A practical AI governance policy covers data boundaries, human oversight, logging, vendor risk, and model usage rules, tailored by workflow risk.
Minimum viable governance
Data classification plus a do-not-use list. An approval process for new use cases. A simple risk matrix that maps workflows to oversight levels.
This takes a day to draft, not a quarter. The goal isn't comprehensiveness. It's having a clear answer when someone asks "can I use AI for this?"
Operational controls
Role-based permissions for who can deploy and modify AI systems. Audit logs of all AI decisions and data access. Retention rules for prompts and outputs. An incident response process for when things go wrong.
These aren't bureaucratic overhead. They're the controls that let you scale AI confidently.
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Frequently asked questions
Do small teams need governance?
Yes. Lightweight governance is easier to start early than to retrofit after a breach or incident.
Who should own it?
A cross-functional owner (Product/IT) with Security/Legal input. Ownership must be explicit.