Azul’s 2026 AI Vulnerability Gap Report Finds Enterprise Patching Lagging Behind AI Threats

Azul’s 2026 AI Vulnerability Gap Report finds a mismatch between the speed security leaders associate with AI-equipped attackers and the time their organizations need to deploy fixes. In a survey of 300 U.S. security leaders, 69% said attackers could build a working exploit within three days, while 44% said their organizations need four days or longer to patch a critical vulnerability across production.

The report’s most striking finding is the experience respondents associate with those delays: 85% reported an incident, suspected incident, or near miss attributable to slow patching. That figure describes reported experience, not an independently verified breach rate—and it does not establish that AI caused those events.

Commissioned by Azul and conducted by Censuswide, the research focuses on organizations with substantial Java deployments. Its broader message is operational: recognizing a vulnerability is only the start. A fix must survive testing, reach the affected systems, and close exposure across an estate that teams may not fully see.

Two Clocks That Do Not Line Up

The survey separates leaders’ expectations about attackers from their organizations’ own deployment capabilities:

• 69% believe AI-equipped attackers can build an exploit in three days or less, including 22% who put that window below 24 hours.
• 56% say they can deploy a critical patch across production within three days: 13% in under 24 hours and 43% in one to three days.
• 44% require longer: 32% need four to six days and 12% need more time.

Those are distributions of survey responses, rather than measurements of attackers racing specific organizations on the same vulnerabilities. The difference supports concern about readiness, but it should not be read as a precise three-day exposure window for every enterprise.

At the one-day threshold, the same imbalance appears: 22% expect attackers to move that quickly, compared with 13% who say their organization can deploy a fix within the window. Meanwhile, 59% acknowledge greater exposure from AI-powered attack tools without having changed their security posture.

Testing Is a Major Part of the Delay

Testing Java runtime updates adds four or more days to the patch cycle at 58% of surveyed organizations. That includes 36% reporting four to six days and 22% reporting a week or longer. Another 33% report one to three days, and 9% less than 24 hours. No respondent said testing was skipped.

Runtime changes can affect many applications, so testing has a practical purpose: a security update that disrupts a critical service creates another operational problem. The report argues that update scope contributes to that burden because full OpenJDK quarterly releases can include non-security changes alongside vulnerability fixes.

Azul separately assessed 26 quarterly OpenJDK updates over more than six years and classified six as causing a serious regression. That 23% figure is the vendor’s release assessment, not a survey estimate of how often an individual customer’s application will break.

Among the biggest reported barriers to faster patching, 44% cite extensive testing, 40% the complexity or age of affected systems, and 36% limited visibility into known vulnerabilities. Vendor patch delays were cited by 25%. The pattern places much of the remediation challenge inside the enterprise’s own deployment process.

Incident Reports and Inventory Gaps Need Different Readings

Fifty-five percent report a confirmed incident attributable to slow patching, and 53% report a near miss. Respondents could select multiple answers, so these figures overlap and should not be added together. The broader 85% headline also includes suspected incidents.

The survey does not establish that all those events involved Java vulnerabilities. Its participants work in Java-heavy organizations, but the research includes questions about software patching more broadly as well as questions specifically about Java runtimes.

Inventory is a second difficulty. Only 19% say at least 90% of their production Java runtime estate is on the latest security patch level. That means 81% fall below that threshold; it does not mean 81% have no current runtimes. About one in five say half or less of their estate is current.

Separately, 51% acknowledge partial or limited visibility into critical Java runtimes, including embedded, legacy, and unmanaged instances. Finding a vulnerable runtime inside a packaged application or container can be harder than identifying a centrally managed installation. A patch cannot close exposure on a system the deployment process never reaches.

What Azul’s Technology Adds to the Discussion

Azul’s security-update offering limits selected releases to security fixes on a previously stabilized OpenJDK baseline. The company provides quarterly, targeted monthly, and out-of-cycle updates, aiming to reduce the amount of unrelated change enterprises must evaluate when deploying an urgent fix.

That is a narrower technical intervention than removing the need for change control. Limiting code changes can reduce the scope of validation, while application-specific checks, deployment ownership, and rollback preparation still matter. Azul is the report’s sponsor and sells products addressing the problems it measures.

Its Java inventory and vulnerability tooling addresses the other side of the workflow: identifying runtimes, versions, and exposure so a new disclosure can be mapped to affected systems. The report does not provide a controlled comparison demonstrating how much the surveyed organizations would improve by adopting those products.

“Our research shows security leaders understand the threat: awareness is no longer the gap; execution is,” said Scott Sellers, Azul’s co-founder and CEO.

Turning Patching Intent Into a Measurable Process

For the next year, 32% identify faster and more frequent patching as their primary strategy for reducing exposure, while 26% favor better software and runtime inventory. Another 17% prioritize detection and response instead of faster patching.

The research suggests tracking the whole path from disclosure to remediation: which systems are affected, who owns them, what testing is required, and whether the fix actually reached production. A deadline is useful only when teams can verify completion across the relevant estate.

The U.S. findings come from fieldwork conducted August 3–7, 2026, among security leaders at organizations with at least 1,000 employees and significant Java/JVM production use. They are self-reported responses from a defined population, rather than a census of enterprises or direct measurement of exploit development.

The full 2026 AI Vulnerability Gap Report details the findings and methodology. Its clearest implication is that patch speed and runtime visibility need to improve together: a rapid fix has limited value if the organization cannot identify every affected deployment or confirm that the update reached it.