We started AI Security Engineer because we were tired of tools that stop at "here's what's wrong." A scanner that hands you 64 findings and walks away hasn't done the job — it's just moved the job to you. We wanted something that reads the output like a senior engineer would, tells you the three things that actually matter tonight, writes the fix, and proves it worked.
Make application security something a normal team can actually keep up with. Not by adding another dashboard to ignore, but by doing the tedious, high-judgement work — triage, prioritization, remediation, verification — so engineers can spend their time shipping instead of drowning in alerts.
Most breaches aren't exotic. They're a missing authorization check, a secret in git history, an expired certificate, a reused password. The fundamentals, missed at scale. We think the right tool relentlessly closes those gaps and tells you, in plain language, why each one matters.
A wall of findings is a to-do list nobody finishes. We surface the handful that actually matter and put the rest in their place.
Telling you something is broken is the easy half. We write the patch, apply it, and verify the fix held.
Detected secrets are never sent to the model. Passwords never leave our server in full. Least data, always.
Role-based access, per-project permissions, audit trails and onboarding — because security is a team sport.
Point us at a GitHub repo, a pull request, or upload a zip. Real scanners — Semgrep, Trivy, Gitleaks, Checkov — run against it.
Each finding is scored by real-world risk: is it internet-reachable, does it need auth, what does it expose?
Generate a concrete remediation, apply it, and re-scan to confirm the issue is actually gone — not just acknowledged.
Beyond code: external attack surface, credential-breach exposure, and daily reminders on what's still open.