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The questions below are a practical governance aid, not a universal legal checklist. Applicable duties depend on the organization, jurisdiction, its role in an AI system, and the system’s use.
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What should a board ask about AI risk?
Start with visibility and accountability. A board cannot oversee risks management has not identified. Ask management to explain how it finds and tracks AI use across the organization, including AI features embedded in vendor products and generative AI tools used by staff.
Confirm the mandate and owners
- Which board or committee oversees AI-related risks, and how is that work coordinated with audit, risk, technology, privacy, and compliance oversight?
- Which executive is accountable for the organization’s AI risk decisions? Who owns operational decisions for each material use?
- Who can approve a use, require changes, pause it, or retire it—and how are disagreements escalated?
Ask for a usable inventory
For each material AI system or use case, ask management to record:
- its intended purpose and business owner;
- who uses it and who may be affected by its output;
- where it sits in its lifecycle and whether it makes, supports, or does not influence decisions;
- its risk classification and the basis for that classification;
- material vendors, models, datasets, plugins, and other dependencies.
The inventory should distinguish approved uses from pilots and unapproved or prohibited uses. It should also make clear when a familiar product includes an AI feature that changes how information is processed or decisions are made.
How should directors oversee AI risk assessments?
Ask whether management assesses risks before deployment and revisits the assessment when the system, its use, affected people, or operating context changes. The assessment should consider potential harm to people, the organization, and society—not only technical failure or financial loss.
Rank #2
Test the substance of the assessment
- What could go wrong in the intended use, through foreseeable misuse, or when people rely on an output in an unexpected way?
- Which groups could be affected, and how are their perspectives or relevant stakeholder input considered?
- What evidence supports the assessment of data quality, validation, and system performance in the intended context?
- Are intended and unintended impacts documented, including effects of third-party components, plugins, and downstream use?
- What risks remain unresolved, who accepted them, and what conditions would trigger a change in that decision?
NIST’s Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1, released July 26, 2024) calls for documenting risks and potential impacts, including downstream effects such as third-party plugins. The EU AI Act’s Article 9 requires an iterative risk-management system for providers of covered high-risk AI systems, including identification and analysis of known and reasonably foreseeable risks. That statutory requirement has a defined scope; it is not a rule for every AI use or every organization.
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Ask management to connect each material risk to a control, a responsible owner, evidence that the control operates, and a route for responding when it fails. Controls should cover development or procurement, deployment, use, monitoring, and decisions to modify or retire a system.
Rank #3
Human oversight and system evaluation
- Which decisions are automated, which are supported by AI, and which remain for a person to make?
- Who can question, override, or stop an AI-assisted decision, and do they have enough information and authority to do so?
- What evaluations happen before deployment and during operation? What limitations are known, and how are they communicated to users?
- How does management test for inaccurate, unsafe, discriminatory, insecure, or otherwise harmful outputs in the context of the intended use?
Incidents, response, and recovery
- What counts as an AI-related incident or near miss, and who is responsible for identifying and recording it?
- What thresholds trigger escalation to executives, the board, regulators, customers, or other parties where required?
- Who leads response and recovery, and how does management decide to suspend, modify, or decommission a system?
- Does management review incidents and near misses to determine whether assessments, controls, training, or approved uses need to change?
NIST AI 600-1 identifies testing, incident identification, reporting, escalation, and information sharing as organizational practices for generative AI risk management. These are prompts for evidence and ownership, not a board-specific checklist prescribed by NIST.
What should directors ask about vendors and AI supply chains?
External systems and components can affect a use case’s risks even when the organization did not build the model. Ask management to identify material dependencies and explain how it obtains enough information to oversee them.
- Which models, vendors, datasets, plugins, and other external components are material to each use?
- Do contracts and operating processes provide the information, auditability, incident notice, and change notification the organization needs?
- Who reassesses risk when a vendor changes a model, service, data source, or terms of use?
- How does management account for downstream use by business units, users, or other systems?
- Are users trained on approved and prohibited uses, including how to handle sensitive information and verify outputs where appropriate?
NIST AI 600-1 addresses oversight across the generative AI lifecycle, including supply chains and downstream effects. ISO/IEC 23894:2023 provides guidance on integrating AI risk management into organizational activities and adapting it to context.
What belongs in regular AI reporting to the board?
Ask management for a repeatable report that highlights exceptions, changes, and trends—not simply a list of completed activities. A useful report can cover:
- material use cases and changes to the AI inventory;
- risk ratings, significant impacts, and unresolved high risks;
- control testing and monitoring findings, including overdue remediation;
- incidents, near misses, complaints, and response status;
- vendor or model changes that alter a use case’s risk;
- relevant legal or policy changes for the organization’s role and jurisdictions;
- decisions requiring board attention, with available options and accountable owners.
These are recommended reporting fields synthesized from governance and lifecycle risk-management guidance; they are not a board report mandated verbatim by a standard. Agree on a cadence suited to the organization’s risk profile, with prompt escalation for significant events rather than waiting for the next scheduled report.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should the board prioritize AI use cases?
When board attention is limited, compare use cases using consistent questions rather than an unsupported numeric score. Management can explain how it weighs the factors and what evidence informs each judgment.
- Potential harm: How severe and likely are the possible harms or losses?
- People affected: How many people may be affected, and are any groups especially vulnerable in this context?
- Decision importance: How consequential is the decision the AI informs or makes?
- Human intervention: How autonomous is the system, and can a person intervene effectively?
- Data and dependencies: How sensitive and reliable are the data, and how dependent is the use on a model or vendor?
- Misuse and reversibility: What foreseeable misuse is plausible, and can the decision or its effects be reversed?
- Readiness: Can management monitor the use and respond effectively to incidents?
- Legal scope: Which legal requirements apply given the organization’s role, the system classification, the use, and the jurisdiction?
These comparison factors reflect considerations in NIST guidance, ISO/IEC 23894:2023, and Article 9 of the EU AI Act. They are not a prescribed scoring scale. Any numeric weighting should come from an organization-specific method supported by evidence.
How do AI standards and the EU AI Act fit into oversight?
Standards and laws serve different purposes. They can help management structure governance and risk work, but they are not interchangeable, and adopting a framework does not by itself establish legal compliance.
| Source | What it covers | How a board can use it |
|---|---|---|
| ISO/IEC 38507:2022 | Guidance on governance implications of organizational AI use, directed to members of governing bodies. | A direct reference for framing governing-body oversight questions; it does not prescribe this article’s checklist. |
| ISO/IEC 42001:2023 | Requirements for establishing, implementing, maintaining, and continually improving an AI management system, using a Plan-Do-Check-Act approach. | A reference for asking how management organizes and improves its AI management system; it is not a board checklist. |
| ISO/IEC 23894:2023 | Guidance for AI-specific risk management and its integration into organizational activities, adaptable to organizational context. | A reference for asking how AI risk management fits into the organization’s broader risk work. |
| NIST AI Risk Management Framework 1.0 | A voluntary framework for AI risk management. NIST’s overview, checked October 7, 2026, says the framework is being revised and notes a 2026 concept note for a critical-infrastructure profile. | A voluntary structure for management’s risk work; check NIST’s current overview for revision status before relying on a particular version. |
| EU AI Act, Article 9 | A legal requirement for providers of covered high-risk AI systems to maintain a documented, iterative risk-management system across the lifecycle. | Ask counsel and management to confirm the organization’s role, system classification, jurisdiction, and applicable dates before determining whether the provision applies. |
The source set supports a governance role for the board, but does not establish a universal legal duty for every board to follow a particular AI checklist. Legal obligations depend on applicable law, jurisdiction, company type, system role, and use case.
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