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A practical AI governance framework for financial institutions
The core elements of AI governance: ownership, risk tiering, validation, monitoring and audit trails.
10/9/2026
Governance is what allows AI to move from pilot to production in a regulated organisation. Done well, it speeds adoption by giving everyone clear rules.
Core elements 1. Ownership. Every AI system has a named business owner and a named technical owner. 2. Inventory. Keep a register of all AI use cases, including those embedded in vendor tools. 3. Risk tiering. Classify use cases by impact. A document summariser needs less control than a system influencing credit decisions. 4. Validation. Independent testing before launch, with documented limits and intended use. 5. Human oversight. Define where a person reviews or approves. 6. Monitoring. Track performance, drift, incidents and user complaints after launch. 7. Audit trail. Log inputs, outputs, versions and decisions. 8. Data and security. Control what data models can see and where it goes.
Frameworks to know. Many institutions map their approach to existing model risk guidance (for example, SR 11-7 in the US), the NIST AI Risk Management Framework, ISO/IEC 42001, and, where relevant, the EU AI Act. Confirm which apply to you.
Tip. Extend your existing model risk process rather than creating a separate one. It's faster and easier for regulators to follow.