Model Governance

The policies, processes, and controls that manage AI models across their full operational lifecycle — from initial selection and approval through deployment, monitoring, versioning, and retirement. Model governance defines who can authorize a model for a given use case, how updates and new model versions are evaluated and promoted, what performance thresholds trigger a review or rollback, and who is accountable for model outputs in production. Without model governance, organizations accumulate unapproved models, lose track of which models are in use, and lack a process for handling degraded or harmful model behavior.

A fintech company implements model governance by requiring that any model touching credit decisions be approved by the risk committee, version-controlled in a model registry, and reviewed quarterly for performance drift.