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.
How do we measure and mitigate algorithmic bias?
Standardized metrics for testing whether a model unfairly discriminates against protected groups, and processes for remediation when bias is found.
What is our process for model drift monitoring?
Defining ownership and cadence for ongoing monitoring of deployed AI models to detect performance degradation, behavioral shifts, and emerging bias after deployment.
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 AI regulations apply to a US-based SaaS company?
Mapping the federal, state, and international AI regulatory requirements that apply to US SaaS companies offering AI features, based on use case and customer location.
How do we build an AI governance program from scratch?
A sequenced guide to standing up an AI governance program — from initial inventory through ongoing operations — for organizations that are starting with nothing.
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.
How do we disclose AI governance maturity to investors and regulators?
A framework for organizations facing investor, regulator, or board requests for evidence of AI governance maturity — covering what to disclose, how to structure it, and how to avoid common credibility traps.
How do we comply with China's AI regulations?
A compliance guide for organizations deploying AI systems accessible to users in China — covering the four-layer regulatory stack administered by the CAC, security assessment obligations, content labeling requirements, and the practical differences between China's framework and Western AI governance regimes.
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.