Model Governance
Framework of policies, processes, and controls that ensure responsible development, deployment, and management of AI systems.
Definition
Model governance establishes organizational structures, approval workflows, monitoring protocols, and accountability measures to manage AI systems throughout their lifecycle from development to retirement.
This framework includes risk assessment procedures, performance monitoring requirements, update protocols, and incident response plans to ensure AI systems remain safe, effective, and compliant over time.
Why It Matters
Strong model governance reduces business risks associated with AI deployment, including regulatory violations, performance degradation, and unintended consequences that could harm customers or operations.
Governance frameworks enable scalable AI adoption by providing repeatable processes for evaluating, approving, and managing AI systems, supporting systematic digital transformation initiatives.
Examples in Practice
Banks implement model governance frameworks for AI-powered fraud detection systems, including regular performance reviews, bias testing, and approval processes for model updates to maintain regulatory compliance.
Healthcare organizations establish governance protocols for diagnostic AI tools, requiring clinical validation, ongoing monitoring, and clear escalation procedures to ensure patient safety and regulatory adherence.
Manufacturing companies create governance structures for predictive maintenance AI, including performance benchmarks, update approval workflows, and incident response procedures to maintain operational reliability.