A governance framework isn’t a document you write once. It’s a system you build — and you can start this week.
Step 1: Inventory everything
Start with a reliable view of every AI system, agent and use case — including shadow AI. You can’t govern what you can’t see. Record the owner, model, environment and data each system touches.
Step 2: Set direction and accountability
Define who owns each AI system and which decisions are theirs. Establish policies, standards and guidelines — and, critically, an approval path for exceptions. Accountability is the foundation everything else sits on.
Step 3: Classify risk
Score every system by business impact, data sensitivity, autonomy and exposure. Not all AI is equal: a support chatbot and an autonomous claims underwriter need very different treatment. Risk classification tells you where to focus.
Step 4: Enforce controls
Convert policy into safeguards: access and privilege controls, input and output validation, runtime enforcement. Controls should apply dynamically based on policy, risk and context — not as a static checklist.
Step 5: Observe, assure and improve
Monitor activity, detect anomalies, keep evidence, and feed what you learn back into policy. A framework that doesn’t improve is a framework that’s already stale.