AI Governance Best Practices
Good AI governance is less about volume of policy and more about a few practices done consistently. These are the ones that reliably reduce risk and satisfy customers and regulators.
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Keep a live AI inventory
List every AI system and use, its owner, its data, and its risk tier. Revisit quarterly. You cannot govern what you have not inventoried.
Publish an acceptable-use policy
One or two pages: what is allowed, what is prohibited, what data may never be entered, and who to ask. Make it readable and enforceable.
Classify risk, then match controls
Triage uses as low, medium, or high. Apply proportionate oversight — light for low, human review and testing for high. Do not apply enterprise controls to trivial uses.
Design human oversight
Decide where a human must review AI output before it affects people or money. Document who, when, and what they check.
Assess vendors as you would any supplier
Ask how they use your data, whether they train on it, their security posture, and their subprocessors. Keep the answers on file.
Monitor and rehearse incidents
Log AI incidents and near-misses, and know who acts when an AI output causes harm. A short incident playbook beats a long policy.
Review on a cadence
Set a quarterly review of the inventory, incidents, and policy. Governance that is not reviewed decays.
Frequently asked questions
What is the single most valuable AI governance practice?
A live, owned AI inventory. Almost every other practice depends on knowing what AI you actually use and where.
How do we prevent shadow AI?
Combine a clear acceptable-use policy with a sanctioned tool set, so using approved tools is easier than going around them.
How often should we review?
Quarterly is a practical default for smaller organizations, with ad-hoc review after any significant AI change or incident.