AI Governance Assessment Guide
An AI governance assessment checks whether the organization knows what AI it uses, who owns it, how it is approved, and how decisions are reviewed and improved over time.
What to assess
AI governance is the structure that decides who can use AI, who can approve it, what evidence is required, and how risk is reviewed.
| Governance area | Assessment question | Evidence to collect |
|---|---|---|
| Ownership | Is a responsible owner named for each AI use case? | RACI, org chart, ticket history |
| Policy | Are AI acceptable-use and security rules documented? | Policies, standards, exceptions |
| Inventory | Does the organization know what AI systems and tools exist? | Inventory export, intake register |
| Approvals | Are higher-risk uses reviewed before launch? | Approvals, risk acceptance notes |
| Lifecycle | Are changes, renewals, and retirements tracked? | Change records, retirement plans |
| Third parties | Are vendor terms and data use reviewed? | Vendor risk reviews, DPAs |
Governance and security are related but not identical
AI governance sets the rules and accountability structure. AI security implements the technical and operational controls that reduce risk.
A program can have policy language without strong technical controls, or strong controls without clear ownership. An effective assessment should look at both.
Common governance gaps
Common gaps include no clear owner, no AI inventory, inconsistent exception handling, and weak third-party review.
- Business teams start AI use cases without a formal intake path.
- Policies exist but are not translated into operational controls.
- High-risk vendors are not reviewed consistently.
- No routine monitoring of policy exceptions or risk acceptance.
Next step
Guides explain what good AI security looks like. SecureAIScore helps you measure where your organization stands.