How do you implement AI governance frameworks for enterprise organizations?

AI governance establishes accountability, oversight, and controls for AI development and deployment. Framework components: (1) Principles: define organizational AI principles (fairness, transparency, accountability, safety, privacy). (2) Policies: translate principles into actionable policies covering development standards, deployment requirements, monitoring obligations, and incident response. (3) Processes: risk assessment, approval workflows, monitoring, auditing, and decommissioning. (4) Roles: AI ethics board (oversight and policy), AI risk committee (risk assessment), model owners (accountable for specific models), and AI auditors (independent review). Risk assessment: classify AI applications by risk level (critical: impacts health, safety, legal rights, financial standing; high: significant business impact, uses personal data; medium: operational efficiency, limited personal data; low: internal tools, no personal data). High-risk applications require formal review, testing, and approval before deployment. Model lifecycle governance: development (documented design decisions, data provenance, bias testing), validation (independent testing against acceptance criteria), deployment (approval workflow, monitoring setup, rollback plan), monitoring (performance tracking, drift detection, fairness metrics), and retirement (documented decommissioning, data handling, stakeholder notification). Documentation: maintain model cards documenting purpose, training data, performance metrics, limitations, and intended use for every deployed model. Create datasheets documenting data sources, collection methods, known biases, and preprocessing steps. Audit capabilities: maintain complete lineage from data to deployed model, log all model decisions for high-risk applications, implement periodic independent audits, and generate compliance reports on demand. Maturity progression: start with inventory (catalog all AI in use), establish governance (policies and processes), implement automation (automated testing, monitoring, reporting), and achieve continuous improvement (feedback-driven optimization of governance processes).