Model Governance
Definition
Processes, roles and documentation that steer the lifecycle of an AI model in a company — from selection through validation, sign-off, monitoring and re-approval to decommissioning. Encompasses responsibilities, eval requirements, risk classification, versioning, audit trails and escalation paths.
Noise — Signal
Model governance is often summarised as an "AI governance framework" on a single slide — typically a diagram with arrows between roles such as "AI Lead", "Risk Owner", "Compliance Officer". Substantive governance isn't the diagram, it's the answer to concrete questions: who is allowed to deploy which model for which use case? At which risk level does which escalation kick in? Which eval thresholds are binding? Who monitors model drift in live operation, and what triggers a rollback? Without documented answers to these questions, model governance is a PowerPoint artefact that does not carry weight in audits.
The right question
Not: "Do we need a model governance framework?" But: "Which of the concrete lifecycle decisions — model selection, sign-off, drift detection, re-approval, decommissioning — are today assigned by name to a role, with documented thresholds and a traceable decision history?"