AI Safety Audit
Improve continuously
Every gap fixed, every release starts from a known state.
Step 1
What to fix, in the right order
Every gap comes with the agent's exact answer and concrete actions, ranked by severity.
What you gain
Your teams know right away what to fix first.
- The failed scenario and the agent's answer
- Up to three technical actions per criterion
- A ranking by severity
Step 2
A plan tracked in your teams' own tools
Each action has its owner and its status, tickets go straight to Jira or Linear, and your AI coding agents read the gaps and the recommended fixes directly through Mankinds' MCP server.
What you gain
Fixes move forward in your teams' usual workflow, right into their code editor.
- One owner and one status per action
- Jira or Linear tickets
- MCP server: Claude, Cursor or Codex read the gaps and the recommended actions
Step 3
At every release, proof that it's fixed
The next evaluation closes the gaps that are gone and reopens those that come back. In your CI pipeline, a release that regresses stops before production.
What you gain
Every release starts from a known state, and your report stays true.
- Attacks that succeeded are replayed
- GitHub Action or Docker image
- Each run compared with the previous one
A closed loop, from gap to proof
Mankinds evaluates, recommends and verifies; your teams decide and fix. Every fix keeps its record, from finding to verification, and feeds straight into your evidence report.
The questions we get asked
Do you fix the agent for us?
Your teams fix it; Mankinds tells them what to fix first, verifies the fix and keeps its record.
What happens when we change models?
The new release reruns the same tests: you see right away what improves and what regresses.
Does it fit our deployment pipeline?
Yes: the tests run in your CI pipeline, via GitHub Action or Docker image.