Topic

Model Governance

Model governance refers to the frameworks and processes organizations use to manage the lifecycle of AI/ML models from development through deployment and retirement. It encompasses model registry, versioning, performance monitoring, documentation, and approval workflows to ensure models meet quality, compliance, and business standards. Effective model governance is critical for enterprises to maintain transparency, reduce risk, and demonstrate accountability to regulators and stakeholders.

1 item

Corporate PolicyGlobal2026-05-30

2026 International AI Safety Report Shifts Enterprise Risk Focus to Post-Deployment and Agentic Systems

IBM's analysis of the 2026 International AI Safety Report concludes that AI safety risks now primarily materialize after deployment, not during model development, as systems trigger business processes, access sensitive data, and make autonomous decisions. The report places heightened emphasis on agentic AI, where multi-step actions can proceed without human approval at each stage. Cybersecurity, access controls, change management, model governance, and real-time monitoring are identified as the compliance functions most directly implicated.

agentic AI deployment risk model governance access controls enterprise risk