Model governance provides a structured framework for managing AI and machine learning models throughout their entire lifecycle, from design and development through deployment, monitoring, and retirement. It encompasses policies and procedures for model validation, performance monitoring, bias detection, explainability requirements, and change management, ensuring that models remain accurate, fair, and compliant with applicable regulations.
With the introduction of the EU AI Act and growing regulatory scrutiny of automated decision-making, model governance has become a board-level concern. Organisations deploying high-risk AI systems must demonstrate robust governance practices including model inventories, risk classifications, regular performance reviews, and clear accountability structures that define who is responsible for model outcomes.