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An AI acceptable-use policy works when an employee can answer five questions before using a tool: Which tools may I use? What work may I do with them? What information may I enter? What must I check or disclose? Who do I contact when the answer is unclear? Build the document from your organization’s actual tools, data classifications, workflows and legal obligations—not from a generic template. The process below turns those decisions into short, operational rules, with approval, review, training and incident reporting built in.
What the policy must accomplish
The policy is an operating control for human use of generative and other AI features. It should reduce misuse, abuse, inappropriate repurposing and misalignment between people and systems, the purpose described by the National Institute of Standards and Technology (NIST) in its Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1). It is not a promise that an AI system is accurate, private or safe by default.
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A usable policy connects four decisions:
- Scope: which AI applications, embedded features, models, agents, plug-ins and organizational activities are covered.
- Permission: which tools and tasks are approved, which require review, and which are prohibited.
- Accountability: who checks outputs, owns the final decision and records or discloses AI assistance.
- Governance: who approves changes, handles questions and incidents, trains staff, and reviews the rules.
Define terms employees will encounter, such as “generative AI,” “approved tool,” “work information,” “personal information,” “high-impact decision” and “human review.” NIST recommends defining key terms and the purposes and intended uses covered by guidance. Keep definitions consistent with your existing information-security and privacy policies.
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Start with an inventory of real work
Ask what teams already do
Before writing a rule, ask each team which AI tools and features it uses, what task each one supports, what information is entered, who receives the output and whether a person makes a consequential decision from it. Include browser services, office-suite assistants, customer-support functions, code tools, transcription, image generation, analytics and vendor portals. A feature built into an approved system can still create a different data flow from the system itself.
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Record the tool name and version or configuration, business owner, data location if known, retention settings, access group, contract owner and intended users. Mark uses that are unapproved, experimental or difficult to explain. This inventory is a practical application of NIST’s governance and risk-mapping guidance, not a universal NIST-required form.
Map the destination and decision
For every use, document where prompts, uploaded files and generated outputs go next. A draft may remain internal, enter a customer record, be published, become source code, or influence hiring, credit, benefits, safety or legal advice. The destination and the decision’s impact often matter more than the label “AI.”
Turn findings into a scope statement
State whether the policy covers employees, contractors, temporary staff, interns and vendors acting for the organization. Include personal accounts used for work, AI inside other software and automated agents that call models. Say whether personal experimentation on a company device or network is covered. Exclude systems governed by a more specific policy only when you name that policy and explain which rule controls if they conflict.
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Employees need a quick decision route, not a list of model brands. Use the organization’s established information-classification scheme and a small number of impact levels. The following matrix is a drafting model; adjust labels and thresholds to your business.
| Use class | Information | Task impact | Tool status | Minimum human control | Employee action |
|---|---|---|---|---|---|
| Routine assistance | Public material or approved low-sensitivity internal content | Low-impact brainstorming, formatting, summaries or drafts | Approved enterprise tool or approved embedded feature | Check material facts, copyright-sensitive content and unintended disclosures | Use the approved tool; keep the final responsibility; disclose assistance when the audience or contract requires it |
| Controlled internal work | Internal or confidential information allowed by the tool’s configuration | Work product that could affect operations, customers or finances | Approved tool with documented access, retention and security controls | Subject-matter review, source checking and a record of the reviewer where required | Confirm the data category and workflow are approved before entry; follow the team’s review checklist |
| Restricted or regulated work | Personal, regulated, privileged, secret or otherwise restricted data | High-impact decisions, legal obligations, safety, employment, rights or access to services | Only a specifically approved configuration and use case | Qualified expert review, documented rationale, required notices and any applicable testing or monitoring | Obtain prior approval; do not proceed on a general “AI is allowed” assumption |
| Unreviewed use | Any category | Any impact | Public service, plug-in, model or feature not approved for the task | No organizational assurance | Do not enter work information or rely on the output; submit an approval request |
Make the table actionable by naming the approval route and linking each data category to examples employees recognize. The axes—information sensitivity, task impact, tool control, and human review or disclosure—are practical controls inferred from NIST’s risk-based guidance, not a NIST-mandated classification scheme.
Set information boundaries employees can apply
Define what may be entered
For each approved tool, list permitted data classes and realistic examples. “Public” might include already-published web copy. “Internal” could include a non-sensitive project outline if the tool’s access and retention settings permit it. “Restricted” can include customer identifiers, health or financial records, credentials, security findings, trade secrets, legal advice and data subject to contractual or regulatory limits. If a category is not explicitly allowed, the safe instruction is to keep it out and ask the policy owner.
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Prohibit secrets and direct identifiers in prompts unless a specifically approved workflow requires them. Tell employees to remove unnecessary fields, use synthetic or redacted examples, and avoid uploading an entire record when a small excerpt answers the question. Redaction is not automatically sufficient: combinations of details can re-identify a person.
Explain retention, access and reuse
Document how prompts, files and outputs are retained, who can access them, whether the provider may use them for model improvement, and how deletion or legal holds work. Do not claim that a provider does or does not train on data, retain it, or keep it confidential unless your organization has checked the current product configuration and terms. If a setting changes the answer, name the required setting and the owner responsible for verifying it.
State where an output may be stored. An AI draft copied into a customer record, source repository or case file inherits that destination’s access, retention and records-management rules. Require employees to label or log AI-assisted content when your records, contracts or professional rules demand it.
Control onward sharing
Generated text can contain confidential material copied from a prompt, and generated code or images can create licensing or intellectual-property questions. Require a review before external publication, delivery to a customer, inclusion in a filing, or release under an open-source or commercial license. Do not treat an output as public merely because it came from a public tool.
Make human judgment explicit
Assign the final decision
Write that a named employee or role—not the model—owns the accuracy, fairness, legality and suitability of work sent onward. “Human in the loop” is too vague. Specify who reviews, what they examine, and what evidence they record for each high-impact workflow.
Define a review depth
- Routine drafts: check factual claims, calculations, names, dates, tone, citations, confidential material and obvious bias before use.
- Technical or regulated work: have a qualified subject-matter reviewer reproduce important results, inspect source material and confirm that the method is appropriate.
- Consequential decisions: do not let an unverified output determine employment, access to services, credit, safety action, legal position or similar outcomes. Require the organization’s existing decision controls, an accountable decision-maker and any notices or appeal rights that apply.
State that a generic “AI detector” or a single plausibility check does not guarantee correctness. NIST calls for testing and risk management appropriate to context; your policy should identify the tests, sampling, monitoring or second review that the particular workflow needs.
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Require disclosure where appropriate
Tell employees when to disclose AI assistance internally, to customers, to regulators, to authors or to other affected people. Disclosure may be required by a contract, law, professional rule or publication standard. Provide the approved wording and the record location rather than leaving employees to improvise.
Approve tools and third-party integrations deliberately
Name an approval owner
Identify the role that approves a tool and the role that approves a sensitive use case. A practical route may involve information security, privacy, legal, procurement, records management and the business owner. Include a request form that captures the tool, configuration, data classes, users, purpose, output destination, review method and exit plan.
Perform proportionate due diligence
NIST notes that third-party generative-AI integrations can create intellectual-property, privacy and information-security risks. Depending on the use, procurement may consider contract terms, service-level agreements, software bills of materials, independent attestation reports, access controls, incident notification, deletion, subcontractors and audit rights. These are options to evaluate, not proof that a provider is safe.
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Provide a fast exception path
Employees will bypass a policy that takes weeks to answer a simple question. Set a service target for routine requests, an expedited route for urgent operational work and a named escalation for blocked or disputed requests. Require written conditions and an expiry date for temporary exceptions.
State prohibited and prior-review uses plainly
Use short action statements. Examples that organizations commonly need to evaluate include:
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- Do not paste credentials, private keys, security tokens or unnecessary personal data into an AI tool.
- Do not use an unapproved public service for work information.
- Do not present generated claims, citations, code, images or calculations as verified without checking them.
- Do not let an AI output make a high-impact decision without the designated accountable reviewer and required process.
- Do not circumvent access controls, confidentiality duties, records rules or contractual restrictions by rephrasing restricted material.
- Obtain prior review for new data categories, external publication, regulated work, safety-critical use, employment decisions, legal advice or automated actions that can change a customer or employee record.
Explain the reason and the alternative: use an approved tool, redact the information, consult the subject-matter owner or submit a request. A prohibition without a workable route encourages shadow use.
Assign roles, training and reporting
Put responsibilities in the policy
- Policy owner: maintains the document, approval register and review calendar.
- Tool or use-case approver: evaluates data flows, controls, contract terms and intended purpose.
- Managers and process owners: ensure staff follow workflow-specific review and recordkeeping rules.
- Employees and contractors: use approved tools, protect information, check outputs, disclose when required and report concerns.
- Security, privacy, legal, HR and compliance contacts: advise on incidents and context-specific obligations.
Train for decisions, not slogans
Give affected staff short scenarios using your own data categories: a public marketing draft, an internal forecast, a customer complaint containing identifiers, and a hiring recommendation. Show the approval screen, redaction method, review checklist, disclosure language and reporting route. Repeat training when a material tool or rule changes, and keep attendance or completion records where required.
Make reporting safe and specific
Provide one channel for suspected disclosure, harmful or discriminatory output, prompt injection, inaccurate consequential advice, unauthorized tool use and unexpected charges. Tell employees what to preserve—tool name, date, prompt or file identifier if safe, output, recipients and immediate action—and what not to forward if it contains sensitive information. State that good-faith reporting is supported and who coordinates containment, notification, correction and lessons learned.
Draft the document in a sequence employees can scan
- Write the one-page decision rule. Start with approved tools, approved tasks, forbidden data, required checks and the question or incident contact.
- Add scope and definitions. Cover people, systems, embedded features, personal accounts used for work and the terms employees must understand.
- Insert the data-and-impact matrix. Map each class to permitted information, tool status, review depth and disclosure.
- Document the approval route. Name owners, required evidence, turnaround targets, temporary exceptions and expiry.
- Specify information handling. Cover minimization, redaction, retention, access, onward sharing, records and deletion.
- Specify human review. Identify the accountable role, verification steps, documentation and prohibited reliance for consequential work.
- Add reporting and response. Provide the channel, immediate containment instruction, escalation contacts and incident-handling expectations.
- Obtain contextual review. Have legal, privacy, security, HR, compliance and business owners review the portions relevant to your jurisdictions and purposes. Requirements vary by industry and application.
- Publish and train. Put the current version where staff work, make the approval register discoverable and test understanding with scenarios.
- Schedule maintenance. Set a review date and event triggers, then record decisions and changes.
A compact employee-facing core you can adapt
Keep the opening rule short, then link to the detailed procedures:
Use only AI tools and features approved for your work and the information you plan to enter.
Do not enter credentials, unnecessary personal data, confidential or regulated information unless the approved workflow explicitly permits it.
Check factual claims, calculations, code, citations, rights and confidential content before relying on an output.
A designated person remains responsible for every decision and deliverable; high-impact uses require the review listed for that workflow.
Disclose AI assistance when the contract, law, professional rule or internal procedure requires it.
Ask the policy owner before a new tool, data category or consequential use. Report suspected disclosure, harmful output or unauthorized use through the incident channel.
Replace the generic references with your actual tool register, data-classification names, review checklist, contact addresses and disclosure wording. Keep those details maintained outside the paragraph if they change frequently, but make the links obvious.
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Review, monitor and change the policy
Set a regular cadence and triggers
Choose a review cadence appropriate to how quickly your tools and obligations change, and review sooner after a material model or feature change, a new connector, a security or privacy incident, a harmful-output report, a regulatory or contractual change, a new high-impact use, or evidence that staff cannot follow the rule. NIST’s Generative AI Profile emphasizes monitoring, and its Playbook organizes voluntary prompts under Govern, Map, Measure and Manage.
Measure whether the controls work
Track practical signals such as approval turnaround, training completion, reports by category, repeat violations, review records for high-impact uses, outdated tool entries and time to contain a suspected disclosure. These measures show where instructions or workflows need improvement; they do not prove that an AI system is accurate or that the policy eliminates risk.
Version the policy and register
Give the policy an owner, effective date, version, change summary and next review date. Retire tools that no longer meet requirements, update the approved-use register when configurations change and notify affected users. Preserve superseded versions when records or legal obligations require it.
Adapt the rules to your legal and operational context
There is no universal AI-use rule that satisfies every organization. Jurisdiction, industry, purpose, affected people, contractual commitments and the sensitivity of the data can change the required controls. Have the appropriate legal, privacy, security, HR, compliance and business specialists review the final document. NIST AI 600-1 and the NIST Playbook are voluntary guidance, not law, certification or a substitute for obligations that apply to your organization. NIST describes its AI Risk Management Framework 1.0 as a living document and noted that revision work was tasked in 2025, so check the current NIST materials when you maintain the policy.
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“AI is allowed for productivity”
This leaves data, tool and impact decisions unanswered. Replace it with approved tools, permitted tasks and a matrix tied to your classifications.
A long list of banned prompts
Examples age quickly and miss new features. Keep a short set of durable prohibitions, then govern new tools and data flows through approval and review.
One review rule for every task
A spelling suggestion and a benefits decision do not need the same evidence. Scale human review to impact, information sensitivity and the consequences of error.
Promises about a provider’s privacy
Product terms and administrator settings change. Record what your organization verified for the specific configuration, who owns the check and when it must be repeated.
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Employees either guess or use a shadow tool when the policy only says “ask security.” Name a role, channel, response target and emergency route, and explain what information to include in a report.
No maintenance owner
An unowned document becomes inaccurate as models, connectors and obligations change. Assign ownership, a review calendar, event triggers and a versioned approval register.
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