AI model governance establishes accountability, transparency, and risk management practices across the AI lifecycle from development through decommissioning. Governance frameworks address data quality, model validation, bias testing, explainability requirements, human oversight, and change management. Financial services firms face regulatory expectations for AI governance under SR 11-7 and similar frameworks requiring model risk management. Effective governance includes model inventories, performance monitoring, version control, and incident response procedures for AI failures. Cross-functional governance boards typically oversee high-risk AI applications affecting customers or critical decisions.