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For years, the gap between identifying an exposure and neutralizing it before attackers could act was a known problem. Mythos, and the frontier models that will follow, made it existential. They collapse time-to-exploit from weeks to hours, and transform low and medium severity vulnerabilities that sit low on most priority lists into high-impact attack paths. The exposures you've been deprioritizing may now be the ones that breach you. Kai was built to close that gap.
When Mythos findings land, enterprise security teams face an impossible math problem: millions of findings, a shrinking window to act, and workflows built for a world where attackers moved in weeks, not hours.
Kai was built for exactly this moment. It takes on the investigation, detection, and remediation work that no human team can execute at the speed Mythos demands, so your team can neutralize risk before attackers have a chance to act.
In a recent Fortune 500 deployment, Kai triaged and investigated 3 million SCA findings in under an hour, confirmed 99% as false positives, and provided one-click remediation guidance for the remainder. In another, Kai analyzed 200,000 SAST findings across 3,500 code repositories and eliminated 65% as false positives in under two hours.
THREE WAYS KAI CLOSES THE GAP
01
Verifying real exposure with AI-native risk prioritization
Not every Mythos finding is exploitable in your environment. Kai determines which vulnerabilities are truly reachable and exploitable, then uses AI-native prioritization to rank them by actual risk, so your team's effort is always focused on the exposures that matter most.
02
Generating detection rules automatically
While remediation is underway, Kai creates and deploys detection rules to identify exploitation attempts in real time.
03
Delivering streamlined, one-click remediation plans
Least-invasive and fully reasoned, with accurate asset owner and remediation owner assignments, and ready for engineering teams to act on immediately or use as the foundation for future auto-remediation.
Mythos didn't create the coverage gap between machine-speed offense and human-speed defense. It made that gap impossible to ignore. Security teams are now asked to respond at a speed and scale their tools and workflows were never designed to handle.
Millions of vulnerability findings that no team can process. False positive rates that bury real risk. Remediation timelines measured in weeks against attackers moving in hours. And a fragmented stack that was already failing before AI-enabled threats arrived.
WHERE HUMAN-SPEED DEFENSE BREAKS DOWN
Remediation speed
Mythos surfaces critical, genuinely exploitable vulnerabilities that demand immediate action.
Collapsing time window
The gap between discovery and weaponization is now hours. Patch cycles were built for weeks.1
Analyst capacity
A skilled analyst investigates 10-20 vulnerabilities per day. AI-generated findings number in the millions.
False positive overload
50-99% of findings are false positives. Without intelligent triage, real risks are buried in noise.
Fragmented stack
Point tools generate findings but don't investigate them. Context is scattered, handoffs create delays.
Incomplete asset context
CMDBs are 4-50% complete on ownership. You cannot remediate what you cannot attribute.
1 CSA CISO Community, SANS, "The AI Vulnerability Storm: Building a Mythos-ready Security Program," April 2026. Time-to-exploit now measured in hours based on Zero Day Clock data.
Claude Mythos is Anthropic's frontier AI model that autonomously discovers critical software vulnerabilities at a scale and speed no prior capability has matched. Its release marks a structural shift in the offense-defense balance for every enterprise security team.
Mythos is capable of finding thousands of zero-days across every major OS and browser, chaining multi-primitive exploits without human guidance, and operating at a 72% exploit success rate.2 The window between discovery and weaponization has collapsed to hours. Current patch cycles, response processes, and risk models were not built for this environment. And Mythos-class capabilities will proliferate fast.
2 Anthropic, "Claude Mythos Preview & Project Glasswing," April 2026. Based on internal lab environment testing across Firefox exploit generation.
The Mythos capability timeline
April 2026
Anthropic releases Claude Mythos Preview and launches Project Glasswing — the largest coordinated vulnerability disclosure effort in history.
The exploit success rate
72% exploit success rate across Firefox testing.¹ 181 working exploits generated where the previous best achieved two under the same conditions.
The gap it opened
Mean time to exploit collapses to 20 hours in 2026, down from 2.3 years in 2018.² Current patch cycles, risk models, and response processes were not built for this environment.
What comes next
Mythos-class capabilities expected in open-weight models within months. This is the first wave, not the last.
