Governance teams often report inventories completed, assessments launched and policies published. These are useful activity measures, but they do not demonstrate that important AI risks are controlled.
1. Coverage metrics
Measure the share of relevant AI systems with a current owner, purpose, risk classification, connected data and tools, approved use and review date. Coverage should be segmented by business unit, geography, lifecycle stage and risk tier so gaps are visible.
2. Control-effectiveness metrics
Track whether expected controls are present and operating: approval gates applied, privileged tools restricted, sensitive-data rules enforced, high-impact actions reviewed and policy violations blocked or escalated.
A single governance score can hide weak coverage and uncertain evidence. Keep the underlying dimensions visible.
3. Remediation and verification
Time to triage and time to close matter, but closure should require verification. Measure recurrence, overdue high-risk findings, accepted residual risk and the proportion of fixes that produced a confirmed change in access, behaviour or policy outcome.
4. Evidence quality
Useful records connect the source signal, system, owner, policy version, decision, approver, action and verified final state. Buyers should test whether these records can be exported, searched and retained without losing context.
A compact executive view
- Known and governed AI coverage
- High-risk exposure by lifecycle stage
- Control failures and verified remediation
- Material changes awaiting reassessment
- Evidence completeness for critical decisions
Good metrics support action. If a measure cannot change prioritisation, ownership or control design, it may be reporting noise.