CAPTIVOLT INSIGHTS · Published · Updated · 3 min read
Executive summary
Listed and regulated enterprises need AI governance that produces evidence, not just policy. Effective governance starts with AI system inventory, risk classification, use-case intake, control ownership, monitoring, and leadership oversight. When the board, the auditor, or the regulator asks what AI is running and who owns its risk, a policy document is not an answer; a populated register is.
The problem
AI adoption in most enterprises has outrun oversight. Teams deploy copilots, vendors embed models into platforms, and business units experiment, while governance consists of a policy PDF written eighteen months ago. The gap becomes visible at the worst moments: an audit, a regulatory inquiry, a board question, or an incident. The question is never whether the policy exists; it is whether the organisation can produce evidence of control.
A practical framework
- 01
Start with inventory: a living register of every AI system, its owner, model, data sources, and integrations. You cannot govern what you have not counted.
- 02
Classify risk per system: privacy, safety, fairness, explainability, business impact. Proportionate controls follow classification.
- 03
Operate a use-case intake: new AI initiatives enter through a defined gate with defined questions, not through procurement side doors.
- 04
Assign control ownership: every control has a named owner, a cadence, and an evidence requirement.
- 05
Monitor in operation: governance that ends at approval has governed nothing; behaviour, drift, and incidents need ongoing oversight.
- 06
Report to leadership: a defined cadence that gives boards decision-grade visibility, mapped to frameworks such as ISO 42001 where relevant.