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Write an AI use policy as a practical decision guide, not a list of abstract warnings. Tell employees which tools and tasks are approved, what information must stay out of them, when a person must check an output, when AI use must be disclosed, and whom to contact when something goes wrong. The policy should also assign an owner, explain recordkeeping and training, and set a review schedule. Employees should be able to use it to answer, “Can I do this with this tool and this data?” before they paste, publish, or act on an AI-generated result.
What should an AI use policy include?
A useful policy covers the whole path from choosing a tool to reviewing its output and reporting a problem. It should be specific enough to guide an ordinary workday, while making clear that existing privacy, security, records, intellectual-property, anti-discrimination, accessibility, and professional-conduct requirements still apply.
Include these elements, each with an accountable owner:
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- Approved tools and purposes: a maintained inventory showing permitted tasks, data classes, tool owners, retention settings, and review status.
- Allowed, restricted, and prohibited uses: examples employees can recognize, plus a route to request approval for a new use.
- Data handling: clear rules for personal, confidential, proprietary, regulated, and security-sensitive information.
- Human accountability: required checks, decision authority, override rights, and escalation for consequential uses.
- Transparency and records: when to disclose AI assistance and what use information to retain.
- Training, incidents, enforcement, and review: how workers learn the rules, raise concerns, and get current guidance.
NIST’s AI Risk Management Framework describes organizational practices for managing AI risks, including documented responsibilities, communication, inventories, and review. Its framework is voluntary; it is a way to structure governance, not a substitute for an organization’s legal or contractual duties. NIST released the framework’s 1.0 version on January 26, 2023, and its Generative AI Profile on July 26, 2024.
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How should you define the policy’s scope and ownership?
Define the boundary before listing rules. State who must follow the policy, what counts as AI for its purposes, which work activities are covered, and which organization-approved tools are in scope.
Specify people, systems, data, and outputs
Say whether the policy applies to employees, temporary workers, contractors, or other people acting for the organization. Define the covered systems broadly enough to address the actual work environment: for example, generative assistants, AI features embedded in approved software, and systems that automate or support decisions. Explain whether personal accounts and tools used on personal devices are covered when they are used for work.
Define covered inputs and outputs too. Prompts, uploaded files, generated text, code, summaries, recommendations, and decisions may all create risks. A rule that only addresses “AI-generated documents” can leave out the data employees submit or the recommendations they act on.
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Identify the policy owner and the roles that approve tools, use cases, exceptions, and high-impact deployments. Depending on the organization, responsibilities may involve information security, privacy, legal, records management, accessibility, human resources, procurement, or a business lead. Give employees a practical contact route rather than telling them only to “consult management.”
Make approval responsibilities explicit: who decides whether a new tool is safe for a data class, who accepts a use case’s risks, and who can stop or suspend a use. NIST recommends documenting roles and responsibilities and maintaining an AI-system inventory. The Information Commissioner’s Office (ICO) likewise advises senior management to review and sign off an AI system’s intended use in line with the organization’s risk appetite.
How do you decide which AI tools and uses are approved?
Separate tool approval from use-case approval. A system may be permitted for one task and data type but unsuitable for another, so an approved-tool list should say what employees may use it for—not just name a product.
Maintain a usable register
For each approved system, record its owner, approved purposes, permitted data classes, relevant settings such as retention, and the date or status of its review. Keep the register easy to find and update it when a tool or its use changes. NIST recommends an AI-system inventory and attention to third-party systems.
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Do not ask employees to infer approval from a vendor’s reputation, the fact that a tool is available in a browser, or a colleague’s use. Make the approved route clear and explain how to request review when the register does not cover a task.
Choose the level of control deliberately
Organizations can choose a more permissive, controlled, or prohibition-heavy policy. None is automatically right for every organization: the choice depends on its data, work, risk tolerance, and ability to govern tools.
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| Policy approach | Potential advantage | Trade-off to manage |
|---|---|---|
| Permissive, with baseline safeguards | Employees have flexibility to explore useful tasks. | It can be harder to control data exposure, track tools, and maintain consistent review. |
| Controlled, with approved tools and use cases | Approval and inventory processes give the organization more visibility over use. | Review and training take time; unclear or slow approval routes may discourage compliance. |
| Prohibition-heavy, with narrow exceptions | It limits the range of uses employees may undertake without approval. | Employees may still use tools outside the approved process unless the organization makes compliant options and escalation routes practical. |
Compare the approaches against employee usefulness, confidentiality risks, the human-review burden, discrimination and legal exposure, auditability, training needs, vendor controls, and how easily the rules can be updated. Treat this as a governance decision, not a promise that a particular policy style eliminates risk.
What uses can employees be allowed to make?
Give recognizable examples of low-risk work, but label them as examples subject to your organization’s approval and data rules. An apparently simple task can still be unsuitable if it involves restricted information, an unapproved system, or an output that affects someone’s rights or interests.
Examples to consider for approval
Depending on the tool and safeguards, an organization might allow employees to brainstorm, format text, summarize non-confidential material, translate general content, or ask for an explanation of code. Make clear what “non-confidential” means in your environment, and whether generated material needs review before it is shared or used.
Be equally clear about tasks that need additional review before they enter production. A tool that helps draft a document, for instance, does not thereby receive authority to make the decision described in that document. The policy should distinguish assistance with a task from delegation of accountability for its result.
Make the examples organization-specific
For each permitted example, state the approved system or route, acceptable input, expected human check, and any disclosure or recordkeeping requirement. If those details are not yet established, say that the use is not approved until the designated owner reviews it. Do not present a generic example as organizational permission.
Can employees paste company data into ChatGPT?
They should not enter personal, health, confidential, proprietary, regulated, or otherwise restricted information into a public or unauthorized AI system unless an approved secure process explicitly permits it. The policy should make this boundary easy to recognize and give employees a safe alternative when a task depends on such information.
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Define restricted information in workplace terms
List the data classes your organization uses and give examples employees can identify. Explain that information does not become safe to submit merely because a prompt omits a person’s name; a combination of details may still identify someone or expose confidential work. Address documents and file uploads as well as text pasted into a prompt.
CMS and IRS guidance make restricted-input rules operational rather than leaving them as general cautions. For instance, the IRS GenAI policy section dated February 10, 2026, supplies examples of restricted inputs and calls for direct human review. Those agencies’ guidance applies in their contexts; it is not, by itself, a rule for every employer. Organizations should translate their own legal, sector, and contractual obligations into the policy’s data categories.
Give employees a safe next step
Tell employees to stop before submission when they cannot determine a data class or tool approval. Identify the privacy or security contact, policy owner, or approved secure workflow that can answer the question. A prohibition without a response path can leave an employee unsure whether to abandon a legitimate task, find another tool, or risk proceeding.
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When must a human review an AI decision?
A qualified person should review an output before the organization relies on it, and AI should not be the sole basis for consequential decisions about people. The reviewer must have enough context and authority to challenge, change, reject, or escalate the result—not merely approve it automatically.
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Require a defined human review and escalation process when an AI output could affect someone’s employment, rights, safety, finances, or access to services. The policy should identify who can make the final decision, what evidence that person must consider, and how the affected decision can be reconsidered when appropriate. Do not let an opaque recommendation become an unreviewed decision about a person.
Review should account for discrimination, accessibility, and privacy impacts, as well as the purpose and context of the decision. A review that checks only whether the output sounds plausible does not address whether its use is fair or suitable.
Use a pre-reliance checklist
Before relying on an AI-assisted output, ask the reviewer to check:
- Accuracy: Are factual claims and calculations verified against trustworthy sources or records?
- Source quality: Can the important claims be traced to evidence that is relevant and current?
- Privacy and confidentiality: Was information handled within approved boundaries, and does the output expose anything it should not?
- Fairness and accessibility: Could the result disadvantage a person or group, or exclude someone from using or understanding the work?
- Intellectual property and security: Does the output create rights, licensing, code, or security concerns that need further review?
- Fitness for purpose: Is the result appropriate for this audience, task, and consequence?
For high-impact uses, require the reviewer to record the decision and any escalation. ICO guidance emphasizes meaningful human oversight and the ability to override an AI system’s output. The policy should turn that principle into authority employees can actually exercise.
Do you have to disclose AI-generated work?
The policy should answer that by audience and use case; there is no single disclosure rule in the supplied government guidance that applies to every employer and every output. Specify when employees must tell colleagues, customers, regulators, or other stakeholders that AI assisted the work, and how much detail to provide.
Describe what disclosure should communicate
Where disclosure is required, say whether it should identify that AI was used, describe its purpose, explain relevant limitations or uncertainty, and identify the human review or safeguards applied. CMS guidance calls for communicating AI use, purpose, limitations, safeguards, and uncertainty. The appropriate detail may differ by audience and by the governing law, contract, or professional obligation.
Distinguish a disclosure obligation from a claim that a particular output is entirely AI-generated. A person may use AI for a limited drafting or analysis task and then substantially review or revise the result. The policy should tell employees what level of assistance triggers disclosure in each covered context instead of relying on an ambiguous label.
What should employees record, and how should they report an incident?
Set a proportionate recordkeeping rule and a clear incident route. Employees should know what to retain for a material use and how to report accidental disclosure, a harmful output, suspected discrimination, a security issue, or another policy violation.
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Define records by risk and purpose
For uses that require a record, specify which details to preserve. They may include the tool and model, date, purpose, material inputs and outputs, reviewer, approvals, and incidents. Explain where records belong, who can access them, and how long they are kept under the organization’s existing records requirements. Avoid implying that every exploratory prompt must be retained if the organization has not set such a rule.
CMS and IRS guidance call for disclosure, documentation, and reporting in their respective contexts. NIST’s governance guidance supports documenting policies and maintaining an inventory. Translate those principles into your organization’s own retention and records processes rather than treating agency-specific instructions as universal.
Make reporting easy to act on
Provide a known channel and a backup contact for reports involving accidental data exposure, unsafe or inaccurate content, suspected bias, unauthorized tool use, or a possible security incident. Explain what an employee should do immediately—for example, stop further use and alert the relevant privacy or security contact—without asking them to investigate or contain a technical incident on their own. State that prompt reporting is preferable to quietly trying to erase or conceal a mistake.
How do you stop shadow AI without blocking useful work?
Reduce unauthorized use by making the approved path visible, useful, and responsive. A policy alone cannot show employees every tool in use or eliminate workarounds, so pair its rules with an inventory, practical alternatives, training, and a way to request a new use.
Employees need to know how to recognize an approved system, whether embedded AI features count, and where to ask about an unfamiliar tool. Keep the register current and make approval responsibilities clear. If requests commonly involve a legitimate work need, give the relevant owner a process to evaluate them rather than leaving employees to decide informally.
Address unauthorized use in the training and enforcement sections without making reporting itself feel like misconduct. Explain consequences for violations, but also distinguish a good-faith report of an accidental mistake from deliberate bypassing of safeguards. The organization should decide and state its own enforcement approach; the policy should not leave employees to guess.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you train employees and keep the policy current?
Train workers for the AI tasks and risks they encounter, then review both the policy and approved-tool inventory on a defined schedule and after material changes. A one-time announcement is not enough if tools, data processing, or obligations change.
Make training role-based
Cover the rules employees need to apply: identifying restricted information, checking tool and use approval, reviewing outputs, disclosing assistance, retaining required records, and reporting incidents. Give additional training to people who approve tools, design or operate AI systems, or make consequential decisions using AI outputs.
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Article 4 of the EU AI Act states that providers and deployers of AI systems must take measures to ensure, to their best extent, sufficient AI literacy for staff and other people dealing with operation and use on their behalf. The Act applies according to its scope and implementation rules; it does not make every organization or every AI use subject to identical duties. NIST also supports personnel training as part of governance.
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Set review triggers and an owner
State who reviews the policy and how often. Also trigger a review when the organization adopts a new tool, changes a material data-processing practice, experiences a significant incident, or faces a relevant legal change. NIST’s governance guidance calls for monitoring, periodic review, inventory maintenance, and safe decommissioning. Define how employees will learn about a changed rule or a tool that is no longer approved.
How can you turn the policy into an employee decision guide?
Put a short decision checklist near the front of the policy or wherever employees find approved tools. It should help people pause at the point of use and know when to ask rather than guess.
- Check the tool and task: Is this system approved for this particular use?
- Check the input: Does the prompt or upload contain personal, confidential, regulated, proprietary, or security-sensitive information?
- Check the consequence: Could the output affect someone’s employment, rights, safety, money, or access to services?
- Check the result: Have facts, sources, bias, accessibility, privacy, intellectual property, security, and fitness for purpose been reviewed?
- Check obligations: Must you disclose AI assistance or preserve a record?
- Stop if uncertain: Ask the policy owner or privacy/security contact before proceeding.
Keep the checklist aligned with the full policy: it is a quick route through the rules, not a replacement for tool-specific instructions, high-impact review, or applicable law.
What legal and jurisdiction limits should the policy state?
Present the policy as an organizational control, not a universal compliance guarantee. Requirements depend on sector, purpose, data, contracts, and jurisdiction, so obtain local legal review before treating a general template as a complete compliance policy.
NIST’s framework is voluntary and should be mapped to actual legal and contractual duties. ICO guidance is under review. The EU AI Act applies according to its scope and implementation rules. CMS and IRS guidance should be understood in their respective government settings, rather than copied as if their rules automatically governed every employer. Cite the applicable rules in the policy where they matter to the organization, and assign someone to monitor changes.
Frequently Asked Questions
Where should an AI use policy sit alongside existing security rules?
Make it part of the organization’s policy system and point to the privacy, security, records, and conduct rules that govern related issues. If two documents appear to conflict, give employees a named contact and an interim instruction to pause the affected use until the owner clarifies which rule applies.
Should employees be allowed to use AI-generated code at work?
That depends on the approved tool, the code’s intended use, and the organization’s security and intellectual-property controls. A policy can distinguish code explanation or drafting from merging code into a production system, which may require additional review and approval.
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Route the report promptly to the organization’s privacy or security contact and follow its incident process; do not ask the employee to conceal the event or make unsupported claims about whether data was retained. The organization’s designated responders should assess the facts and determine any required containment or notification.
Can a small organization use the same AI policy as a large company?
It can use the same core decision questions, but the approval roles, recordkeeping, training, and review process should fit its actual work and obligations. A small organization still needs a clearly named owner and a reliable way for employees to get an answer.
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