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Use a risk framework to organize the rules
NIST’s voluntary AI Risk Management Framework (AI RMF) provides a useful structure for managing AI risks; it is guidance, not a legal requirement. NIST released AI RMF 1.0 on January 26, 2023, and says it is being revised. Its Generative AI Profile was released July 26, 2024. See the NIST AI RMF page for its current status and resources.
The associated Playbook groups recommendations under four functions: Govern, Map, Measure, and Manage. NIST says the Playbook is neither a checklist nor a set of steps every organization must follow in full; choose suggestions that fit your organization and use cases. The NIST AI RMF Playbook can help teams turn those functions into policy questions.
- Govern: Set ownership, responsibilities, oversight, training, and rules for transparency.
- Map: Identify the tools, tasks, data, people affected, and likely consequences.
- Measure: Assess relevant risks and whether controls work for the task.
- Manage: Decide how to reduce, monitor, escalate, or accept risks, with accountable approval.
NIST identifies risk dimensions that include validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy, and fairness, including the management of harmful bias. These characteristics can involve tradeoffs, so the right controls depend on context. The NIST AI RMF overview describes the framework’s core concepts.
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Build the policy around actual tools and tasks
1. Inventory current and proposed uses
Ask teams which AI systems they use or want to use, what tasks they perform, what information they provide, and who will rely on the results. Include informal uses, not only centrally purchased systems. Distinguish low-impact activities such as brainstorming from uses that could affect a person’s work, access to services, or treatment.
2. Approve tools and uses separately
Maintain an internal list of approved tools and permitted use cases, with an owner who can update it. Approval of a tool should not automatically authorize every task or data type: a service suitable for drafting public-facing copy may not be suitable for sensitive records or consequential decisions. Define how staff can request review of a new tool or a higher-risk use; the route should fit your organization rather than assume a single approval model works everywhere.
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When comparing proposed tools or uses, consider task suitability and output quality; the data collected, retained, or used by the service; security and access controls; the ability to verify, explain, and audit outputs; effects on workers, customers, and others; human review and override options; applicable obligations; and cost and operational burden. NIST and OECD guidance do not prescribe a universal scoring formula.
3. Set data-handling boundaries
Have security, privacy, and legal owners determine what confidential, personal, regulated, client, or unreleased information may be entered into each approved system. Base the decision on the service’s actual configuration and terms, as well as your organization’s obligations—not on a general assumption about how all AI tools handle data. Set practical instructions for staff on what to exclude, where approved use is allowed, and whom to ask when a case is unclear. Privacy and security are risk dimensions in the NIST AI RMF; the specific boundaries depend on the organization and jurisdiction.
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Require verification and keep a human accountable
Make the person using AI responsible for checking its output before relying on it or sharing it. Specify checks suited to the task: verify factual claims against reliable sources, recalculate figures, test code, validate citations, and review customer-facing material for accuracy and tone. The policy should identify who approves final work and when a qualified person must make or review a decision rather than treating an AI output as the decision-maker.
NIST Playbook recommendations include explicit human roles and responsibilities, risk tracking, proficiency standards, risk-management training, oversight procedures, and transparency policies. Use those recommendations to make accountability concrete: name the responsible role, define when review is required, and set an escalation path for uncertain or high-impact outputs. See the NIST Playbook.
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Apply extra scrutiny to uses that affect people
Employment decisions, worker monitoring, performance evaluation, and other consequential uses warrant careful review before adoption and while in use. Consider privacy, discrimination, labour rights, job quality, transparency, explainability, and accountability. The OECD identifies these as workplace AI concerns in its Employment Outlook 2023, Chapter 6. OECD guidance does not replace applicable law; get jurisdiction- and sector-specific advice where needed.
Rules vary by location, industry, data type, and use case. A general internal policy cannot establish whether a particular employment or monitoring practice is lawful. Route proposed consequential uses to appropriate legal, privacy, security, and workplace stakeholders before they are deployed.
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Train staff, report problems, and keep the policy current
Give staff usable instructions
Training should explain which tools and tasks are permitted, how to protect information, how to check outputs, when human review is necessary, and how to disclose AI use where your policy requires it. Give employees a clear contact for questions and a reporting route for mistakes, suspected misuse, or unexpected effects. NIST recommends risk-management training and clear roles for operation and oversight.
Assign an owner and review triggers
Name a policy owner and set a review schedule appropriate to the organization. Revisit the rules when a new tool or use case is proposed, a vendor changes data practices, a material incident occurs, or relevant law or guidance changes. NIST describes the AI RMF as a living framework and says it is being revised, so check its official resources when refreshing internal guidance.
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