Autonomous agents that pentest your AI product across every workspace, prove what one customer can reach of another's data, and verify the fix — the evidence your buyers' security review asks for.
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Agents sweep nightly, again after every deploy, and on demand when you want one thing looked at. What you shipped this morning is tested tonight rather than at the next review.
The unauthenticated surface is where most tools stop. Agents hold accounts in more than one workspace at once and work every pair between them, and where a login has no automatable path a human signs in once and they carry on from the session.
Your own agents are part of the attack surface. Injection through every input you accept — the chat box, an uploaded document, a fetched page, a tool result — and abuse of the functions you have given the model, judged on whether it reached data or an action it should not have.
Findings reach your engineers over MCP with the exploit chain as the specification, so the patch is written against evidence. Then replay re-runs the original exploit and records whether it still reproduces.
Every domain you own, the providers underneath, and the hosts nobody has thought about since they were created. Discovery finds them from outside, so a console stays on the map whether or not anyone remembers deploying it.
Nothing is recorded without the requests and identities that proved it.
Connect it over MCP and point it at the finding. The exploit is the spec.
It reproduces, or it does not. That is the answer you send the buyer.
Point agents at your product and see what crosses the workspace boundary. Every finding arrives with the request that proved it, which is what a buyer's security review is actually asking for.
Common questions from teams building AI products.