AI Governance: Best Practices for Scalable AI Governance

Arizona is deciding how AI will be controlled, governed, and trusted before it scales across a federated environment.

This session will examine the control-layer choices you can make so you can scale AI as a force multiplier instead of a systemic risk.

- Assess ownership and accountability models for AI risk, decisions, and outcomes across agencies, including where human-in-the-loop review is required.
- Improve enforceability by connecting data inventory and classification to decision control, not just visibility.
- Reduce risk by establishing guardrails for bias management, model transparency, human-in-the-loop escalation, procurement, and auditability before scale.
- Advance operational governance by embedding controls into workflows, systems, and cross-agency collaboration.