Federated AI risk management balances centralized policy-setting with decentralized execution, allowing business units to manage AI risks within their domains while adhering to enterprise-wide principles and controls. This model accommodates diverse AI use cases and rapid innovation while ensuring consistent governance. Central teams establish frameworks, tools, and escalation procedures while domain experts assess context-specific risks. For example, a bank's centralized AI governance team sets model validation standards while business units manage credit, fraud, and marketing AI applications with specialized knowledge.