Governance vs. compliance: the distinction that matters
Compliance means meeting a specific external requirement. Governance means having the policies, processes, and accountability structures to manage AI risk as a continuous operational discipline. You can be compliant without being governed — and compliant organizations still have major AI incidents.
The five pillars of AI governance
Policy (what is and isn't permitted), Visibility (what AI is in use and what it is doing), Controls (technical enforcement of policy), Accountability (who owns what and who escalates what), and Measurement (how you know the program is working). A gap in any pillar undermines the others.
Standing up the AI governance committee
Effective AI governance committees include representatives from security, legal/compliance, engineering, business units, and executive leadership. The committee owns the AI use policy, approves new AI deployments, reviews incident reports, and signs off on compliance attestations. It needs a clear charter and a defined meeting cadence.
The policy structure that actually works
Start with a single-page acceptable use policy that every employee can understand. Layer underneath it a technical policy document that specifies data handling requirements for each classification tier. Add a third layer of tool-specific policies for your highest-risk applications. This three-layer structure is maintainable and auditable.
Measuring governance maturity
The metrics that indicate a maturing program: time from policy decision to technical enforcement (should be days, not weeks), percentage of AI interactions covered by monitoring, number of policy violations detected vs. self-reported, and the speed and quality of your incident response when something goes wrong.