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An effective enterprise AI acceptable-use policy does more than ban sensitive prompts. It defines which AI systems and people are covered, assigns an accountable owner to every use case, matches approval and human review to the potential harm, controls data and vendor exposure, and creates a process for training, incidents, monitoring, and retirement. It should apply to centrally purchased services, open-source models, APIs, AI features embedded in ordinary software, and tools used by contractors—not only to products deployed by the IT department.
The policy is one control in a broader governance system. Maintain a system and use-case inventory, perform risk reviews before deployment, document decisions, and revisit them when the model, purpose, data, users, or impacts change. NIST describes governance as continual across an AI system’s lifespan and the organization’s hierarchy. Its AI Risk Management Framework (AI RMF) is voluntary guidance, not a substitute for sector, contractual, privacy, employment, intellectual-property, or other applicable law.
Start with a policy that answers the operational questions
Employees and managers need unambiguous answers to questions such as: Which AI tools are approved for work? Can confidential information be entered? When must a person check an answer? Who can approve a customer-facing use? How is a harmful output reported? Put the answers in enforceable rules, then connect those rules to security, privacy, records, procurement, acceptable-use, intellectual-property, accessibility, and employment policies.
NIST’s Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1, 2024) says, “Establishing acceptable use policies and guidance for the use of GAI in formal human-AI teaming settings as well as different levels of human-AI configurations can help to decrease risks arising from misuse, abuse, inappropriate repurpose, and misalignment between systems and users.” The policy should therefore describe the relationship between a person and the system, not just list prohibited prompts.
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Define purpose, scope, and terms
State the policy’s objective and boundaries
Explain that the policy governs the safe, lawful, and accountable use of AI in company work. Cover generative and predictive systems, chat and code assistants, model APIs, retrieval or agent systems, fine-tuned models, automated decision tools, and AI functions built into software already approved for other purposes. Include systems operated by the company and systems supplied by vendors or partners.
Name every covered user group: employees, temporary staff, contractors, consultants, interns, suppliers, and other third parties acting for the organization. A rule limited to IT-procured tools misses browser services, developer libraries, customer-support features, and contractor accounts. NIST specifically recommends updating acceptable-use policies for proprietary and open-source technologies and data and covering contractors and other third-party personnel.
Use definitions that work across departments
Define terms such as AI system, generative AI, approved tool, sensitive data, high-impact use, human reviewer, and AI-generated content. Make definitions consistent with internal security classifications, privacy notices, records schedules, procurement standards, and incident procedures. Identify whether an AI system drafts, recommends, classifies, or can directly trigger an action; that distinction determines controls later in the policy.
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Maintain an approved-tool and permitted-use process
Make approval discoverable
Publish an approved-tool register or a single approval route. For each system, record its owner, intended purposes, model or relevant components, supported users, data-handling and retention terms, known limitations, security review, risk decision, and required human oversight. Tell users where the authoritative list lives and what to do when a feature is not listed.
Permit routine activities—such as drafting, summarization, search, translation, or code assistance—only when the tool, information, and review controls fit the approved use. A tool approved for public marketing copy is not automatically approved for employee records, source code, regulated data, or customer decisions.
Give prohibited and restricted examples
Prohibit or restrict conduct that violates law, contract, rights, security requirements, or the organization’s risk tolerance. Examples may include:
- Entering unapproved personal, confidential, regulated, customer, employee, credential, source-code, or third-party information.
- Bypassing access controls or using a model to obtain information the user is not authorized to see.
- Impersonation, deception, malicious content, or unlawful discrimination.
- Using unverified generated material as the sole basis for a consequential decision about a person, customer, worker, payment, safety action, or access right.
- Deploying generated code, configurations, or automated actions without the testing and security review required for that environment.
These examples are starting points, not a universal legal list. Tailor them to the organization’s values, products, jurisdictions, contracts, and actual risks. NIST’s framework supports restrictions that reflect documented intended purposes and organizational risk tolerance.
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Require a documented risk review before new use
Capture the context
Require an intake before a new system or materially different use goes live. The record should identify:
- Business purpose, expected benefit, users, and people or groups affected.
- Context of use, workflow position, model or provider, connected systems, and data types.
- Known limitations, foreseeable misuse, legal or contractual requirements, and rights or accessibility impacts.
- Risk owner, proposed safeguards, testing evidence, monitoring measures, and an exit or fallback path.
NIST recommends mapping context to support an initial appropriateness or go/no-go decision and revisiting that decision as context, capabilities, risks, and impacts change.
Scale approval to consequence
Use risk tiers to avoid treating a low-consequence drafting aid like an automated eligibility decision. Your policy should specify who can approve each tier and when legal, privacy, security, procurement, human-resources, accessibility, records, or domain specialists must participate. A higher tier should apply when a use affects rights, safety, employment, access to services, finances, reputation, or sensitive populations; when data is highly sensitive; or when the system can act without a person.
Keep the tier labels and approval matrix organization-specific. The important controls are a named decision-maker, recorded rationale, explicit conditions, and a trigger for reapproval when the system or purpose changes.
Set data, privacy, security, and records rules before prompting
Map classifications to approved environments
For every data class, state which approved systems may receive it and under what conditions. Cover prompt text, uploaded files, connected repositories, retrieval indexes, fine-tuning data, feedback, and telemetry—not just the visible chat message. Require users to confirm that they have authority to use the information and that provider terms do not permit unintended retention, training, sharing, or reuse.
Answer the practical question, “Can an employee paste confidential information into a public chatbot?” The default should be no unless the specific environment is approved for that classification, the business purpose is authorized, and the provider and contract controls meet the organization’s requirements. A personal account or an unapproved browser extension is not made safe by deleting the conversation afterward.
Control access and retention
Require identity-based access, least privilege, connector approval, segregation of duties where appropriate, and logging of administrative and material user activity. Define how prompts and outputs are stored, searched, exported, deleted, and placed under legal hold. Align retention with records schedules, privacy obligations, discovery needs, and contractual commitments. DOE policy provides a federal example in which access controls cover information input, maintained, or output by a model and align with records requirements; a private enterprise must map the equivalent controls to its own obligations.
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Review third-party integrations for data leakage, prompt injection, excessive permissions, and insecure output handling. NIST warns that third-party integrations can increase intellectual-property, data-privacy, and information-security risks.
Keep a human accountable for consequential use
Specify the human-AI role
For each approved use, state whether AI drafts, recommends, classifies, or acts. Name a business owner and a reviewer. The reviewer must have relevant competence, enough time to check the result, authority to reject or stop the workflow, and a clear escalation route. The model does not assume the organization’s legal, ethical, or operational responsibility.
Define what verification means
Require proportionate checks for important facts, calculations, citations, recommendations, personal information, bias, security defects, and rights concerns. A reviewer may need to trace claims to authoritative sources, reproduce a calculation, test code, inspect a sample for disparate errors, or confirm that a recommendation is supported by complete and relevant data. For code, require normal peer review, testing, dependency and security checks before deployment.
Do not treat benchmark performance as proof of production reliability. NIST notes that tests conducted in laboratories or benchmark settings may not predict performance in the actual deployment context. The policy should define acceptable error, monitoring, and stop conditions for the real workflow.
Govern intellectual property, attribution, and output use
Users must have the right to submit input material and must follow confidentiality, license, contract, and privacy restrictions. Before external publication or delivery, review generated text, images, code, and other outputs for copied or sensitive material, unsupported claims, personal information, and possible infringement. Set internal rules for attribution, disclosure of AI assistance, ownership, retention, and employee-created materials; coordinate them with contracts and intellectual-property counsel.
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NIST identifies third-party intellectual-property risk and recommends due diligence and transparency controls. DOE policy similarly tells users to understand applicable intellectual-property restrictions and recognizes that an output may infringe. Neither source creates a universal ownership rule, so the policy should state the organization’s approved handling and require specialist review when rights are uncertain.
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Review providers and open-source components
Extend existing procurement and security reviews to foundation-model providers, hosted APIs, fine-tuned models, open-source packages, and embedded vendor features. Ask for relevant information about training and input-data use, retention, security controls, limitations, subcontractors, incident notification, model-change practices, and service continuity. Check whether the contract permits audit, deletion, portability, and timely incident cooperation.
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NIST identifies procurement due diligence, software bills of materials, service-level agreements, and attestation reports as possible third-party risk measures. Use the measures that fit the system’s risk rather than demanding identical evidence from every supplier.
Plan for provider failure and model change
Document a fallback if a provider becomes unavailable, changes the model, loses required data protections, produces unsafe behavior, or suffers an incident. Record who can pause use, how data is isolated or deleted, how customers and regulators are notified when required, and how the service is replaced. Rehearse the response for high-impact systems and retain an incident record.
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Set transparency and disclosure rules carefully
Define when employees must identify AI assistance internally and when customers, workers, users, or the public must be informed. Disclosure should depend on the use, audience, contractual commitments, and applicable law; do not claim that every AI-generated item universally requires a label.
For EU operations, the European Commission’s current guidance says AI Act Article 50 applies from August 2, 2026. It describes obligations for specified interactions and content contexts, including certain deepfakes and public-interest text produced without human review or editorial control. Coverage, exceptions, provider or deployer role, and implementation details depend on the actual system and use. Review the regulation and local guidance for each affected operation rather than copying an EU rule into a global policy unchanged.
Train users, approvers, and reviewers—and enforce the rules
Make training role-specific
Train all covered users on approved tools, data classification, prompt and file handling, verification, hallucinations and limitations, bias, intellectual property, disclosure, and incident reporting. Train approvers and reviewers on their additional duties: context assessment, evidence standards, documentation, escalation, and stop authority. DOE’s federal policy lists policy compliance, responsible use, bias and discrimination, hallucinations and limitations, data handling, disinformation or malicious outputs, and security incident reporting as training subjects.
Provide a safe reporting route
State how to ask questions and report suspected exposure of sensitive data, unsafe outputs, security events, rights concerns, harmful outcomes, or material errors. Offer a route for good-faith escalation without requiring employees to resolve legal or technical uncertainty alone. Make the policy accessible to contractors and other affected staff, and explain consequences for intentional misuse, repeated unsafe behavior, and failure to follow required approvals.
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Maintain an inventory and change control
Keep an inventory of approved systems, use cases, owners, data classes, risk tier, review date, and dependencies. Monitor changes in purpose, model version, connected data, behavior, performance, security, and impact. A new model release or connector can require a fresh review even when the product name has not changed.
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Operate an incident process
Define response owners, severity thresholds, containment steps, evidence preservation, user and customer communications, and escalation to legal, privacy, security, or regulators where applicable. Include prompt-data exposure, malicious or biased output, unsafe automated action, intellectual-property concern, and material factual error. Align reporting deadlines and notices with applicable law and contracts.
Review and retire safely
Set a recurring policy review cadence and review after significant incidents, regulatory changes, or material shifts in capability. NIST’s AI RMF Core calls for ongoing monitoring, periodic review, inventory mechanisms, incident identification, and safe decommissioning. Retirement should address access removal, connector shutdown, data deletion or records preservation, archived decisions, replacement controls, and communication to users.
Keep legal claims scoped to the actual jurisdiction
NIST states that use of the AI RMF is voluntary and that AI RMF 1.0 is being revised. The Generative AI Profile was released on July 26, 2024. Use these publications as risk-management guidance, not as proof that a clause is legally required for every company.
DOE P 203.1, dated December 29, 2025, is an operational policy for a U.S. federal department. It is a useful example of controls for human accountability, validation, data awareness, access, provenance, intellectual property, training, and high-impact approvals; it does not automatically govern private enterprises.
EU obligations likewise depend on the organization’s role, system classification, use, location, and implementation date. Commission guidance for high-risk systems describes duties that can include following instructions, monitoring, competent human oversight, and—where the deployer controls inputs—ensuring they are relevant and sufficiently representative. Public-authority, workplace, and decisions affecting people may trigger additional requirements. A company policy should direct owners to obtain jurisdiction-specific legal review rather than promise compliance merely because the policy exists.
Turn the policy into a working control system
- Assign an executive sponsor and named owners for security, privacy, legal, procurement, HR, records, accessibility, and business risk.
- Inventory AI systems and use cases, including embedded features, open-source components, APIs, and contractor use.
- Publish data-classification rules and the approved-tool register.
- Require context and risk intake before new or materially changed use.
- Document human roles, verification steps, stop conditions, and escalation for each approved use.
- Contract for appropriate provider transparency, security, retention, incident cooperation, and continuity.
- Train users and reviewers, then measure completion and recurring questions.
- Monitor performance, incidents, model changes, and impacts; review the policy on a defined cadence.
- Retire systems safely when controls no longer match the risk or the service is no longer needed.
The result is not a blanket permission slip or a list of banned prompts. It is a living set of decisions that connects acceptable use to context, data, people, suppliers, evidence, and accountability.
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