Every AI agent you deploy carries business-specific risk. Our adversarial agents read your real exposure — data, tools, customers, policies — and generate attacks that match. No boilerplate scripts. No generic CVE match. The real risk, calculated against the real agent.
AAV replaces predefined playbooks with agentic adversaries that think about your environment the way your actual attackers do.
Detection, response, and asset management are mature. What nobody owned: a continuous, autonomous proof of what an attacker could actually do with what you have — today.
Most security tooling lives in the top row: mature, but operationally noisy. AAV is the new leader quadrant — mature enough to run in production, strategic enough to reframe what “risk” means.
Same goal, different era. Scripts don’t learn. Libraries don’t pivot. Scheduled tests don’t match continuous exposure.
Only surface findings tied to real, reachable exploits.
Every risk is validated end-to-end, not inferred.
Business-impact correlation replaces raw CVSS noise.
AI agents think like attackers; reports talk to defenders.
Always-on purple teaming, not point-in-time testing.
Measure which controls actually stop real attack chains.
The AAV thesis has been shaping up across every major analyst shop. We’re just the first to productize it end-to-end.
