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 inventory and classify AI systems by risk level?
Catalog your AI tools, including shadow AI. Assess each system’s data sensitivity, decision impact, and regulatory exposure.
Who owns AI governance within the organization?
Assign AI governance responsibilities across Legal, IT, Risk, and any dedicated AI function. Document who approves decisions and handles escalations.
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 are our obligations under emerging AI regulations?
Map AI compliance requirements across EU rules, US executive orders, SEC guidance, and sector regulations.
How do we detect and mitigate algorithmic bias?
Test AI used in hiring, lending, and customer decisions. Keep audit trails that support your response to discrimination claims.
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 should employees be trained on acceptable AI use?
Set rules for approved tools, permitted input data, and AI-assisted work in regulated industries.
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 do we document AI decision-making for auditability?
Use logs, version control, and model cards to explain AI decisions when regulators or litigants request evidence.
How do we ensure human-in-the-loop review is actually effective?
Design oversight that counters automation bias. Give reviewers authority and practical ways to override AI decisions.
What is our explainability standard for AI decisions?
Match transparency requirements to system risk. Build the technical tools and review procedures needed to meet them.
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.
How does the EU AI Act affect our global operations?
Assess how EU requirements affect organizations outside Europe. Decide whether to use its risk-based approach as a global internal standard.
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.
How do we build and maintain an AI model registry?
Keep a model registry with production status, owners, training data, risk classifications, and review dates.
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.
How do we comply with the EU AI Act?
Work through EU AI Act classification, high-risk obligations, GPAI requirements, and phased enforcement dates.
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.
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 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 govern AI models from preview release through retirement?
Govern models from preview evaluation through production approval and retirement. Define reassessment triggers throughout their working life.
How do we disclose AI governance maturity to investors and regulators?
Prepare evidence of governance maturity for investors, regulators, and boards. Decide what to disclose and how to support each claim.
How do we build and maintain a multi-framework AI risk register?
Combine AI risks from the EU AI Act, NIST AI RMF, GDPR, ISO 42001, and sector rules. Retain framework-specific requirements.
How do we build director-level AI literacy for effective board oversight?
Give board directors enough AI knowledge to question management, assess risks, and carry out oversight without technical expertise.