How to run AI governance at scale
A practical guide for compliance officers, general counsel, GRC teams, and risk managers navigating the operational realities of enterprise AI governance. Questions every compliance team needs to answer.
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What applies to me? →How do we detect and mitigate algorithmic bias?
Testing protocols and audit trails for AI used in hiring, lending, or customer decisions, to defend against discrimination claims.
How do we handle AI-generated content and hallucinations?
Defining responsibility when AI produces inaccurate outputs used in contracts, reports, or customer communications, and the controls that prevent harm.
Is our AI red-teaming rigorous enough?
Defining pass/fail criteria for adversarial testing of high-risk AI systems before deployment, covering toxicity, data leakage, jailbreaking, and misuse scenarios.
How do we govern AI agents that take autonomous actions?
Agentic AI systems that can browse the web, execute code, send messages, and interact with external services require governance controls that traditional policy frameworks were never designed to handle.
What do we do when an AI system causes harm or fails?
A structured incident response process for AI failures — from initial detection through containment, root cause investigation, regulatory notification, and prevention.
How do we govern AI models from preview release through retirement?
A lifecycle governance framework covering every stage of an AI model's production life — from evaluating preview releases, through controlled promotion to general availability, to scheduled re-assessment triggers and formal retirement.
New guidance, every week
We publish practical guidance as governance questions come up in the field — plus everything else changing in AI regulation. Every Thursday.