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A governance model where central policy and oversight are paired with distributed decision-making by business units or regional offices, balancing consistency with operational flexibility.
Federated AI governance accepts that a single central team cannot scale to govern hundreds of AI systems across a global enterprise. The central function maintains the policy, the high-risk approval authority, and the audit-trail infrastructure; business unit or regional governance officers handle local intake, low-risk decisions, and contextual interpretation. The model only works if the central function maintains tight control over the policy itself - drift between business units is the primary failure mode.
Real world example:
A multinational employer with operations across 39 countries appoints a Global Responsible AI Officer who maintains the policy and high-risk review - with regional Responsible AI Officers handling local intake and approvals. This preserves consistency while scaling capacity.
