Author: Saeed Abbasi, Senior Manager, Threat Research Unit, Qualys
With Time-to-Exploit now at negative seven days and autonomous AI agents accelerating threats, the data no longer supports incremental improvement. The architecture of defense must change.
What Leaders Need to Know
Analysis of CISA’s Known Exploited Vulnerabilities over the past four years shows critical vulnerabilities still open at Day 7 worsened from 56% to 63% despite teams closing 6.5x more tickets. Staffing cannot solve this.
Of the 52 tracked weaponized vulnerabilities in our study, 88% were patched more slowly than they were exploited — half were weaponized before any patch existed.
The problem is not speed. It is the operational model itself.
Cumulative exposure, not CVE counts, is the true risk metric that security teams now need to measure. While dashboards reward the sprint to get patches implemented, breaches exploit the tail. AI is not another attack surface — instead, the transition period where AI-powered attackers face human defenders is the industry’s most dangerous window.
In response, defenders have to implement their own autonomous, closed-loop risk operations.
The Broken Physics
New research from the Qualys Threat Research Unit, analyzing more than one billion CISA KEV remediation records from across 10,000 organizations over four years, quantifies what the industry has long suspected but never proved at scale. The operational model underpinning enterprise security is broken.
Vulnerability volumes have grown 6.5 times since 2022. According to Google M-Trends 2026, the average Time-to-Exploit has collapsed to negative seven days; in other words, adversaries are weaponizing the most serious vulnerabilities before patches exist. The percentage of critical vulnerabilities still open at seven days has climbed from 56 percent to 63 percent.
Yet this is not for lack of effort. Organizations closed 400 million more vulnerability events annually now than they did at baseline. Teams work harder, but it fails to make the difference where it counts. Our researchers call this the “human ceiling” — a structural limit no amount of staffing or process maturity can overcome. The constraint is not effort. It is the model itself.
Of 52 high-profile weaponized vulnerabilities tracked with complete exploitation timelines, 88 percent were remediated slower than they were exploited. As an example, Spring4Shell was exploited two days before disclosure, yet the average enterprise needed 266 days to remediate.
Similarly, the flaw in Cisco IOS XE was weaponized a month early; average close was 263 days.
The attacker’s advantage was measured in days. The defender’s response was measured in seasons. This is not an intelligence failure. It is an operationalization failure.
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The Manual Tax and Risk Mass
The report identifies a “Manual Tax” — the multiplier effect where long-tail assets that human processes cannot reach drag exposure from weeks into months. For Spring4Shell, average remediation was 5.4 times the median.
The median tells a manageable story. The average tells the truth. Infrastructure systems face a harsher reality: for Cisco IOS XE, even the median was 232 days — compared to endpoint medians consistently under 14. When the best-case outcome is eight months, the Manual Tax is no longer a multiplier. It is the baseline.
Looking at average figures is no longer helpful for decision-making. Instead, looking at Risk Mass — vulnerable assets multiplied by days exposed — captures what CVE counts obscure around cumulative exposure. A companion metric, Average Window of Exposure (AWE), measures the full duration from weaponization to remediation across the environment.
As an example, Follina was weaponized 30 days before disclosure with an average close at Day 55.
However, the AWE stretched to 85 days. While the blind spot before disclosure accounted for 36 percent of that 85 days, the long tail of patching accounted for a further 44 percent. In total, pre-disclosure and long tail together represent 80 percent. The sprint that gets measured makes up less than 20.
At the same time, of 48,172 vulnerabilities disclosed in 2025, only 357 were remotely exploitable and actively weaponized. Organizations are burning remediation cycles on theoretical exposure while genuinely exploitable gaps persist.
Why the Gap Will Widen
Cybersecurity has long operated as a derivative of technology shifts — Windows security followed Windows, cloud security followed cloud. Leading practitioners and investors now argue AI breaks that pattern. It is not merely a new surface to defend; it is a fundamental transformation of the adversary itself.
Offensive agents can already discover, weaponize, and execute faster than any human-staffed operation can respond. The remediation data proves humans cannot keep pace today. Autonomous AI ensures the gap will accelerate tomorrow.
The transition period — where AI-powered attackers face human-speed defenders — represents the industry’s most dangerous window, compounded by the structural vulnerabilities that dominate the near term: attack surfaces expanded beyond what teams can govern, identity sprawl that outpaces policy, and remediation workflows still built on manual execution.
The traditional scan-and-report model was built for lower volumes of CVEs and longer exploit timelines. What replaces it is an end-to-end Risk Operations Center: embedded intelligence arriving as machine-readable decision logic, active confirmation validating whether a vulnerability is actually exploitable in a specific environment, and autonomous action compressing response to the timescale the threat demands.
The objective is not to eliminate human judgment but to elevate it, shifting practitioners from tactical execution to governing the policies that direct their own autonomous systems.
The organizations already winning the physics gap are not winning with larger teams. They are winning because they have removed human latency from the critical path.
How Security Teams can close the Risk Gap
The scan-and-report model — discover, score, ticket, manually route — was built for lower volumes and longer exploit timelines.
What replaces it is an end-to-end Risk Operations Center: embedded intelligence arriving as machine-readable decision logic, active confirmation validating whether a vulnerability is actually exploitable in a specific environment, and autonomous action compressing response to the timescale the threat demands.
The objective is not to eliminate human judgment but to elevate it — shifting practitioners from tactical execution to governing the policies that direct autonomous systems. The organizations already winning the physics gap are not winning with larger teams. They are winning because they have removed human latency from the critical path.
Time-to-Exploit will not return to positive numbers. Vulnerability volume will not plateau. The reactive model has hit a hard mathematical ceiling.
The only remaining question is whether organizations will use the architecture to match the mathematics — before the window between human-scale defense and autonomous-scale offense closes for good.
Contact Qualys for insights into how companies manage remediation at scale with automation and AI, and how you can make that difference right now.
Sponsored and written by Qualys.
