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A workable AI use policy tells your team which AI tools and tasks are allowed, what information may be entered, when a person must check the output, who approves higher-risk uses, and what happens when something goes wrong. Write it for the systems your team actually uses, connect it to existing privacy and security controls, assign named owners, train employees with concrete examples, and set review triggers so the policy changes with the technology and your obligations.
Start with the policy’s job, not a generic ban
An AI use policy is an internal operating rule. It is not a list of impressive principles that nobody can apply, and it is not a substitute for privacy, security, records-management, employment, safety, or sector-specific requirements. Its job is to turn your team’s actual AI activities into clear decisions:
- Which tools, models, integrations, and internally built systems are covered.
- Which people, contractors, offices, and business processes must follow the rules.
- Which uses are permitted, restricted, or prohibited.
- What data can be supplied to each tool and how outputs may be used.
- What human review is required before an output is acted on or shared.
- Who owns the policy, approves exceptions, reviews risk, and receives incident reports.
- How the organization monitors performance, handles failures, and updates the rules.
Keep the policy proportional to risk. A drafting assistant that never receives confidential information needs different controls from a system that recommends medical, credit, employment, safety, or customer-service decisions. NIST’s AI Risk Management Framework (AI RMF) recommends defining scope, purposes, and intended uses for both deployed systems and third-party systems; use that as a design prompt rather than copying a one-size-fits-all document.
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Inventory what “AI” means for your team
List the technologies people may encounter, including public chatbots, enterprise assistants, image and audio generators, code tools, machine-learning services embedded in other software, automated decision systems, and models developed internally. Include tools procured by a department, connected through an application programming interface, or enabled as a feature inside an approved application. If a vendor changes a feature or model without your team deploying anything new, the system is still within scope.
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State whether the policy covers only generative AI or all automated and predictive systems. A narrow generative-AI policy can coexist with a broader algorithmic-decision policy, but say where the boundary lies and which document controls when both apply.
Identify covered people and work
Specify employees, temporary workers, contractors, interns, consultants, and anyone using organizational accounts or information. Clarify whether personal accounts may be used for work. List covered activities such as research, drafting, translation, coding, analysis, customer support, marketing, recruiting, documentation, and decision support. Explain that an employee remains responsible when an AI feature is built into a familiar product; “the software suggested it” is not an exemption.
Define terms people can apply
Use short definitions for terms such as AI system, generative AI, personal data, confidential information, high-impact decision, human review, approved tool, and incident. Avoid assuming that every employee understands the difference between a model, an application, a plug-in, and a data processor. A definition should answer what a worker must do, not reproduce technical jargon.
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Employees need a fast answer before they paste a prompt or accept an output. Publish a table like the following, then replace the examples with your team’s real work. It is an operating model, not a universal legal classification.
| Use category | Typical examples | Minimum conditions | Approval or review |
|---|---|---|---|
| Permitted | Brainstorming, outline creation, rewriting non-confidential text, summarizing public material, generating test data that contains no real personal information | Use an approved account and remove information that is not needed. Check the result for accuracy, copyright or licensing concerns, and unintended disclosure before use. | Routine human review by the person using the output |
| Restricted | Internal analysis, code suggestions for production systems, customer communications, translation of non-public material, processing personal or commercially sensitive data | Use only a tool and configuration approved for that data class. Follow retention, access, logging, and transfer controls. Keep a record of material review. | Process owner or designated privacy, security, legal, or risk reviewer, according to the use |
| High-impact | Recommendations that could affect employment, access to essential services, safety, health, credit, insurance, discipline, or a person’s rights | Document purpose, data sources, limitations, testing, monitoring, human decision authority, and a route for challenge or correction. Do not let an unreviewed output make the final decision. | Formal risk assessment and named accountable decision-maker before deployment; ongoing monitoring |
| Prohibited unless expressly authorized by a separate control | Entering secrets or regulated data into an unapproved public service; deceptive impersonation; bypassing security controls; generating unlawful or harmful content; making a consequential decision solely from an unverified output | Do not perform the activity. Escalate a business need or suspected violation through the stated route. | Executive, legal, privacy, security, or risk authority defined by your governance model |
Explain that an example is not exhaustive. The same task can move categories when the data, audience, model, or consequence changes. Summarizing a public press release is not equivalent to summarizing a patient record; drafting a low-risk internal note is not equivalent to sending an automated denial to a customer.
Write data-handling rules that match existing controls
Classify information before it enters a tool
Reuse your organization’s existing data-classification labels. For each label, say whether the information may be entered, under what conditions, and into which approved tools. A useful rule distinguishes at least public, internal, confidential, personal, regulated, and security-sensitive information. If your classification system uses different names, use those names consistently rather than creating a parallel taxonomy.
- Public: Information already approved for public release may generally be used in an approved tool, subject to accuracy and intellectual-property review.
- Internal: Use an organization-managed account and follow access and retention settings. Do not assume that “internal” means harmless to disclose.
- Confidential or trade-secret: Require an approved environment with contractual, technical, and administrative safeguards. Minimize the content and record the permitted purpose.
- Personal or regulated: Apply the privacy, security, retention, transfer, and subject-rights rules that already govern the data. If the policy cannot identify those controls, the use is not ready for approval.
- Credentials, keys, exploit details, or other security secrets: Never paste them into a general-purpose service. Use the organization’s designated secret-management and security processes.
Apply data minimization and output controls
Tell users to provide the least information needed, redact names and identifiers where possible, and use synthetic or masked data for experiments. Clarify whether a provider may retain prompts, use them for service improvement, transfer them across borders, or permit human access. Those facts must come from the organization’s approved vendor and privacy review; the policy should not promise settings that have not been verified.
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Controls also apply after generation. Store outputs only in approved locations, label drafts when they have not been checked, restrict access to sensitive results, and delete temporary files according to the existing retention schedule. An output can contain personal or confidential information even when the prompt did not, so apply the same classification test to the result.
Make human review specific and accountable
“Human in the loop” is too vague to protect anyone. Define what the reviewer must check, when the check occurs, and who has authority to reject or override the output.
Set a review depth by consequence
- Routine assistance: The user checks factual accuracy, relevance, tone, obvious bias, and unintended disclosure before incorporating the result.
- External or customer-facing material: A suitably knowledgeable person verifies claims, promises, citations, calculations, accessibility, and compliance with communication standards before release.
- Production code or configuration: A qualified engineer tests the code, reviews dependencies and security implications, and follows the normal change-control process. AI-generated code does not bypass peer review or testing.
- High-impact recommendations: A named decision-maker reviews the underlying evidence, model limitations, alternatives, and the affected person’s opportunity to correct relevant information. The human must be able to disagree rather than merely approve a score.
Document material use
For higher-risk work, require a record of the tool and version or feature, purpose, relevant data class, date, reviewer, material prompts or inputs where retention is permitted, checks performed, decision, and any known limitation. Do not require employees to retain sensitive prompts indefinitely; align records with your retention and privacy rules.
Check common failure modes
Reviewers should look for fabricated facts and citations, outdated information, arithmetic errors, insecure code, discriminatory patterns, unauthorized personal data, hidden instructions in retrieved content, and overconfident language. Require a second subject-matter review when the first reviewer lacks the expertise needed to detect a domain error.
Design governance, ownership, and escalation
A policy fails when nobody can answer “Who decides?” Assign roles in the document and name an accountable position or team, not an abstract committee.
| Role | Core responsibility | Decision or evidence to maintain |
|---|---|---|
| Policy owner | Maintains the policy, coordinates reviews, publishes changes, and tracks exceptions | Current policy version, review dates, change log, and exception register |
| Business or process owner | Explains the intended use, expected benefit, users, workflow, and consequences of failure | Use-case description, success criteria, affected stakeholders, and operating procedure |
| Privacy or data-governance reviewer | Assesses personal data, lawful handling, minimization, retention, transfers, and individual rights under applicable rules | Data map, classification, privacy assessment, and required safeguards |
| Security reviewer | Assesses access, authentication, secrets, integrations, logging, supply-chain exposure, and abuse scenarios | Threat assessment, configuration requirements, test results, and monitoring plan |
| Legal, compliance, or risk reviewer | Interprets obligations and risk tolerance for the relevant sector, geography, contract, and decision | Advice, conditions of approval, and unresolved questions |
| Human decision-maker | Owns the final action where an AI output informs a consequential decision | Rationale, evidence considered, review performed, and route for challenge |
| All users | Follow the policy, protect information, verify outputs, and report concerns promptly | Required training completion and incident or question submissions |
State who can approve an exception, its maximum duration, required compensating controls, and how it expires. A temporary exception should have a business reason, owner, end date, and review condition; “approved forever” is not an exception process.
Use a repeatable approval workflow
Give teams a path from an idea to an approved use. The following sequence can be adapted to a small team or a formal review board.
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- Describe the use. Record the task, users, affected people, expected benefit, system, integrations, data sources, output destination, and what happens if the output is wrong.
- Classify the risk. Consider data sensitivity, impact on people, degree of automation, reversibility, scale, exposure to external users, and dependence on a third party. Assign a preliminary category using the organization’s table.
- Choose the environment. Confirm account ownership, access controls, retention, logging, model-training settings, geographic processing, contractual terms, and whether the feature is enabled by default.
- Define controls. Specify prompt and data restrictions, testing, human review, output labeling, approval gates, monitoring metrics, and a rollback or shutoff method.
- Obtain review. Route the proposal to the process owner and the privacy, security, legal, compliance, or risk reviewers required by its category. Record conditions, not just a yes or no.
- Pilot safely. Use limited users, non-production or synthetic data where possible, a defined time window, and a documented success and failure threshold. Do not quietly expand the audience during the pilot.
- Approve deployment. Name the accountable owner, publish the operating instructions, train users, and register the system or use case in the organization’s inventory.
- Operate and reassess. Monitor the agreed signals, record incidents and material changes, and revisit the approval when the tool, data, users, purpose, or consequences change.
Plan monitoring, incidents, and change management
Monitor what can drift
Choose measures that reflect the use: error or correction rates, complaints, unsafe or biased outputs, privacy events, security alerts, override frequency, rejected outputs, service changes, and whether required reviews are actually occurring. A dashboard is not a control unless someone receives it, understands thresholds, and can act.
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Make reporting easy
Provide one visible route for questions and another for urgent incidents if the organization’s normal security or privacy channels differ. Tell users what to report: accidental disclosure, suspicious prompts or files, fabricated or harmful output, discriminatory behavior, unauthorized tool use, policy ambiguity, vendor changes, and a decision made without required review. Instruct them to preserve relevant evidence without spreading sensitive content further.
Define the response
- Contain the issue: stop the workflow, revoke access, isolate affected data, or disable the integration when appropriate.
- Notify the designated incident, privacy, security, legal, or risk contacts according to existing response procedures.
- Assess scope, affected people, data, outputs, decisions, and any reporting or contractual duties.
- Correct or reverse the result where possible, and communicate with affected stakeholders through the responsible owner.
- Document root cause, decisions, evidence, corrective actions, and the person accountable for closure.
- Update the policy, training, configuration, or approval conditions and verify that the fix works.
Set review triggers, not only a calendar date
Review the policy on a stated cadence and whenever a new tool or model is introduced, an existing feature changes materially, a new data class or use case appears, an incident occurs, monitoring shows unexpected behavior, or applicable requirements change. Record the version, effective date, owner, approvers, and a concise change summary. Employees should be able to find the current version and see what changed.
Train people with examples they recognize
Training should be role-based and short enough to repeat. Every user needs the core rules; reviewers, developers, managers, and incident responders need additional instruction.
- All users: approved tools, prohibited data, safe prompting, verification, output handling, reporting, and where to ask questions.
- Managers and process owners: risk classification, approval gates, human accountability, monitoring, and exception decisions.
- Developers and administrators: access control, secrets, testing, logging, dependency risk, model and vendor changes, and rollback.
- Privacy, legal, security, and risk reviewers: assessment methods, evidence standards, escalation, and how controls map to existing obligations.
- Decision-makers: how to challenge an output, document independent judgment, and avoid treating a model score as a final decision.
Use paired examples: “You may ask an approved tool to rewrite a public announcement”; “You may not paste a customer case containing identifiers into a public account.” Explain why the distinction matters and show the approved alternative. Require acknowledgement and refresh training when the policy or a material tool changes.
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Connect the policy to law and existing governance
No universal AI policy can establish legal compliance without knowing the organization’s jurisdiction, sector, data, contracts, and use cases. Ask the responsible internal owners to test the draft against applicable privacy and data-protection rules, employment and discrimination requirements, intellectual-property and confidentiality duties, records retention, cybersecurity controls, accessibility, procurement terms, and any sector or safety obligations. The applicable requirements can differ by industry, business purpose, geography, and application context.
Cross-reference existing policies instead of copying them. The AI policy should point to the authoritative rules for information classification, secure development, vendor management, incident response, records, acceptable technology use, and customer communications. Where those documents conflict, state which owner resolves the conflict and which stricter control applies during review.
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Use NIST and OECD guidance without treating it as a checklist
NIST describes the AI RMF as voluntary guidance. Its Playbook organizes suggested actions around Govern, Map, Measure, and Manage, but explicitly says, “The Playbook is neither a checklist nor set of steps to be followed in its entirety.” Select the actions that fit your risk and document why important suggestions are out of scope.
NIST’s AI RMF 1.0 was released on January 26, 2023. Its Generative AI Profile was released on July 26, 2024. The framework is being revised, and the current overview records an April 7, 2026 concept note for a critical-infrastructure profile. Check the live NIST materials when you review your policy rather than assuming the framework is static.
The OECD’s 2025 report Governing with Artificial Intelligence describes a generative-AI policy as setting acceptable-use terms and establishing safeguards and oversight. It also emphasizes organizational guidance and workforce AI literacy. Examples in that report concerning federal government are not automatically requirements for a private team or an organization in another jurisdiction; use them as ideas to tailor, not as a legal conclusion.
Adaptable policy clauses you can use
The following wording is a starting point. Replace broad terms with your organization’s defined classifications, owners, systems, and escalation channels.
Purpose and scope
“This policy governs the use, development, procurement, configuration, and monitoring of artificial-intelligence systems for organizational work. It applies to employees, contractors, and other authorized users, including use of AI features embedded in approved applications. It covers currently deployed systems and third-party services.”
Acceptable use
“Users may use an approved system for a documented business purpose and only within the data, review, and access conditions assigned to that system. Users must verify material outputs and remain responsible for work submitted, communicated, or used in a decision.”
Data protection
“Users must not enter confidential, personal, regulated, or security-sensitive information into a system unless the responsible data and security owners have approved that system and its configuration for the relevant data class. Users must minimize inputs, follow retention rules, and protect generated outputs according to their classification.”
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Human responsibility
“AI output is advisory unless a separately approved process states otherwise. A named human decision-maker must review and be able to reject output used in a high-impact decision. Required review must be documented at the level specified by the use-case approval.”
Incidents and questions
“Users must promptly report suspected disclosure, harmful or discriminatory output, unauthorized use, security concerns, material errors, or uncertainty about this policy through the designated reporting channel. Reporting a concern will not by itself be treated as misconduct.”
Exceptions and review
“Exceptions require written approval from the designated authority, a stated business reason, compensating controls, an owner, and an expiration date. The policy owner will review this policy on the published cadence and after material tool, use-case, incident, or requirement changes.”
Rollout checklist
- Inventory deployed, embedded, third-party, and internally built AI systems.
- List covered users, processes, data classes, and decision types.
- Publish permitted, restricted, high-impact, and prohibited-use examples.
- Map each tool to approved data, account, retention, access, and logging settings.
- Assign policy, process, privacy, security, legal or risk, and decision owners.
- Define review depth, evidence, approval gates, exceptions, and escalation.
- Register monitoring measures, thresholds, incident routes, and rollback actions.
- Deliver role-based training and record completion.
- Set calendar and event-based review triggers; publish version history.
- Have the relevant internal owners validate the policy for jurisdiction, sector, geography, data, contracts, and actual use cases.
Common failure modes and fixes
A blanket prohibition that people ignore
If employees have legitimate productivity needs, an absolute ban can push use into unapproved accounts. Define safe, approved paths and explain which risks the controls address. Keep prohibitions for activities the organization genuinely cannot accept.
A list of approved tools with no task boundaries
Approval is not transferable from one use to another. Record the allowed purpose, data class, audience, human review, and integrations for each system. Reassess when any of those change.
“Check the output” with no standard
Specify accuracy, privacy, security, bias, accessibility, licensing, and domain checks appropriate to the consequence. Name the qualified reviewer and the evidence to retain.
Governance without a decision owner
A committee can advise, but one role must own each approval and final decision. Publish the escalation path and response time for urgent concerns.
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Use examples tied to the tools people actually see, announce changes, and trigger refresh training after a material feature, policy, or risk change. Measure whether users can make the right classification decision, not only whether they clicked “complete.”
Copying a framework word for word
Frameworks help identify controls; they do not know your data, contracts, people, or consequences. Explain how selected guidance maps to your existing governance and where a responsible owner made a different, documented choice.
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