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An effective AI acceptable-use policy is a short operating manual, not a generic legal template. It tells employees which tools and tasks are approved, what information they may enter, how they must check outputs, when expert approval is required, and where to report a mistake or suspected disclosure.
Write the policy from your organization’s actual workflows, data classifications, contracts, and legal obligations. The steps below turn those decisions into rules employees can apply while doing real work.
What an AI acceptable-use policy must accomplish
Generative AI can draft text, summarize records, write code, classify material, create images, or support decisions. The same capability can also expose confidential information, reproduce errors, infringe intellectual property, or influence a consequential decision without adequate review. A usable policy reduces those risks by connecting each activity to a tool, data boundary, human responsibility, and escalation path.
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NIST’s Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1, published July 26, 2024) explains that acceptable-use guidance can reduce misuse, abuse, inappropriate repurposing, and misalignment between people and generative AI systems. The profile and the NIST AI RMF Playbook are voluntary guidance, not legislation, a certification, or a universal checklist. Apply them to your organization’s circumstances and obtain appropriate legal, privacy, security, human-resources, compliance, and business review.
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Your policy should answer six employee questions:
- Which AI tools and features may I use for work?
- What work may I do with each approved tool?
- What information may I enter, and what must never be entered?
- What checking, documentation, or disclosure is required before I use an output?
- Who approves a new tool or a higher-risk use?
- How do I report an incident, harmful output, or uncertain situation?
If an employee cannot answer those questions from the policy and a linked procedure, the document is not operational enough.
Start with an inventory of real AI use
Do not begin by copying a model policy. First learn what employees are already doing. Ask teams and contractors:
- Which public services, enterprise applications, plug-ins, browser features, coding assistants, and embedded AI functions are being used?
- What tasks are performed: brainstorming, drafting, translation, summarization, coding, analysis, customer communication, screening, or decision support?
- What information is entered, including prompts, uploaded files, copied records, and connected data sources?
- Where do outputs go: internal documents, production systems, customer messages, code repositories, case files, or decisions?
- Who reviews the result, and what happens if the result is wrong?
This inventory is a practical application of NIST governance and risk-mapping guidance, not a prescribed universal process. It reveals unapproved tools, repeated use cases that deserve a standard approval, and activities that require immediate restriction. Include third-party services and AI functions hidden inside products; a feature labeled “assistant” can create the same data, security, and accountability issues as a standalone model.
Record enough detail to make a decision
For each use, record the tool and product edition, provider, account type, business owner, data categories, intended output, affected people, retention settings, integrations, and failure consequences. Note whether the service is centrally administered or an externally hosted public service. Keep this register with your vendor and security records so that a tool change triggers a review.
Define scope and key terms
State who and what the policy covers. Include employees, temporary workers, contractors, interns, and anyone processing organizational information on the organization’s behalf, unless a stricter contract or separate standard applies. Cover AI used on company devices, personal devices for company work, enterprise applications, externally hosted services, and embedded features in approved software.
Define terms in plain language. For example:
- Generative AI: a system that produces or transforms content such as text, code, images, audio, or video in response to instructions or data.
- Approved tool: a service or feature reviewed and authorized for specified organizational uses and data classes.
- High-impact use: a use that may affect a person’s employment, access to services, rights, finances, safety, legal position, or similarly significant interests.
- Human review: a named person’s substantive verification and decision, not merely clicking “approve” or checking whether text looks plausible.
- Restricted information: any information classified by the organization as confidential, personal, regulated, privileged, export-controlled, security-sensitive, or otherwise prohibited from a particular tool.
Explain that approval is specific. Authorization for one tool, account, data class, or task does not automatically authorize another. A public-facing service, an enterprise-managed tenant, and an AI feature inside an existing application may have different retention, access, and contractual controls.
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Choose approved tools and approved tasks
Publish an accessible list of approved tools and the uses allowed for each. For every entry, identify the business owner, permitted data classes, prohibited inputs, retention and sharing settings, required account type, and review date. Tell employees how to request a tool or use that is not listed.
Use a risk-and-data decision matrix
Classify a proposed use using four practical axes: information sensitivity, impact of the task, tool status and control, and human review or disclosure. This is an organizational design recommendation inferred from NIST’s risk-based approach, not a mandatory NIST classification scheme.
| Use class | Information sensitivity | Task impact | Tool and control | Minimum review and disclosure |
|---|---|---|---|---|
| Routine assistance | Public information or synthetic examples | Low-impact brainstorming, formatting, or first drafts | Approved, centrally managed tool | Employee checks factual claims, appropriateness, and licensing before use; disclose AI assistance when a procedure requires it |
| Internal work support | Internal information permitted by the tool’s approval | Operational documents or analysis that does not itself determine a person’s rights or access | Approved enterprise tool or approved embedded feature with documented controls | Employee verifies content against authoritative records and preserves source material where required |
| Sensitive or personal information | Confidential, personal, regulated, privileged, or security-sensitive data | Any impact level | Only a tool explicitly approved for that data class and purpose | Follow privacy, security, retention, and access controls; obtain the designated owner’s approval when the procedure requires it |
| High-impact assistance | Any data, especially information about identifiable people | Employment, customer eligibility, finances, safety, legal obligations, or other consequential outcomes | Approved tool and documented use case; unreviewed public services are not permitted | Qualified human makes and records the decision; verify relevant evidence, test for material errors or bias, and provide required notice or explanation |
| Unreviewed use | Any non-public or restricted information, or information whose status is uncertain | Any task | Unapproved service, account, integration, or feature | Do not use for work until approval; ask the policy owner if uncertain |
Use your existing information-classification labels rather than inventing a second vocabulary. If a cell is not covered by an approval, the default should be “stop and ask,” not an employee guess.
State prohibited uses plainly
List prohibitions that follow from your actual controls. Common examples include entering restricted information into a tool not approved for it; using generated content as the sole basis for a high-impact decision; bypassing access controls by pasting records into a personal account; uploading customer or employee data without an approved purpose; presenting generated material as verified when it has not been checked; and using AI to create discriminatory, harassing, deceptive, malicious, or unlawful content.
Do not imply that the word “AI” alone determines risk. A third-party model, a private deployment, and an embedded feature can process different data and have different contractual and technical safeguards.
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Employees need an input rule, not a general warning to “protect data.” Connect each data category to an approved destination and a handling requirement. For example, a policy might say:
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- Public material may be entered only when the employee has the right to share it and the prompt does not combine it with restricted context.
- Internal information may be entered only into a tool approved for internal information, using the organization’s managed account and configured access controls.
- Confidential, personal, regulated, privileged, or security-sensitive information may be entered only into a tool and workflow expressly approved for that category and purpose.
- If the classification or approval is unclear, do not upload the material. Ask the information owner or policy contact.
Explain minimization: remove names, identifiers, unnecessary fields, attachments, and full records when a smaller or synthetic example will do. Do not assume that deleting a prompt later removes copies from logs, backups, connected applications, or exports.
Document retention, access, and deletion
For each approved tool, state what organizational records are retained, who can access prompts and outputs, how long they are kept, where they are stored, and how deletion or legal holds work. Align those statements with the current product configuration and contract. Never promise that a provider does not train on, retain, or disclose information unless your organization has verified the current terms and settings.
NIST’s Generative AI Profile identifies data protection and retention as relevant practices. Your privacy and security owners should confirm the lawful basis, notices, cross-border transfers, access controls, and records-management requirements for the intended use.
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Make human accountability explicit
An AI output is a draft or recommendation, not an accountable decision. Assign the person who remains responsible for accuracy, fairness, confidentiality, legality, and fitness for purpose. The policy should require a level of review proportionate to the task rather than claiming that a generic “AI check” guarantees correctness.
Specify what reviewers must check
- Verify factual claims, calculations, quotations, citations, dates, names, and code behavior against authoritative sources or tests.
- Check that the output follows the assignment, does not omit material qualifications, and does not expose confidential or personal information.
- Look for fabricated references, unsupported conclusions, unsafe instructions, discriminatory patterns, and inappropriate tone.
- Confirm that the employee has permission to use any source, image, code, or other material incorporated into the result.
- Record the reviewer, date, source evidence, and material corrections when the output enters a controlled record or consequential workflow.
Escalate consequential uses
Require expert or designated-owner review before using AI assistance in employment actions, customer eligibility or pricing, lending or financial decisions, safety-related work, legal advice or filings, regulated communications, security operations, or any decision that may materially affect a person. A human must be able to understand the evidence, challenge the output, and make an independent decision. If the system cannot provide enough information to support that review, do not use it for the decision.
Set disclosure rules
Tell employees when they must disclose AI assistance to customers, regulators, colleagues, reviewers, or affected individuals. Disclosure may be required by contract, law, professional standards, or an internal procedure. Specify what to record: the tool, purpose, material human edits, reviewer, and approval, where that record is necessary for auditability.
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Assign ownership, approval, and escalation
Name a policy owner and publish a working contact route. The owner may coordinate with information security, privacy, legal, procurement, compliance, human resources, records management, and business leaders; responsibility should not disappear into a generic mailbox.
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- The requester describes the tool, provider, account, task, data classes, users, integrations, expected output, and failure consequences.
- The business owner confirms the purpose and identifies an accountable human reviewer.
- Security and privacy assess access, retention, confidentiality, data transfers, and technical controls.
- Legal, compliance, procurement, or human resources review the use when jurisdiction, contract, employment, regulated activity, or intellectual-property issues make that necessary.
- The approver records permitted tools, tasks, data classes, users, controls, review date, and conditions. Approval is denied or limited if controls are insufficient.
Provide an escalation route for uncertainty, suspected disclosure, harmful output, copyright or licensing concerns, security incidents, and requests from people affected by an AI-assisted process. Tell employees what to do immediately: stop further use, preserve relevant prompts and outputs where permitted, avoid deleting evidence, and contact the incident channel or manager. Coordinate incident handling with your existing security, privacy, and records procedures.
Train employees with scenarios, not just a signature
Training should use the tools and workflows employees actually encounter. Demonstrate a safe public-data prompt, an unsafe upload of a customer record, a fabricated citation, a coding suggestion that fails a test, and a high-impact recommendation that requires independent review. Show the approved-tool list, data-classification labels, approval form, disclosure rule, and reporting route.
Require additional training for people who approve tools, administer accounts, review high-impact uses, handle restricted information, or investigate incidents. Keep attendance and role-specific completion records. Refresh training when the policy, approved tools, configurations, or material risks change.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Address third-party AI services before approval
Third-party generative AI integrations can create intellectual-property, privacy, and information-security risks. During due diligence, consider the provider’s current terms, data processing commitments, retention and deletion controls, access model, incident notification, subcontractors, geographic processing, audit evidence, and change-notification process. Depending on the use, procurement may also consider a service-level agreement, a software bill of materials, or an attestation report. These instruments do not replace a use-case risk assessment.
Verify the exact product configuration and contract before making a statement about training, confidentiality, security, or retention. A provider’s general marketing page may not describe the edition, region, account type, or administrator settings your organization would use. Reassess an approval when those conditions change.
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Write the policy in employee actions
Use short rules followed by the reason or exception. Examples of useful wording include:
- “Use only tools and features listed in the approved AI register for work information.”
- “Do not enter confidential, personal, regulated, privileged, or security-sensitive information unless the register expressly permits that category for the stated purpose.”
- “Treat generated content as unverified until you check factual claims, calculations, sources, and instructions against authoritative material.”
- “You remain responsible for work you submit, send, publish, or use in a decision, even when AI produced part of it.”
- “Obtain the named owner’s approval before using AI in an employment, customer-eligibility, financial, safety, legal, or other consequential process.”
- “Stop and report a suspected disclosure, harmful output, security issue, or material error through the incident route in this policy.”
These are drafting examples. Replace the data categories, owner names, systems, and escalation channels with your organization’s actual terms. Put detailed procedures, the approved-tool register, and forms in linked documents so the core policy stays short enough to read.
Review and change the policy continuously
Set a review cadence, such as an annual review, and define earlier triggers. Review when a tool adds a model, changes retention or training terms, enables a new integration, moves data to a new region, or changes account controls. Also review after an incident, a near miss, a material regulatory or contractual change, a new high-impact use, or a change in information classification.
Track version, owner, effective date, approval record, affected tools and tasks, training impact, and superseded guidance. Remove obsolete tools promptly from the approved list and disable accounts or integrations when appropriate. Monitor reported incidents, exceptions, review findings, and unapproved-use signals to identify where the policy is unclear or controls are failing.
NIST describes its AI RMF as a living document and noted that revision work was tasked in 2025. Check current NIST materials and applicable requirements at the time you publish or revise your policy; do not treat an older edition as a permanent legal or technical baseline.
A final pre-publication checklist
- Scope names covered people, systems, embedded features, and work performed on personal devices.
- Definitions distinguish approved tools, high-impact uses, human review, and restricted information.
- An approved-tool register identifies allowed tasks, data classes, account controls, owners, and review dates.
- Prohibited uses and “stop and ask” conditions are concrete.
- Data rules address minimization, retention, access, deletion, legal holds, and uncertainty.
- Human reviewers have explicit checks and authority to reject an output.
- High-impact uses require qualified review and any required disclosure or record.
- Tool approval, policy ownership, incident reporting, and escalation contacts are named.
- Third-party due diligence and change notification are assigned to responsible teams.
- Training, publication, exception handling, monitoring, and review triggers are scheduled.
- Legal, privacy, security, HR, compliance, procurement, and business owners have reviewed the parts relevant to the organization’s jurisdictions and uses.
A policy employees can follow is specific enough to guide the next prompt they write and the next output they receive. Keep the core rules brief, connect them to maintained procedures and approvals, and update them whenever the tools, data, risks, or obligations change.
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