How to run AI governance at scale
Find answers to the AI governance questions your team faces. These guides give compliance officers, lawyers, GRC teams, and risk managers the steps to put each control in place.
Compare AI governance tools by control domain when you need software. Use the AI governance maturity model to assess your program.
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How do we ensure third-party AI vendors meet our standards?
Review AI vendors for model transparency, data handling, bias testing, and contractual liability for their outputs.
What does meaningful human oversight look like for high-risk AI decisions?
Define human review responsibilities and the level of oversight required. Keep records showing how reviewers meet those requirements.
How do we handle AI-generated content and hallucinations?
Assign responsibility for inaccurate AI outputs used in contracts, reports, or customer communications. Set controls to prevent harm.
How do we maintain data privacy compliance when using AI?
Review training data sources, data minimization, cross-border transfers, and applicable explanation duties under GDPR and the California Consumer Privacy Act (CCPA).
How are we managing third-party AI risks?
Govern external AI APIs and models embedded in vendor software. Review data handling, documentation, and ongoing monitoring.
Do we have a complete AI inventory?
Maintain a registry of AI tools, including shadow AI found through procurement reviews, network checks, and employee surveys.
Is our AI red-teaming rigorous enough?
Set pass/fail criteria for testing high-risk AI before deployment. Cover toxicity, data leakage, jailbreaking, and misuse.
How do we govern AI agents that take autonomous actions?
Govern agents that browse websites, execute code, send messages, and use external services. Set controls for their autonomous actions.
How do we apply a three lines of defense model to AI risk?
Adapt the three lines of defense to AI. Assign business ownership, risk oversight, and independent assurance responsibilities.
What does audit-ready AI documentation look like in practice?
Keep evidence of responsible AI development, deployment, and monitoring. Prepare for regulatory scrutiny, board questions, and litigation throughout the lifecycle.
What does AI governance look like for a company with under 50 employees?
Build a startup governance program around essential protections. Keep the process manageable for an early-stage team.
How do we perform an AI risk assessment?
Assess data sensitivity, decision impact, and regulatory exposure before deployment. Match controls to each system’s risk level.
What AI regulations apply to a US-based SaaS company?
Identify federal, state, and international AI requirements for a US SaaS business. Map obligations by use case and customer location.
How do we build an AI governance program from scratch?
Start an AI governance program with a system inventory. Follow the steps through to ongoing operations.
How do we manage third-party AI vendors safely throughout the vendor lifecycle?
Manage AI vendors from due diligence through monitoring. Review data handling, contractual protections, and the response to model changes.
What do we do when an AI system causes harm or fails?
Respond to AI failures through detection, containment, investigation, and regulatory notification. Use findings to prevent recurrence.
How do we prepare for AI regulation over the next 12 months?
Identify regulations becoming enforceable in your jurisdictions over the next year. Assess gaps and fund a remediation plan.
How do we report AI risk to the board and audit committee?
Choose what AI risks to report to the board and how often. Set thresholds for urgent notification between scheduled reports.
How do we intake and govern open-weight and self-hosted AI models?
Review open-weight models before downloading, fine-tuning, or self-hosting them. Set deployment and maintenance controls suited to those responsibilities.
How do we map AI compliance obligations across multiple jurisdictions?
Map overlapping AI obligations across jurisdictions and resolve conflicts. Build a coordinated program around the strictest applicable requirements.
How do we monitor voluntary AI safety commitments and respond when they change?
Track AI vendors’ voluntary safety commitments. Reassess relationships when providers weaken, abandon, or fail to honor those commitments.
How do we govern our AI supply chain and manage upstream model dependencies?
Review upstream AI dependencies, including foundation models, datasets, development tools, and compute infrastructure. Treat them as supply chain components.
How do we govern agentic coding assistants and AI developer tools?
Review coding assistants and AI developer tools before approval. Examine codebase access, data boundaries, and separate transmission and retention settings.
How do we audit our AI governance program, not just individual AI systems?
Audit the governance program against maturity levels. Define what internal audit should test and which evidence supports each rating.