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 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.
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.
What is our process for model drift monitoring?
Assign owners and review schedules for deployed models. Monitor performance degradation, changes in behavior, and emerging bias.
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.
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 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 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 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 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 engage regulators and standards bodies proactively on AI governance?
Engage regulators through consultations, standards work, and direct dialogue. Use those channels to contribute to emerging AI governance requirements.
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 govern MCP servers and other agent tool connections?
Govern MCP servers and connectors that provide agents with tools and data. Review intake, scoped credentials, and separation between content and instructions.
What AI agent security controls do we need as agent autonomy expands?
Review security before expanding agent autonomy. Cover multi-agent trust, agent identities, emergency-stop testing, and governance approval.