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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchAn effective AI-use policy tells people which tools and uses are allowed, what information may be entered, who is accountable for AI-assisted work, how outputs must be checked, and what to do when something goes wrong. Use the checklist below as a starting point, then tailor permissions and safeguards to your organization’s work, risk tolerance, and applicable requirements.
Start with scope, ownership, and an AI inventory
Define the policy’s purpose and who and what it covers: employees, contractors, organizational systems, and work performed on the organization’s behalf. Explain what the policy means by “AI” and “generative AI,” including features embedded in software people already use.
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Name a policy owner and assign responsibilities for tool approval, higher-risk use review, incident response, and policy updates. Keep an inventory of AI systems and approved providers. For each entry, record the business purpose, owner, data involved, risk tier, and review date. NIST’s AI Risk Management Framework (AI RMF) Core includes system inventory, context mapping, clear roles, and workforce training among its governance and risk-management outcomes.
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Approval should apply to a tool for a stated purpose, not automatically to every possible use of that tool. Identify approved tools and the work each may support. Distinguish personal experimentation from using AI in organizational work, and require review before staff adopt a new service, connect it to company systems, or use an existing tool for a materially different purpose.
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Set restricted and prohibited uses according to the organization’s risk tolerance, likely effects on people, and existing commitments. Explain who can grant an exception and how approval may be changed or withdrawn if the tool, its terms, the use, or the surrounding risks change.
For a proposed use, compare factors such as data sensitivity; the impact and reversibility of decisions; the likelihood and severity of incorrect output; system autonomy and access; who could be affected; the quality of available testing; vendor and integration risks; and the organization’s ability to monitor, correct, or stop the use. This is a practical way to apply context-sensitive risk management, not an official NIST scoring formula. NIST says its AI RMF actions “do not constitute a checklist, nor are they necessarily an ordered set of steps.” Its AI RMF Playbook likewise says it is “neither a checklist nor set of steps to be followed in its entirety.”
Set rules for data, privacy, security, and intellectual property
Tell staff what data may be entered into each approved tool, using concrete examples drawn from the organization’s own classification scheme. Cover personal information, confidential material, credentials, customer records, source code, and third-party or licensed content. Avoid blanket assurances that a tool is safe for sensitive information: requirements depend on the specific service, settings, contract, and use.
- Require users to check relevant settings and contractual terms for retention, use of submitted data to train or improve services, access, and deletion before entering sensitive information.
- Set access-control and approved-integration requirements, and explain how to protect AI outputs that may contain sensitive information.
- Assign responsibility for vendor review, including privacy, security, intellectual-property, and other relevant risks.
- Explain how to request exceptions and how to escalate a vendor or service incident.
NIST’s Generative AI Profile calls for organizational attention to privacy, security, intellectual property, and third-party risks. Requirements in NIST SP 800-63-4 concerning AI and machine learning are specific to identity systems; they should not be treated as rules for every workplace AI tool.
Keep people accountable and define how outputs may be used
Make clear that the person or team using AI remains responsible for the work product and decisions. Identify which categories of work need qualified human review before an output is relied on, sent externally, or used in a way that affects people.
- Require fact-checking and source checks; send consequential, specialized, or technical claims to an appropriate reviewer.
- Specify when and how users must label or disclose AI assistance, based on the audience, use case, contracts, and applicable requirements.
- Provide a way for coworkers or affected people to raise concerns or request human attention where appropriate.
NIST’s AI RMF Core addresses human-AI roles and oversight. Its Generative AI Profile notes that generative AI may warrant additional human review, tracking, documentation, and management oversight, and recommends evaluating capability claims and reviewing sources and citations in outputs.
Review proposed uses and test for risk
Before a use goes live, assess it in a context close to actual deployment where practical. Consider output quality and likely failure modes alongside privacy, security, bias, accessibility, and how people may respond to the system. Keep approval records, review results, known limitations, and mitigation decisions in proportion to the risk.
Reassess when a tool receives a significant update, a new integration or data source is added, the purpose changes, an incident occurs, or the people affected change materially. NIST’s Generative AI Profile emphasizes pre-deployment testing and cautions that anecdotal tests or tests that do not match the deployment setting may not establish validity or reliability there. The AI RMF treats risk management as ongoing rather than a one-time approval.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Plan for incidents, feedback, and service interruptions
Give workers a clear reporting route for inaccurate or harmful outputs, data exposure, security problems, inappropriate use, and suspected vendor incidents. Name the people or teams responsible for triage, preserving relevant records, notifying internal stakeholders, and deciding whether to pause a use.
Set fallback procedures for outages or failures involving high-risk third-party services. Review reports and feedback for recurring problems, then use them to adjust controls, training, or permissions. NIST’s Generative AI Profile treats incident disclosure as a primary consideration and recommends contingency processes for high-risk third-party AI systems.
Train workers and maintain the policy
Train workers on approved tools, data-handling rules, review expectations, and incident reporting before or alongside access. Explain how the AI policy interacts with existing privacy, security, records, procurement, and conduct rules. Tell staff where to direct questions, how exceptions are handled, and what happens when a suspected violation is reported.
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Adapt the checklist to your organization’s obligations
The NIST AI RMF is voluntary guidance, not a universal legal checklist. Applicable obligations can depend on jurisdiction, industry, use case, employment context, contracts, and data type. Have qualified internal reviewers map the policy to the requirements that apply to your organization; the checklist alone does not establish legal compliance.
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