Introduction
Approaches and tools to assess risk, strengthen controls, and build confidence in AI systems and outcomes. The goal is not governance for its own sake. The goal is to make better decisions, strengthen accountability, and help AI deliver real value in the environments where it will actually be used.
1. Assurance means evidence in practice
Assurance is confidence supported by evidence that systems are governed, monitored, and operating as intended.
2. Start with evidence, not assumptions
Review documentation, testing results, operating records, user feedback, and real-world performance before drawing conclusions.
3. Match reviews to the level of risk
Higher-impact uses need deeper review, stronger evidence, and more rigorous oversight than lower-impact uses.
4. Build assurance into existing workflows
Integrate checks into governance, procurement, project management, implementation, and operational review processes.
5. Use findings to improve over time
Treat monitoring and review as inputs to better controls, clearer decisions, and stronger future deployments.