AI governance migration checklist for IT leaders

AI governance migrations often fail when seen purely as technical tasks, like moving APIs or model endpoints, without addressing underlying access models, thus merely relocating risks. This checklist for IT leaders outlines a structured approach to bringing unmanaged AI usage under control while maintaining development momentum. The process involves surfacing shadow AI realities by auditing unsanctioned AI usage, mapping data interactions, identifying broad access points, and establishing a baseline of costs. Refactoring governance includes standardizing identity, defining environment scopes, codifying guardrails, and setting "pause" criteria for high-risk workflows before migration. During migration, the focus shifts to automated boundary enforcement, validating workflows in isolated environments, and ensuring compliance checks through automated pipelines. The final phase operationalizes continuity by making governance an inheritable platform capability, transitioning to proactive monitoring, and ensuring auditor readiness. Upsun supports this roadmap by standardizing environments, facilitating GitOps workflows for auditable changes, and providing multi-cloud portability, thereby shifting governance from a policing role to a platform function.