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What an AI acceptable-use policy should do
The policy turns broad principles into operational rules: what people may do with AI, what needs approval, and what happens when use creates risk. NIST’s Generative AI Profile explains that acceptable-use guidance can help reduce risks from misuse, abuse, inappropriate repurposing, and misalignment between users and systems. It is guidance for risk management, not a ready-made policy or a legal checklist.
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NIST AI RMF 1.0 was released on January 26, 2023, and NIST describes it as voluntary. NIST’s framework page also says the framework is being revised; that status can change, so check the page when adopting or updating your policy. The Generative AI Profile, NIST AI 600-1, was published July 26, 2024. See NIST’s AI Risk Management Framework page.
Your organization still needs appropriate legal, privacy, security, HR, compliance, and procurement owners to identify the laws, regulations, sector rules, contracts, and jurisdictional requirements that apply to its work.
1. Assign owners and define the policy’s scope
Name an executive sponsor and an operational owner who maintains the policy, handles questions, and coordinates reviews. Involve the functions affected by AI use—often legal, privacy, security, IT, HR, compliance, procurement, and relevant business teams. NIST’s AI Risk Management Framework treats governance as an ongoing, organization-wide responsibility; it calls for clear roles, documented accountability, legal and regulatory requirement management, an AI system inventory, and periodic review.
State who must follow the policy and which systems it covers. Depending on your organization, that could include employees, contractors, vendors, or other users, as well as consumer chatbots, enterprise services, AI features embedded in existing software, internally hosted models, APIs, and AI-enabled devices. Make clear whether personal experimentation on organizational accounts or devices is in scope.
Scale oversight to your size and risk. A small organization may use named owners and straightforward checks instead of a formal, multi-layer governance structure; the important thing is that accountability and escalation routes are clear.
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2. Classify tools and uses by risk
Maintain an inventory or approved-tool list. Explain how staff can request a tool or a new use case, who reviews it, and how approval is recorded. Approval should apply to the specific service and configuration, not merely to a familiar product name: settings, integrations, and data handling may differ.
Set rules according to the use’s likely impact, the information involved, the degree of automation, and the organization’s ability to oversee it. For example, brainstorming or formatting public information may need fewer controls than a use that affects customers, employees, eligibility, safety, or financial outcomes. These are practical policy-design distinctions, not a claim that NIST assigns those specific examples to risk categories.
For higher-impact uses, specify what additional approval, testing, documentation, and human review are required. State which uses are prohibited or require escalation, including harmful or discriminatory use, unapproved repurposing, or activity outside the organization’s risk tolerance or values. NIST AI 600-1 identifies restricting applications that cause harm or exceed stated tolerances as an available risk-management approach.
3. Set rules for information people may enter
Make data rules usable by connecting information classes to approved tools and conditions. Tell staff whether they may enter public, internal, confidential, customer, employee, personal, regulated, or third-party information—and under what circumstances. Link to existing data-classification and security policies rather than creating a competing classification scheme.
For each approved service, identify relevant controls and limits: who can access it, how long prompts and outputs are retained, whether inputs may be used to improve models, how deletion works, and whether information can be shared with other services. The answer may depend on the service’s configuration and terms. If those protections are unknown or unsuitable for a type of information, instruct employees not to submit it until the service and use have been reviewed.
4. Require review and assign responsibility for outputs
Require users to check AI-generated material before relying on it. The level of review should reflect the consequences of an error: routine drafting may call for ordinary editorial checks, while higher-impact recommendations or content may require a qualified reviewer, documented sign-off, or additional validation.
State who may approve, publish, or act on an output, and make clear that an AI system does not take over the organization’s decision-making responsibility. Assign accountability to a person or role, especially when the output informs a consequential decision. Specify when content must be disclosed or labeled—for example, when organizational rules, law, customer commitments, or the context requires it. There is no single disclosure rule established for every use by the cited NIST material.
5. Review providers and integrations before approval
Assess the specific service, configuration, and intended use before employees rely on a third-party AI tool. The review should cover the issues that matter for the data and decisions involved:
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- Security controls, access permissions, logging, and incident notification.
- Intellectual-property terms and how the provider handles inputs and outputs.
- Transparency about system capabilities, limitations, and material changes.
- Integration risks, including information passed between the AI tool and other systems.
For material procurements, NIST identifies established due-diligence controls that may help, including software bills of materials, service-level agreements, and SSAE reports. The right checks depend on what is being acquired: a foundation model, fine-tuned system, embedded feature, or open-source system may require different review.
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6. Train people, report incidents, and respond
Train employees for their roles and give them an accessible list of approved services, data rules, review expectations, and escalation contacts. NIST’s AI RMF Core says personnel and partners should receive training that enables them to carry out their responsibilities consistently with related policies, procedures, and agreements.
Define how to report exposure of protected information, harmful or misleading output, suspected policy violations, or other AI-related incidents. Identify who receives reports, who leads the response, what records should be preserved, and who can pause a use or remove a tool while the issue is assessed. Monitor use in proportion to risk and in keeping with applicable privacy and employment requirements.
7. Review the policy and keep it current
Give the policy an owner, version date, and review schedule. Set specific triggers for an earlier review, such as a material vendor or tool change, a significant incident, a change in law or contractual obligations, or a shift in organizational risk tolerance. Communicate revisions to affected users and update training and approved-tool lists when the rules change.
Keep the governance process proportionate: a small team may be able to manage review through a few named owners, while a larger organization may need specialist assessments, formal procurement controls, and more structured monitoring. If internal capacity is limited, targeted external governance advice can help tailor controls without changing who inside the organization owns the decisions.
Quick Recap
A practical drafting checklist
- Name the executive sponsor, policy owner, reviewers, and decision-makers.
- Define covered users, systems, organizational accounts, and uses.
- Maintain an approved-tool list and a documented route for new requests.
- Set permitted, restricted, and prohibited uses according to risk and impact.
- Map data classes to tools and state conditions for entering sensitive information.
- Require fit-for-purpose human review and name the person or role accountable for decisions.
- Assess providers and integrations for privacy, security, confidentiality, intellectual property, access, transparency, and change management.
- Train users, provide incident-reporting routes, and assign response authority.
- Set monitoring, review triggers, a review cadence, and a process for communicating updates.
- Have appropriate internal experts determine the applicable legal, regulatory, contractual, and jurisdictional obligations.
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