AI governance sounds heavy, but a workable framework is a short list of decisions made clearly. These steps suit most enterprises.
1. Set ownership
- Appoint a senior owner for AI governance.
- Name an owner for every AI system, responsible for its purpose, performance and risk.
- Create a small review group from data, security, legal and the business.
2. Inventory what you have
List every model and AI-enabled tool, including those bought from vendors and those individual teams adopted. You cannot govern what you cannot see.
3. Tier by risk
| Tier | Example | Controls |
|---|---|---|
| Low | Internal drafting aid with no sensitive data | Usage guidelines |
| Medium | Forecasting that informs decisions | Testing, documentation, periodic review |
| High | Decisions affecting customers, finance or health | Formal approval, bias and accuracy testing, human oversight, audit trail |
4. Control the data
Define what data each system may use, where it may run and how long outputs are kept. Record lineage so you can trace results back to sources.
5. Test before release
Test for accuracy, bias, robustness and privacy leakage. Keep the results as evidence for approvers.
6. Approve with evidence
Use a standard approval path with defined criteria, so decisions are consistent and fast.
7. Monitor in production
Track performance, drift and incidents. Define when a model must be retrained, paused or retired.
Keep it proportionate
Light controls for low-risk uses and rigorous ones for high-risk uses keep teams moving. Our Data & AI Governance team can help you build the framework and the supporting platform.
Frequently asked questions
What is an AI governance framework?
It is the set of roles, policies, controls and review steps that decide how AI systems are built, approved, used and monitored in an organization.
Who should own AI governance?
Accountability should sit with a named senior owner, supported by data, security, legal and business representatives, with an owner for each AI system.
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