Your AI Red Teamer Found 200 Vulnerabilities. Prove One.
Offensive agents are good at producing findings. The hard part is proving what happened, and holding clean results to the same standard as positive ones.
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From Evidence Production to Assertion Validation
A control can be documented, evidenced and tested, and still not be true as stated. The useful AI problem in compliance is testing whether the full claim holds.
Not a Cleaner Backlog. A Shorter Exposure Window.
Egnyte cut median time to mitigate critical open-source vulnerabilities with Endor Labs. Not by pushing engineers to work faster or auto-closing unreachable findings, but by surfacing ownership and exposure evidence earlier. Next: agents that remove the remaining latency.
AI Security Agents Fail When You Build Six Products at Once
Discovery, validation, containment, recall, cost control, and patching are different problems. Treating them as one is how agentic security projects die early.
From Vulnerability Triage to Exposure Validation
AppSec credibility is shifting from finding more issues to proving what materially changes exposure: exploitability, tenant impact, failed assumptions, and what engineering should do next.
In Legacy Codebases, AppSec AI Is Mostly a Context Problem
In large legacy codebases, AI AppSec quality depends less on finding a smarter model and more on extracting enough structure and context to avoid shallow analysis.