Model Governance refers to the comprehensive framework for managing AI and machine learning models throughout their entire lifecycle. It ensures models are developed responsibly, deployed safely, monitored continuously, and retired appropriately.
Key aspects of model governance include: model inventory and registration (maintaining a catalog of all AI models in use), model risk assessment (evaluating potential harms before deployment), model validation (testing accuracy, fairness, and robustness), model documentation (recording design decisions, training data, and limitations), model monitoring (tracking performance drift, bias emergence, and unexpected behaviors), model versioning (managing updates and rollbacks), and model retirement (safely decommissioning outdated models).
For enterprises using third-party AI tools and APIs, model governance extends to vendor management: understanding which models power the tools employees use, tracking model version changes by providers, assessing the impact of model updates on business processes, and ensuring vendor models meet organizational standards.
Regulatory frameworks like the EU AI Act and industry standards like ISO 42001 increasingly require formal model governance processes, particularly for high-risk AI applications.
