AI Governance
Enterprises are giving agents access to real systems faster than anyone can define what those agents are allowed to do.
The question nobody can answer in one sentence.
What are your agents allowed to do? In most organisations the honest answer is that it depends which team built them, and nobody has written it down.
Authorization at the boundary
Every tool an agent reaches for is a decision, scoped per agent, per tool and per action. Anything unrecognised is refused rather than allowed.
Sensitive data protection
Personal and regulated data kept out of prompts, responses and telemetry alike, so nothing leaks through the path nobody watches.
Injection defense
Manipulation attempts identified and scored, so genuine attacks stop while real work keeps flowing.
Evidence for review
A complete record of what was allowed, what was refused and why. Written to answer an auditor, not to fill a dashboard.
Why this, and why now.
Agents changed the risk
A chatbot that answers questions is a content problem. An agent that can call your systems is an access-control problem, and most organisations are governing the second with tooling built for the first.
Controls are being written by hand
Every team building agents is reinventing authorization, redaction and logging, slightly differently, with no shared standard and no way to prove coverage across the estate.
The evidence question is coming
Whatever framework applies to you, the request will be the same. Show what the system was permitted to do, and prove it behaved that way. Very few teams can produce that today.
Grounded in frameworks you already use
Our controls map to the OWASP Top 10 for LLM Applications and the NIST AI Risk Management Framework, so this fits reporting you already do and does not add a parallel process.
What are your agents allowed to do?
If the answer takes more than a sentence, that is the conversation. Thirty minutes, no deck.