Architecting AI Governance: Federated Models That Scale

AI governance presents an architectural challenge that centralized approaches cannot solve. Monolithic review processes create friction that development teams bypass; purely decentralized models produce inconsistency that regulators will not accept. The session presents a federated architecture for AI governance: central functions set policies and standards while domain functions retain authority to adapt implementation within defined thresholds for risk, financial impact, and infrastructure cost. This federated model rests on five components: policies and standards that define acceptable use; governance structures that distribute authority appropriately; risk assessment processes that scale from self-service questionnaires to expert review; technical infrastructure including model registries and automated policy enforcement; and monitoring systems that track drift, accuracy, and fairness over time. Each component must be designed for the distinctive nature of AI: algorithms that learn and change, rather than fixed logic that can be reviewed once and approved indefinitely. The session traces a maturity progression from experimentation through restricted deployment to production scale, with governance controls calibrated to each stage. Participants will examine how permission models, audit schedules, and oversight intensity can be tiered to match risk levels, avoiding both the paralysis of excessive control and the exposure of insufficient oversight. Key Takeaways: - Federated governance balances consistency with domain-appropriate flexibility. - Five components (policies, structure, risk assessment, infrastructure, monitoring) form a complete architecture. - Tiered permission and audit models match oversight intensity to risk level. - Maturity progression from experimentation to production requires different controls at each stage.