   // findById(1L) is called
   when(widgetService.findById(1L)).thenReturn(Optional.empty());

   // Execute a PUT /coffee/1 with a mismatched version number: 2
   mockMvc.perform(put("/widget/{id}", 1L)
                    .contentType(MediaType.APPLICATION_JSON)
                    .header(HttpHeaders.IF_MATCH, 2)
                    .content("{\"id\": 1, " + 
                             "\"name\":  \"Updated Coffee 1\"}"))

           // Validate that we get 404 Not Found
           .andExpect(status().isNotFound());
}

We have three variations:

• A successful update.
• A failed update because of a version conflict.
• A failed update because the widget was not found.

In RESTful web services, version management is handled by the entity tag, or eTag. When you retrieve an entity, it has an eTag value. When you want to update the entity, you pass that eTag value in the If-Match HTTP header. If the If-Match header does not match the current eTag, which is the Widget version in our implementation, then the PUT handler returns a 409 Conflict HTTP response code. If you get this error, it means that you need to retrieve the entity again and retry your operation. This way, if two different clients attempt to update the same entity simultaneously, only one will succeed.

In the testSuccessfulUpdate() method, we return a Widget with a version of 5 when the WidgetService’s findById() method is called. We then pass an If-Match header value of 5 and then validate that we get a 200 OK HTTP response code and the expected header and body values. In the testUpdateConflict() method, we do the same thing, but we set the If-Match header to 2, which does not match 5, so we validate that we get a 409 Conflict HTTP response code. And finally, in the testUpdateNotFound() method, we configure the WidgetService to return an Optional.empty() when its findById() method is called, so we execute the PUT operation and validate that we get a 404 Not Found HTTP response code.

Unit testing DELETE /widget

Finally, here is the source code for our two DELETE /widget tests:

@Test
void testDeleteSuccess() throws Exception {
    // Setup mocked product
    Widget mockWidget = new Widget(1L, "Widget 1", 5);

    // Setup the mocked service
    when(widgetService.findById(1L))
                      .thenReturn(Optional.of(mockWidget));
    doNothing().when(widgetService).deleteById(1L);

    // Execute our DELETE request
    mockMvc.perform(delete("/widget/{id}", 1L))
            .andExpect(status().isOk());
}

@Test
void testDeleteNotFound() throws Exception {
    // Setup the mocked service
    when(widgetService.findById(1L)).thenReturn(Optional.empty());

    // Execute our DELETE request
    mockMvc.perform(delete("/widget/{id}", 1L))
            .andExpect(status().isNotFound());
}

The DELETE handler first tries to find the widget by ID and then calls the WidgetService’s deleteById() method. The testDeleteSuccess() method configures the WidgetService to return a mock Widget when the findById() method is called and then configures it to do nothing when the deleteById() method is called. The deleteById() method returns void, so we do not need to mock a response, though we do want to allow the method to be called. We execute the DELETE operation and validate that we receive a 200 OK HTTP response code. The testDeleteNotFound() method configures the WidgetService to return Optional.empty() when its findById() method is called. We execute the DELETE operation and validate that we receive a 404 Not Found HTTP response code.

At this point, we have a comprehensive set of tests for all of our controller operations. Let’s continue down our stack and test our service.

Unit testing a Spring MVC service

Next, we’ll test a WidgetService class, shown here:

package com.infoworld.widgetservice.service;

import java.util.List;
import java.util.Optional;

import com.infoworld.widgetservice.model.Widget;
import com.infoworld.widgetservice.repository.WidgetRepository;

import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;

@Service
public class WidgetService {
    @Autowired
    private WidgetRepository widgetRepository;

    public List findAll() {
        return widgetRepository.findAll();
    }

    public Optional findById(Long id) {
        return widgetRepository.findById(id);
    }

    public Widget create(Widget widget) {
        widget.setVersion(1);
        return widgetRepository.save(widget);
    }

    public Widget save(Widget widget) {
        return widgetRepository.save(widget);
    }

    public void deleteById(Long id) {
        widgetRepository.deleteById(id);
    }
}

The WidgetService is very simple. It autowires in a WidgetRepository and then delegates almost all its functionality to the WidgetRepository. The only business logic it implements is that it sets the Widget version to 1 in the create() method, when it is persisting a new Widget to the database.

While Spring supports slice testing for our controller and (as you’ll soon see) our repository, it doesn’t have a slice testing annotation for our service. We could use the @SpringBootTest annotation, but then Spring would load all the controllers, repositories, and any other Spring resources in our application into the Spring application context. We can avoid by using Mockito directly.

Here is the source code for the WidgetServiceTest class:

package com.infoworld.widgetservice.service;

import static org.junit.jupiter.api.Assertions.assertEquals;
import static org.junit.jupiter.api.Assertions.assertTrue;
import static org.mockito.Mockito.when;

import java.util.Optional;

import com.infoworld.widgetservice.model.Widget;
import com.infoworld.widgetservice.repository.WidgetRepository;

import org.junit.jupiter.api.Test;
import org.junit.jupiter.api.extension.ExtendWith;
import org.mockito.InjectMocks;
import org.mockito.Mock;
import org.mockito.junit.jupiter.MockitoExtension;

@ExtendWith(MockitoExtension.class)
public class WidgetServiceTest {
    @Mock
    private WidgetRepository repository;

    @InjectMocks
    private WidgetService service;

    @Test
    void testFindById() {
        Widget widget = new Widget(1L, "My Widget", 1);
        when(repository.findById(1L)).thenReturn(Optional.of(widget));

        Optional w = service.findById(1L);
        assertTrue(w.isPresent());
        assertEquals(1L, w.get().getId());
        assertEquals("My Widget", w.get().getName());
        assertEquals(1, w.get().getVersion());
    }
}

JUnit 5 supports extensions and Mockito has defined a test extension that we can access through the @ExtendWith annotation. This extension allows Mockito to read our class, find objects to mock, and inject mocks into other classes. The WidgetServiceTest tells Mockito to create a mock WidgetRepository, by annotating it with the @Mock annotation, and then to inject that mock into the WidgetService, using the @InjectMocks annotation. The result is that we have a WidgetService that we can test and it will have a mock WidgetRepository that we can configure for our test cases.

Also see: Advanced unit testing with JUnit 5, Mockito, and Hamcrest.

This is not a comprehensive test, but it should get you started. It has a single method, testFindById(), that demonstrates how to test a service method. It creates a mock Widget instance and then uses the Mockito when() method, just as we used in the controller test, to configure the WidgetRepository to return an Optional of that Widget when its findById() method is called. Then it invokes the WidgetService’s findById() method and validates that the mock Widget is returned.

Slice testing a Spring Data JPA repository

Next, we’ll slice test our JPA repository (WidgetRepository.java), shown here:

package com.infoworld.widgetservice.repository;

import java.util.List;
import com.infoworld.widgetservice.model.Widget;
import org.springframework.data.jpa.repository.JpaRepository;

public interface WidgetRepository extends JpaRepository {
    List findByName(String name);
}

The WidgetRepository is a Spring Data JPA repository, which means that we define the interface and Spring generates the implementation. It extends the JpaRepository interface, which accepts two arguments:

• The type of entity that it persists, namely a Widget.
• The type of primary key, which in this case is a Long.

It generates common CRUD method implementations for us to create, update, delete, and find widgets, and then we can define our own query methods using a specific naming convention. For example, we define a findByName() method that returns a List of Widgets. Because “name” is a field in our Widget entity, Spring will generate a query that finds all widgets with the specified name.

Here is our WidgetRepositoryTest class:

package com.infoworld.widgetservice.repository;

import static org.junit.jupiter.api.Assertions.assertEquals;
import static org.junit.jupiter.api.Assertions.assertNotNull;
import static org.junit.jupiter.api.Assertions.assertNull;

import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;

import com.infoworld.widgetservice.model.Widget;

import org.junit.jupiter.api.AfterEach;
import org.junit.jupiter.api.BeforeEach;
import org.junit.jupiter.api.Test;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.autoconfigure.orm.jpa.DataJpaTest;
import org.springframework.boot.test.autoconfigure.orm.jpa.TestEntityManager;

@DataJpaTest
public class WidgetRepositoryTest {
    @Autowired
    private TestEntityManager entityManager;

    @Autowired
    private WidgetRepository widgetRepository;

    private final List widgetIds = new ArrayList();
    private final List testWidgets = Arrays.asList(
            new Widget("Widget 1", 1),
            new Widget("Widget 2", 1),
            new Widget("Widget 3", 1)
    );

    @BeforeEach
    void setup() {
        testWidgets.forEach(widget -> {
            entityManager.persist(widget);
            widgetIds.add((Long)entityManager.getId(widget));
        });
        entityManager.flush();
    }

    @AfterEach
    void teardown() {
        widgetIds.forEach(id -> {
            Widget widget = entityManager.find(Widget.class, id);
            if (widget != null) {
                entityManager.remove(widget);
            }
        });
        widgetIds.clear();
    }

    @Test
    void testFindAll() {
        List widgetList = widgetRepository.findAll();
        assertEquals(3, widgetList.size());
    }

    @Test
    void testFindById() {
        Widget widget = widgetRepository.findById(
                               widgetIds.getFirst()).orElse(null);

        assertNotNull(widget);
        assertEquals(widgetIds.getFirst(), widget.getId());
        assertEquals("Widget 1", widget.getName());
        assertEquals(1, widget.getVersion());
    }

    @Test
    void testFindByIdNotFound() {
        Widget widget = widgetRepository.findById(
            widgetIds.getFirst() + testWidgets.size()).orElse(null);
        assertNull(widget);
    }

    @Test
    void testCreateWidget() {
        Widget widget = new Widget("New Widget", 1);
        Widget insertedWidget = widgetRepository.save(widget);

        assertNotNull(insertedWidget);
        assertEquals("New Widget", insertedWidget.getName());
        assertEquals(1, insertedWidget.getVersion());
        widgetIds.add(insertedWidget.getId());
    }

    @Test
    void testFindByName() {
        List found = widgetRepository.findByName("Widget 2");
        assertEquals(1, found.size(), "Expected to find 1 Widget");

        Widget widget = found.getFirst();
        assertEquals("Widget 2", widget.getName());
        assertEquals(1, widget.getVersion());
    }
}

The WidgetRepositoryTest class is annotated with the @DataJpaTest annotation, which is a slice-testing annotation that loads repositories and entities into the Spring application context and creates a TestEntityManager that we can autowire into our test class. The TestEntityManager allows us to perform database operations outside of our repository so that we can set up and tear down our test scenarios.

In the WidgetRepositoryTest class, we autowire in both our WidgetRepository and TestEntityManager. Then, we define a setup() method that is annotated with JUnit’s @BeforeEach annotation, so it will be executed before each test case runs. Next, we define a teardown() method that is annotated with JUnit’s @AfterEach annotation, so it will be executed after each test completes. The class defines a testWidgets list that contains three test widgets and then the setup() method inserts those into the database using the TestEntityManager’s persist() method. After it inserts each widget, it saves the automatically generated ID so that we can reference it in our tests. Finally, after persisting the widgets, it flushes them to the database by calling the TestEntityManager’s flush() method. The teardown() method iterates over all Widget IDs, finds the Widget using the TestEntityManager’s find() method, and, if it is found, removes it from the database. Finally, it clears the widget ID list so that the setup() method can rebuild it for the next test. (Note that the TestEntityManager removes entities directly; it does not have a remove by ID method, so we first have to find each Widget and then remove them one-by-one.)

Even though most of the methods being tested are autogenerated and well tested, I wanted to demonstrate how to write several kinds of tests. The only method that we really need to test is the findByName() method because that is the only custom method we define. For example, if we were to define the method as findByNam() instead of findByName(), then the method would not work, so it is definitely worth testing.

Conclusion

Spring provides robust support for testing each layer of a Spring MVC application. In this article, we reviewed how to test controllers, using MockMvc; services, using the JUnit Mockito extension; and repositories, using the Spring TestEntityManager. We also reviewed slice testing as a strategy to reduce testing resource utilization and minimize the time required to execute tests. Slice testing is implemented in Spring using the @WebMvcTest and @DataJpaTest annotations. I hope these examples have given you everything you need to feel comfortable writing robust tests for your Spring MVC applications.

Despite many worthy contenders, React remains the most popular front-end framework, and a key player in the JavaScript development landscape. React is the quintessential reactive engine, continually innovating alongside the rest of the industry. A flagship open source project at Facebook, React is now part of Meta Open Source. For developers new to JavaScript and web development, this tutorial will get you started with this vital technology.

React is not only a front-end framework, but is a component in full-stack frameworks like Next.js. Newer additions like React server-side rendering (SSR) and React server components (RSC) further blur the line between server and client.

Also see: Is the React compiler ready for primetime?

Why React?

React’s prominence makes it an obvious choice for developers just starting out with web development. It is often chosen for its ability to offer a smooth and encompassing developer experience (DX), which distinguishes it from frameworks like Vue, Angular, and Svelte. It could be said that React’s true “killer feature” is the perks that come with longstanding popularity: learning resources, community support, libraries, and developers are all plentiful in the React ecosystem.

Installing React

Real-world React requires running on the server with a build tool, which we will explore in the next section. But to get your feet wet, we can start out with an online playground. There are several high-quality playgrounds for React, including full-blown environments like StackBlitz or Codesandbox. For a quick taste, we will use PlayCode React.

When you first open it, PlayCode React gives you a basic layout like the one shown here:

Matthew Tyson

The menu on the left is the file explorer, at the top is the code window, and at the bottom are the console (on the left) and the preview pane (on the right).

From this screenshot, you can see how the content of the code is displayed on the preview pane, but this basic layout doesn’t use any variables (or “state,” as it’s known in React). It does let you see some of the plumbing, like the React library import and the exported App function.

Modern React is functional. The App function has a return value that is the actual output for the component. The component’s return is specified by JSX, a templating language that lets you use HTML along with variables and JavaScript expressions. Right now, the app just has some simple markup.

The classic example you see next is a “Counter” that lets you increase and decrease a displayed value using buttons. We’ll do a slight “Spinal Tap” variation of this, where the counter only goes to 11 and displays a message:

Matthew Tyson

You can take a look at the running example here, and the full code for the example is below:

import React, { useState } from 'react';

export function App() {
  // 1. The State
  const [volume, setVolume] = useState(0);

  return (
    

      
Spinal Tap Amp 🎸
     
      {/* 2. The "View" (Displaying the state) */}
      

        {volume}
      

      

        {/* 3. The Actions */
         setVolume(volume - 1)}>Down
       
         {
          if (volume 
          Up
        
      

      {/* 4. Conditional */}
      {volume === 11 &&
        
"Why don't you just make ten louder?"
      }
    
  );
}

If you play with the example, you’ll see that moving the buttons changes the value, and the display automatically reflects the change. This is the essential magic of a reactive engine like React. The state is a managed variable that React automatically updates and displays. State is declared like so:

const [volume, setVolume] = useState(0);

The syntax is a bit funky if you are coming from straight JavaScript, but most developers can adapt to it quickly. Basically, useState(0) says, with a default value 0, give me a variable, volume, and a function to set it, setVolume.

To display the value in the view, we use: {volume}.

To modify the value, we use button event handlers. For example, to increment, we’d do:

To modify the value, we use buttons event handlers.  For example, to increment:

onClick={() => setVolume(volume + 1)

Here we’ve directly modified the volume state, and React will update accordingly. If we wanted to, we could call a function (for example, if the logic were more involved).

Finally, when the value reaches 11, we display a message. This syntax is idiomatic React, and uses an embedded JavaScript equality check:

{volume === 11 &&
  
"Why don't you just make ten louder?"
}

The check says, if volume is 11, then display the

Using a build tool with React

Once upon a time, when NVIDIA was nothing but a graphics card, it was quite a bit of work assembling a good build chain for React. These days, the process is much simpler, and the once ubiquitous create-react-app option is no more. Vite is now the standard choice for launching a new app from the React terminal, so that’s the approach you’ll learn here.

With that said, there are a few alternatives worth mentioning. VS Code has extensions that will provide you with templates or scaffolding, but what’s becoming more common is using an AI coding assistant. A tool like Copilot, ChatGPT, or Gemini can take a prompt describing the basics of the application in question, including the instruction to use React, and produce a basic React layout for you. AI assistants are available in both command-line and VS Code extension flavors. Or, for an even more forward-looking option, you could use something like Firebase Studio.

But enough about alternatives—Vite is the standard for a reason. It is repeatable, capable, and fast. To launch a new Vite app, you just enter the following in your command line:

$ npm create vite@latest

The interactive tool will walk you through the process, starting with selecting React as your technology:

Matthew Tyson

Use your own preferences for the other options (like using TypeScript versus JavaScript) and accept the option to install and launch the app immediately. Afterward, you’ll see a simple demo like this one:

Matthew Tyson

The demo app has a counter component like the one we built earlier. If you Ctrl-c (or Cmd-c) to kill the Vite process running in the terminal, you can cd into the new directory. From there, you can see where the counter component is defined, in src/App.jsx (or App.tsx if you have selected TypeScript like I have).

It’s worth looking at that file to see how React appears on the server:

src/App.tsx
import { useState } from 'react'
import reactLogo from './assets/react.svg'
import viteLogo from '/vite.svg'
import './App.css'

function App() {
  const [count, setCount] = useState(0)

  return (
    
      

        
          
        
        
          
        
      
      
Vite + React
      

         setCount((count) => count + 1)}>
          count is {count}
        
        

          Edit src/App.tsx and save to test HMR
        
      
      

        Click on the Vite and React logos to learn more
      
    >
  )
}

export default App</code>

Notice we export the App as a module, which is used by the src/main.tsx file to display the component in the view. That file creates the bridge between the respective worlds of React and HTML:

import { StrictMode } from 'react'
import { createRoot } from 'react-dom/client'
import './index.css'
import App from './App.tsx'

createRoot(document.getElementById('root')!).render(
  
    
  ,
)

Don’t worry too much about the details of how React bootstraps itself with createRoot and the render call (which you won’t have to interact with on a regular basis). The important thing is how the App component is imported and then used with the JSX.

Note

Strict mode adds warning during dev mode to help you catch component bugs early.

There are a few rules to bear in mind when using JSX, the templating language of React:

• HTML elements are lowercase (
  , ), but components are uppercase (, ).
• You can’t just type “class” in JSX; instead, use className; e.g.,
  .
• To access the realm of JavaScript (and the application state) from within JSX, use curly braces: {2 + 2 != 5}.

React components and props

The main organizational concept in React is the component. Components are used to contain the functionality for a part of the view within a self-contained package. We’ve seen a component in action already with but it might be a little obscure, so let’s add another simple component to enhance the demonstration. This component also lets us explore another key part of React: Props.

To start, let’s create a display of the counter value influenced by the Rob Reiner movie This Is Spinal Tap. To start, we create a new file at src/VolumeDisplay.jsx:

// src/VolumeDisplay.jsx

export function VolumeDisplay({ level }) {
  return (
    

      {/* The Dial */}
      
= 11 ? '#d32f2f' : '#f0f0f0',
        color: level >= 11 ? 'white' : 'black',
        transition: 'all 0.2s ease'
      }}>
        {level}
      

      {/* The Message */}
      {level >= 11 && (
        

          "These go to eleven." 🤘
        
      )}
    
  );
}

This is a simple display but there are a couple of things worth noting about it.

One is that we accept a prop (a property) “from above” with VolumeDisplay({ level }). This tells whatever parent component uses this one that VolumeDisplay accepts a single property, called level. VolumeDisplay uses the property by displaying it (though it adds a bit of fancying up using conditional logic like we have already seen).

The way we define the CSS values, inside the double braces, {{ }}, and as a map of value is idiomatic React. (It isn’t essential at this point to grasp why it works that way, but basically, it is the JSX token { } with a JavaScript map of CSS values using JavaScript-friendly camel-cased names, like justifyContent.)

Now, to utilize this component, we can go to App.jsx, and make two changes:

import { useState } from 'react'
import reactLogo from './assets/react.svg'
import viteLogo from '/vite.svg'
import './App.css'
// 1. Import our new component
import { VolumeDisplay } from './VolumeDisplay'

function App() {
  const [count, setCount] = useState(0)

  return (
    
      

        
          
        
        
          
        
      
      
Vite + React
      

         setCount((count) => count + 1)}>
          count is {count}
        
        {/* 2. Pass the 'count' state into the 'level' prop */}
      
        

          Edit src/App.tsx and save to test HMR
        
      
      

        Click on the Vite and React logos to learn more
      
    >
  )
}

export default App</code>

Here, we’ve done two things: imported the new component and used it in the view.

Notice, also, that the line passes the existing count state variable into VolumeDisplay as a prop. React will do the work of ensuring that whenever count changes, the VolumeDisplay will also be updated, including any dependent logic such as the conditional statements.

Now, if we run the code like so:

$ npm run dev

We get what you see in the screenshot below:



Matthew Tyson

Conclusion

The world is now your oyster, at least within the realm of JavaScript web development. Not only is React wildly popular, its basic ideas are applicable to a host of other innovative frameworks, including Svelte and Solid. (To get some idea of the alternatives, just type npm create vite@latest and look at all the available technologies.) Now that you have a basic introduction, a good next step for learning would be to add an control that allows typing in the volume manually. Happy coding!

Security leaders have made strong progress in visibility. Most organizations can now identify vulnerabilities across their applications, dependencies and development pipelines with far more consistency than in the past. Yet a fundamental imbalance remains: Vulnerabilities are being discovered faster than they can be remediated.

That imbalance is growing. Today, 82% of organizations carry security debt, defined as accumulated vulnerabilities that have remained unresolved for more than a year. At the same time, the share of vulnerabilities defined as both “severe” and “likely to be exploited” continues to increase.

This combination has real consequences. Vulnerabilities are not just accumulating; they persist in production environments long enough to be discovered and used.

Among my fellow CISOs, the conversation has shifted. The challenge now is to translate this reality into a business case that resonates with executive leadership and drives investment in remediation capacity. Here are six ways to do this.

Treat security debt like financial debt

Security debt behaves much like financial debt. It accumulates over time, compounds when left unmanaged and creates ongoing costs for the business. Those costs show up in delayed releases, emergency remediation efforts, audit findings and incident response.

Managing it effectively requires the same discipline applied to financial risk. That means measuring total and critical debt, setting reduction targets and tracking progress over time. It also means distinguishing between acceptable and unacceptable levels of risk, rather than treating all vulnerabilities as equal.

I believe security debt should be visible at the executive level. Leadership teams routinely track financial performance, operational resilience and service reliability. Security debt belongs in the same category. It reflects the organization’s exposure and its ability to manage that exposure over time.

Frame remediation capacity as a business constraint

Most organizations have a strong awareness of vulnerabilities. The limiting factor is the ability to address them.

Remediation capacity determines whether security debt grows or shrinks. When the volume of new findings exceeds the organization’s ability to fix them, the backlog expands and exposure increases. This dynamic persists regardless of how effective detection tools are.

In my experience, it’s important to quantify this constraint. That includes showing the gap between findings and fixes, identifying where high-risk vulnerabilities remain open and demonstrating how long they persist. These data points make it clear that incremental efficiency improvements will not close the gap on their own.

Presenting remediation capacity in operational terms helps align the discussion with executive priorities. Leaders understand constraints in engineering throughput, cloud spend and service availability. Remediation capacity should be treated in the same way.

Focus on exploitable risk in critical systems

Security debt becomes meaningful when it is tied to business impact.

Not all vulnerabilities carry the same level of risk. The ones that matter most share two characteristics. They are likely to be exploited, and they exist in applications that are important to the business.

Traditional severity scoring does not fully capture this. The Common Vulnerability Scoring System (CVSS) remains useful. Still, it does not reflect whether a vulnerability is reachable, whether it sits in a critical system or whether exploit techniques are readily available.

A practical approach is to layer exploitability and business context onto existing scoring models. This creates a focused set of high-risk vulnerabilities that require immediate attention. In many environments, this represents a relatively small percentage of total findings, but it accounts for a large portion of potential impact.

By concentrating on this subset, organizations can direct resources where they have the greatest effect. This approach also makes it easier to communicate risks in business terms.

Prioritize crown-jewel applications

Risk is not distributed evenly across applications.

Every organization has systems that are more critical than others. These may include customer-facing platforms, revenue-generating services or applications that process sensitive data. Compromise in these areas has a disproportionate impact on the business.

Focusing remediation efforts on these crown-jewel applications improves outcomes quickly. Our research found that 11.3% of flaws have high severity and high exploitability. It ensures that the most important systems receive the highest level of protection and reduces the likelihood of high-impact incidents.

Clear targets help reinforce this focus. Over a defined period, organizations can reduce critical security debt, shorten the lifespan of high-risk vulnerabilities and maintain strict thresholds for exposure in key systems. These targets translate security activity into business outcomes that leadership can understand and support.

Establish metrics that reflect risk

Metrics play a central role in shaping behavior.

Many organizations continue to rely on the number of vulnerabilities discovered or resolved. While these metrics provide useful context, they do not indicate whether risk is increasing or decreasing.

More effective measures focus on exposure. These include the number of critical or exploitable vulnerabilities in key systems, the average age of those vulnerabilities and trends over time. Together, these metrics provide a clearer picture of how risk is evolving.

Linking these measures to organizational objectives strengthens accountability. Security debt reduction can be incorporated into OKRs, with specific targets for reducing critical debt, lowering vulnerability age and maintaining acceptable thresholds in high-risk applications.

Formalizing risk acceptance is also important. High-risk vulnerabilities that remain open should require business approval and defined timelines. This ensures that risk is acknowledged and managed deliberately.

Increase investment in remediation capacity

Improving security outcomes requires sustained investment in the ability to act.

Remediation capacity can be expanded in several ways. Organizations can allocate dedicated engineering time for security work, integrate remediation into development workflows and adopt automation to reduce manual effort. AI-assisted fixes and automated guidance can help teams address vulnerabilities more efficiently without disrupting development velocity.

Preventing new security debt is equally important. Policies such as requiring high-risk vulnerabilities to be resolved before release help limit the introduction of additional exposure. Over time, this reduces the overall burden on remediation teams.

These changes do not slow innovation. They create conditions for delivering software safely and consistently.

Align the business around risk reduction

Security debt affects more than the security function. It influences resilience, regulatory posture and the organization’s ability to deliver software with confidence.

CISOs play a central role in aligning stakeholders around this issue. By framing security debt in terms of business impact, capacity constraints and measurable outcomes, they can shift the conversation from technical backlog management to enterprise risk reduction.

This alignment is critical for securing investment. When leadership understands the relationship between remediation capacity and business risk, decisions about funding, prioritization and trade-offs become clearer.

Security debt will continue to exist. What matters is how effectively it is managed and measured. For example, a good target should be doubling fix capacity through tooling investment, not just headcount.

Organizations that measure, govern and actively invest in reducing it are better positioned to control risk at scale. Those that do not will continue to see exposure grow, even as their visibility improves.

This article is published as part of the Foundry Expert Contributor Network.
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