Overview
Regulated industries cannot treat generative AI as an unsanctioned experiment. They need inventory, risk classification, approval gates, and continuous monitoring—without freezing innovation.
Introduction
Governance fails when it is only a policy PDF. Build a lightweight operating model: who owns use cases, how risk is scored, what evidence is required to go live, and how incidents are handled.
Use-case inventory
Catalog every AI system—vendor SaaS, internal RAG, copilots, and agents. Capture data classes touched, human oversight level, and business owner. You cannot govern what you cannot see.
Risk tiers
Tier by impact: informational assist, decision support, and automated action. Higher tiers need stronger evaluation, legal review, and human-in-the-loop controls.
Model and vendor diligence
Assess residency, subprocessors, training-data policies, and breach SLAs. Prefer private networking and customer-managed keys when data sensitivity demands it.
Ongoing controls
Monitor drift in quality metrics, unusual export patterns, and prompt-injection attempts. Schedule periodic re-attestation as models and prompts change.
Key Takeaways
Good governance accelerates safe delivery. Teams move faster when the approval path is clear—and when “no” comes with an actionable remediation path.