A structured model outlining how AI governance components (risk management, accountability, oversight) fit together to ensure compliance and ethical use.
A blueprint that integrates risk-assessment methodologies, ethical principles, operational controls (e.g., versioning, monitoring), and reporting mechanisms into a cohesive system. It maps responsibilities to organizational units, defines KPI targets (bias incidents per quarter), and prescribes tools (dashboards, audit platforms) - providing a repeatable playbook for scaling AI governance across business lines.
A healthcare provider adopts a three-layer framework: (1) Board-level oversight defining strategy and risk appetite; (2) Program-level processes for impact assessments and audits; (3) Project-level templates and automated checks embedded in CI/CD pipelines - ensuring governance consistency from high-level policy to code commits.
