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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchAn enterprise AI acceptable-use policy should do more than tell staff to “use AI responsibly.” It must define which systems and people are covered, approve uses according to risk, block unsafe data disclosures, assign human accountability, require output verification, control suppliers, and remain current through monitoring and review. Treat it as one control in a broader AI-governance process with named owners, an inventory of systems and use cases, risk assessments, training, incident response, and change control.
Start with a policy that covers the whole AI estate
Employees encounter artificial intelligence in more places than centrally purchased applications. The policy should cover generative and predictive systems, conversational interfaces, code assistants, application programming interfaces, open-source models, model-hosting services, and AI features embedded in ordinary business software. It should also apply when a worker connects an approved application to an external model or data source.
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List the people who must follow the rules: employees, temporary workers, contractors, consultants, interns, suppliers operating on the company’s behalf, and other third parties with access to company information. NIST’s Generative AI Profile specifically recommends updating acceptable-use policies for proprietary and open-source technologies and data and extending them to contractors and other third-party personnel.
Define terms once and use them consistently
Include a definitions section for “AI system,” “generative AI,” “approved tool,” “sensitive data,” “high-impact use,” “human reviewer,” and “AI-generated content.” Explain how the AI policy relates to existing information-security, privacy, records-management, procurement, intellectual-property, employment, accessibility, and acceptable-use policies. A conflict rule should say which policy controls when requirements differ and who resolves ambiguity.
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State the policy’s purpose and limits
Explain that the policy manages operational, legal, security, privacy, fairness, intellectual-property, and reputational risks. It does not replace laws, regulations, contracts, professional duties, or sector-specific controls. NIST describes its AI Risk Management Framework (AI RMF) as voluntary guidance, not a legal safe harbor or a ready-made corporate policy.
Use an approved-tool and approved-use model
Maintain a searchable approved-tool register or a documented route for obtaining approval. Each entry should identify the system owner, provider, model or relevant components, intended business purpose, permitted data classes, retention and training terms, known limitations, risk tier, review date, and fallback arrangement.
Permit routine work only when the controls fit
Examples may include drafting, summarizing, information retrieval, translation, brainstorming, or code assistance. The policy should make clear that a routine label does not make every prompt safe: the data entered, affected people, and consequences of error determine whether the use is acceptable.
Prohibit or restrict uses that exceed risk tolerance
Give concrete examples tailored to the organization. Restrictions commonly include:
- Submitting confidential, personal, regulated, customer, employee, credential, source-code, or third-party information to an unapproved service.
- Bypassing identity, access, security, or records controls by using a model or connector.
- Impersonation, deception, fraud, harassment, unlawful discrimination, or content that violates contractual or legal duties.
- Using unverified generated material as the sole basis for a consequential decision about a person, customer, worker, applicant, patient, borrower, or supplier.
- Deploying generated code, configurations, or automated actions without the testing and security review required for that environment.
There is no universal prohibited-use list. The examples must reflect the company’s values, jurisdictions, contracts, industry rules, and risk appetite.
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Require risk review before deployment or material change
Every new AI system and every materially different use should pass an intake before production use. NIST’s AI RMF treats context mapping and risk tolerance as foundations for an initial go/no-go decision, with reassessment when the context, capability, risk, or impact changes.
Capture the facts an approver needs
- Business purpose, expected benefit, users, affected people, and operating context.
- Model, provider, connected applications, data sources, geographic processing locations, and subcontractors.
- Input and output data classes, retention, training use, access controls, logging, and deletion or records requirements.
- Known limitations, foreseeable misuse, bias and security concerns, rights implications, and applicable legal or contractual duties.
- Testing methods, acceptance criteria, human-review design, monitoring metrics, incident thresholds, and an exit or fallback path.
- A named business owner who accepts residual risk and a named technical or domain reviewer.
Scale approval to consequence
Define organization-specific tiers rather than pretending every use needs the same process. A low-consequence drafting aid may be approved by a trained business owner after security and data checks. A system that influences employment, eligibility, safety, health, finances, access to services, or other rights should trigger documented review by appropriate legal, privacy, security, compliance, HR, accessibility, procurement, and subject-matter experts. The policy should state who can approve each tier, when a committee or executive must decide, and how an employee can stop or escalate a use.
Set data, privacy, security, and records rules before prompting
Write a data matrix that maps each information classification to permitted environments. Cover prompt text, uploaded documents, images, audio, connected repositories, fine-tuning data, feedback, telemetry, and generated outputs. A user must confirm that the organization has authority to use the information and that provider terms do not permit unintended retention, training, disclosure, or reuse.
Specify the technical controls
- Use enterprise identity, least privilege, role-based access, and separate administrator and user privileges.
- Restrict connectors to the minimum repositories and fields needed for the approved purpose.
- Log access, prompts, files, model versions, outputs, approvals, and administrative changes where lawful and proportionate.
- Define retention periods for prompts and outputs, legal-hold handling, records schedules, deletion requests, and secure disposal.
- Protect credentials, secrets, personal data, regulated records, and source code from accidental inclusion or model memorization.
- Review provider encryption, isolation, incident notification, subprocessors, and cross-border processing terms.
Department of Energy policy P 203.1, dated December 29, 2025, illustrates a federal approach in which access controls cover information input to, maintained by, or output from a model and align with records requirements. Private enterprises must map those controls to their own obligations rather than treating the federal policy as binding law.
Keep a human accountable for consequential use
For every approved use, identify whether AI is a drafting aid, recommender, classifier, or actor capable of triggering an action. Name the business owner and the person responsible for review. The policy should say what the reviewer must independently check, what evidence must be retained, and who can reject, pause, or escalate an output.
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Match review to the harm of error
- Drafting aid: Check factual accuracy, confidentiality, tone, and rights before sharing.
- Recommender: Verify the underlying data, assumptions, alternatives, and reasons for accepting or rejecting the recommendation.
- Classifier or scorer: Test accuracy and disparate impact in the deployment context, document thresholds, and provide qualified human review before an adverse action.
- Action-taking system: Require authorization gates, narrow permissions, transaction limits, logging, rollback, and a rapid stop mechanism.
Reviewers need competence, authority, sufficient time, and a clear escalation route. A model does not assume the organization’s legal or managerial responsibility. NIST’s AI RMF calls for differentiated human and AI roles and oversight configurations.
Verify outputs, code, citations, and published material
Require proportionate validation before anyone relies on an output. Important facts, calculations, legal or compliance statements, citations, recommendations, translations, and summaries should be checked against authoritative sources. Generated code requires peer review, testing, dependency and vulnerability checks, license review, and the normal change-approval process before deployment.
Tests performed in a laboratory or benchmark setting may not predict performance in the actual operating context. Validation should use representative data and realistic workflows, with checks for hallucination, bias, security weaknesses, personal information, unsafe instructions, and context-specific failure modes.
Set publication and disclosure rules
For customer-facing or public material, require factual, confidentiality, intellectual-property, accessibility, and rights review. State when employees must identify AI assistance internally and when content should be labeled or disclosed externally under a contract, organizational rule, or applicable law. Do not impose a blanket rule that every AI-assisted item must be disclosed.
For European 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 certain public-interest text produced without human review or editorial control. Coverage, exceptions, and duties depend on the system, role, use, and jurisdiction; the article is not a universal disclosure checklist.
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Protect intellectual property and output rights
Users must have the right to submit input material and must respect confidentiality, licenses, privacy, and contractual restrictions. Before external use, review outputs for copied passages, third-party code, trademarks, personal information, trade secrets, and other protected material. Define who may store, edit, publish, or license outputs and how records are retained.
NIST identifies intellectual-property risks from third-party integrations and calls for due diligence and transparency. DOE P 203.1 likewise directs users to understand applicable intellectual-property restrictions and recognizes the possibility of infringement in generated output.
Apply procurement and supply-chain controls
Extend existing supplier-security and procurement reviews to foundation-model providers, hosted APIs, fine-tuned models, open-source components, and AI features embedded in SaaS products. Ask vendors for relevant information about training and input-data use, retention, security architecture, model and service limitations, incident handling, subprocessors, service locations, and change-notification practices.
Depending on risk, require contractual commitments for confidentiality, deletion, breach notification, audit or assessment rights, service levels, portability, support, and cooperation with investigations. NIST lists software bills of materials, service-level agreements, and attestation reports as possible third-party risk-management measures.
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Document what happens if a provider changes a model, suffers an outage, introduces unacceptable behavior, loses data, changes its terms, or experiences a security incident. Keep a tested fallback, define who can suspend use, preserve evidence needed for investigation, and rehearse the response for high-impact systems.
Train users, approvers, and reviewers
Training should be role-based and refreshed when tools or rules change. Every user should understand approved tools, prohibited data, verification duties, model limitations, bias and discrimination risks, intellectual property, disclosure, and incident reporting.
Approvers and reviewers need additional instruction on risk classification, representative testing, human-oversight design, documentation, accessibility, and escalation. DOE’s federal policy identifies policy compliance, responsible use, bias and discrimination, hallucinations and limitations, data handling, disinformation or malicious outputs, and security-incident reporting as training subjects.
Make the policy accessible to contractors and other affected staff. Provide a question channel and a good-faith escalation route so uncertainty is reported rather than hidden.
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Maintain an inventory of approved systems, models, owners, purposes, data classes, risk tiers, approvals, and review dates. Monitor changes in purpose, model version, connected data, behavior, performance, security, user feedback, and impact. A material change should reopen the risk review rather than pass unnoticed as routine maintenance.
Define incident reporting and response
Tell users how to report sensitive-data exposure, unsafe or discriminatory outputs, security events, intellectual-property concerns, harmful decisions, material factual errors, provider incidents, and unauthorized use. Assign response owners, escalation thresholds, evidence-preservation steps, notification responsibilities, and criteria for suspending a system. Align reporting with applicable breach, privacy, employment, sector, and contractual requirements.
Review the policy on a schedule
Set a recurring review cadence and trigger reviews after incidents, regulatory changes, major model updates, new connectors, or changes in business purpose. Retire systems with documented data export or deletion, credential revocation, contract closure, records handling, user communication, and lessons learned.
Understand what the major frameworks do—and do not—require
| Source | Status and date | How to use it in a company policy |
|---|---|---|
| NIST AI Risk Management Framework 1.0 | Voluntary framework; NIST says it is being revised | Use its governance, context, human-oversight, measurement, monitoring, and lifecycle concepts as a structure for controls. It is not a private-sector legal mandate. |
| NIST Generative AI Profile (NIST AI 600-1) | Released July 26, 2024 | Use its guidance on acceptable use, third-party integrations, data protection, testing limits, procurement, training, incidents, and fallback planning. |
| EU AI Act Article 50 | Transparency obligations described by the European Commission as applying from August 2, 2026 | Determine whether the organization, system, content, interaction, and exception fit the provision; do not treat it as a rule that every AI output must be labeled. |
| DOE P 203.1 | U.S. Department of Energy policy dated December 29, 2025 | Use as an operational federal example covering accountability, validation, access, provenance, intellectual property, training, and high-impact approvals. It does not govern private companies generally. |
NIST summarizes the governance principle this way: “Attention to governance is a continual and intrinsic requirement for effective AI risk management over an AI system’s lifespan and the organization’s hierarchy.” Its Generative AI Profile also says that acceptable-use guidance can reduce risks from misuse, abuse, inappropriate repurposing, and misalignment between systems and users. These statements support a living governance process, not a one-time policy publication.
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