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Build an employee AI use policy around three practical questions: which tools are approved, what information may go into them, and who must check the results before anyone relies on them. Make the rules risk-tiered rather than banning every use or permitting every task: allow low-risk work with approved tools and human review, require additional approval for consequential uses, and prohibit unsafe or unauthorized activity. A short employee standard works best when it is backed by named owners, training, monitoring, incident reporting, and a scheduled review.
What should an enterprise AI use policy do?
An enterprise AI use policy should tell employees what they may do with AI at work, which tools and data are permitted, and what responsibilities remain theirs. It should cover generative AI as well as other AI systems if employees use them, and it should distinguish public external services from organization-hosted tools or services covered by contracts and company controls.
The policy is not just a list of forbidden prompts. It sets a usable baseline for employees while giving the organization a way to approve tools, manage risk, protect information and intellectual property, and respond when something goes wrong. FS-ISAC’s 2023 framework is expressly a guide for firms designing acceptable-use rules for external generative-AI services; it frames the purpose around protecting intellectual property, reflecting company ethics and legal obligations, and addressing applicable privacy and regulatory requirements.
Adoption is moving quickly even in government. The U.S. Government Accountability Office reported that, across 11 selected federal agencies, reported AI use cases rose from 571 in 2023 to 1,110 in 2024, while reported generative-AI use cases rose from 32 to 282. These are counts of reported use cases in those selected agencies, not measures of private-sector adoption or employee compliance. They do show why a policy should offer a clear route for approved use rather than leave employees guessing.
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How should you choose a policy posture?
Choose the level of access that fits your data, legal obligations, risk tolerance, and ability to oversee use. FS-ISAC presents both permissive and stringent guidance, so an organization can make a deliberate trade-off instead of treating one posture as universally right.
| Policy choice | Data sensitivity allowed | Approved-tool scope | Review and use-case impact | Training, monitoring, exceptions, and review |
|---|---|---|---|---|
| Permissive | May allow a broader range of data in approved settings, subject to explicit restrictions and applicable obligations. | Broad approved-service list; employees still use only listed tools. | Human checks remain necessary; additional review is appropriate for consequential use. | Training, logging, exception handling, and review cadence must be defined by the organization. |
| Managed access | Public and internal information may be allowed where the tool and use are approved; sensitive categories require explicit authorization. | Approved services are tied to owners, business purposes, and permitted data classes. | Review depth increases with the consequence of error or harm. | Use role-based access, employee training, logging, an exception route, and a fixed review cadence. |
| Restrictive | Only public or specifically authorized information is allowed; sensitive data is blocked by default. | Small allowlist, potentially limited to organization-hosted or contract-governed tools. | Require elevated review for higher-impact work and prohibit unauthorized consequential decisions. | Use tighter access and monitoring; define required training, exceptions, and review cadence. |
This table describes policy design options, not a ranking or a legal standard. The right choice depends on the organization’s environment. A small approved-tool list can be easier to govern, but it may constrain legitimate work; wider access can support more uses, but it increases the importance of access controls, employee understanding, and monitoring. Set the posture by data class and use case, not by assuming that a task such as “summarization” is safe regardless of the material being summarized.
What sections belong in the written policy?
Keep the employee-facing rules concise, but make sure they cover the controls employees need to follow and the governance process that makes those controls real.
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1. Purpose, scope, and definitions
State who is covered, including employees and contractors, and what is covered: AI services, models, plugins, integrations, prompts, generated outputs, and work products. Define the boundary between external public tools and organization-hosted or contract-governed services. Specify whether the rules apply to personal accounts used for work; a clear policy should not leave that question to employee interpretation.
Explain that AI use includes submitting information to a service, asking it to generate or transform material, and using an output in a work decision or deliverable. This helps employees recognize that a tool is still in scope when it is embedded in another application or accessed through a plugin.
2. Approved tools and access
Maintain a current approved-tool list with the service owner, business purpose, permitted data classes, and any usage conditions. Tell employees where to find that list and how to request a tool that is not on it. Approval should be specific enough to distinguish different deployments or account types of a service, rather than treating every version as interchangeable.
Limit access according to role and business need, and remove or adjust access when a person’s role changes. Microsoft’s enterprise guidance assigns customers responsibility for configuring policies, training users, managing identity and access, governing data, reviewing outputs, and mapping use to applicable requirements. An approved service does not remove those customer responsibilities.
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3. Data classification and input rules
Use data labels employees can recognize, for example public, internal, confidential, personal or regulated, and highly restricted. Explain each class using the organization’s existing information-handling rules, and say plainly which classes may be entered into which approved systems. Make the default unmistakable: do not enter confidential, personal, regulated, proprietary, export-controlled, security-sensitive, or third-party restricted information into an unauthorized AI system.
If a particular approved system may handle a sensitive class, require explicit authorization and state any additional conditions. Do not imply that a tool’s general enterprise positioning automatically authorizes every data type. The IRS’s 2026 policy, for example, prohibits uploading personally identifiable information, taxpayer information, acquisition-sensitive, classified, proprietary, and other nonpublic information to unauthorized AI systems. That is an agency-specific rule, not a universal legal list, but it illustrates why policy language should name protected categories.
Make the rule apply to details that could identify a person or organization even when names have been removed. Employees should follow the organization’s classification and de-identification standards rather than assume that removing one obvious identifier makes a record safe to submit.
4. Permitted low-risk uses
Give employees examples of routine uses that may be allowed when the tool is approved, the inputs are permitted, and a person checks the result. Examples can include drafting, brainstorming, summarizing, translation, formatting, and code assistance. State that these are examples, not blanket permissions: a summary of public material is different from a summary of confidential customer records, and drafting a routine internal note is different from generating a decision that affects someone’s rights.
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5. Restricted and prohibited uses
Identify activities that need elevated approval or are not allowed. Require additional review for AI used in decisions affecting employment, credit, insurance, health, safety, legal rights, or access to essential services. Prohibit illegal or harmful uses, deception, attempts to evade safeguards, unlicensed copyrighted inputs or outputs, and autonomous consequential decisions without appropriate human oversight.
Be specific about what “human oversight” means in your organization. It should identify who reviews the output, what they must check, and when they must escalate uncertainty rather than simply accept the system’s recommendation. Microsoft’s Code of Conduct for AI services, updated May 1, 2026, calls for human oversight of consequential decisions, disclosure when content or actions are AI-generated, lawful use, and controls against misuse. Its requirements are a reference point, not a substitute for checking the organization’s own obligations.
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6. Accuracy, accountability, and disclosure
The employee who submits, publishes, or acts on an AI output remains accountable for its use. Require review of facts, calculations, citations, security, bias, and fitness for the intended purpose. For high-impact work, define a stronger review path, such as review by a qualified subject-matter expert or a designated business owner, before the output is used.
Tell employees when they must disclose AI assistance—for example, when required by law, a client agreement, the organization’s policy, or the expectations of the audience. Disclosure does not make an unchecked output reliable, and human review does not excuse the use of unauthorized inputs. The IRS policy requires direct human review of outputs used in government business and makes employees responsible for their prompts and for the use or distribution of outputs.
7. Intellectual property and attribution
Require employees to confirm they have rights to material submitted as input and to follow applicable license terms for anything they reuse. Set rules for attribution and disclosure when AI contributed to a deliverable. Employees should not assume that a generated answer is original, free of third-party rights, or appropriate to publish merely because a tool produced it.
FS-ISAC highlights limitations around attribution and copyright protection, while Microsoft places responsibility on customers to ensure they have rights to inputs and released content. Translate that principle into concrete steps for your work: check source material, honor license and client terms, and seek review when the rights status is unclear.
8. Security, retention, monitoring, and incidents
Set rules for approved integrations and extensions, prompt and output handling, logging, retention, and access to records. Tell employees what the organization monitors and why, in language consistent with applicable law and internal privacy requirements. Define how to report suspected data leakage, harmful output, a vulnerable integration, or another policy violation, including an urgent route for incidents that may require containment.
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9. Ownership, training, enforcement, and review
Name the policy owner and the roles of legal, privacy, security, records, and business reviewers. Set an exception process that specifies who may approve a deviation, what information the requester must provide, and how the approval is documented and expires or is reassessed. Define the potential consequences of violations in terms consistent with company procedures and applicable law; there is no universal penalty schedule established for every organization.
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Train employees with short scenarios drawn from their work: a public prompt, a request involving internal material, a client document, a generated calculation, and a high-impact recommendation. Require acknowledgment and explain how employees can ask questions. Choose a review interval and event triggers—such as a material change in tools, law, business use, or incident—based on the organization’s risk and obligations; no universal review period is established.
Policy upkeep is a practical governance issue. In its 2025 report, GAO found that officials at 10 of 12 selected agencies said existing federal policies, including privacy policies, could present obstacles to generative-AI adoption. That does not mean organizations should weaken safeguards; it means policy owners should identify conflicts, clarify requirements, and update procedures as legitimate uses and rules change.
How can you turn the rules into a policy employees can follow?
Write the employee standard in direct language, then connect each rule to a process employees can actually use. A policy that says “use AI responsibly” without an approved-tool list, data rules, or reporting route leaves the most important decisions unresolved.
- Choose the covered services and people. List the employee and contractor groups, external services, internal systems, models, plugins, and work outputs in scope. State how work-related use of personal accounts is handled.
- Map data classes to approved tools. Use the organization’s existing classification scheme where possible. For each tool, identify permitted data categories and restrictions; default sensitive or third-party-restricted data to “do not enter” unless a named approval covers it.
- Sort use cases by consequence. Identify routine uses, uses requiring elevated review, and prohibited uses. Consider what could happen if an output is wrong, biased, disclosed, or treated as authoritative.
- Assign owners and decision rights. Name who approves tools, who approves exceptions, who handles high-impact use cases, and who receives security, privacy, legal, or safety reports.
- Specify review and disclosure duties. Tell employees what checks are required before use or publication, who performs them, and when AI assistance must be disclosed.
- Make compliance workable. Provide a current tool list, a short request route, scenario-based training, an acknowledgment process, and an incident-reporting channel.
- Review and update. Set a cadence and triggers, confirm that the policy still fits applicable law and sector rules, and revise the employee guidance when tools or approved uses change.
Test the draft with employees who will use it. Ask them to classify realistic examples: Is a prompt containing a customer record allowed? Can a generated translation go directly to a customer? Who checks an AI-produced calculation? If two employees interpret the same rule differently, make the policy or its linked procedure more precise.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should employees do before using an AI tool?
A short decision sequence helps employees apply the policy in the moment. It should not replace the organization’s formal classification rules or approval process.
- Check the tool. Confirm the service, account, and integration are on the current approved list for the work you plan to do.
- Check the input. Classify the information and confirm the approved tool permits that data class. If it is confidential, personal, regulated, proprietary, security-sensitive, export-controlled, or third-party restricted, do not enter it unless explicit authorization covers that system and use.
- Check the consequence. If the output could affect employment, credit, insurance, health, safety, legal rights, or access to essential services, follow the elevated-review process before relying on it.
- Check rights and terms. Confirm you may submit the source material and that reuse of the output complies with applicable licenses, client terms, and disclosure rules.
- Review before relying on the result. Verify facts, calculations, citations, security, bias, and fit for purpose. Escalate uncertain or consequential outputs to the appropriate reviewer.
- Report problems promptly. Use the designated incident route if you suspect protected information was exposed, an output is harmful, or a tool or integration has been misused.
What commonly makes an AI policy fail?
Policy failures often arise from gaps between a written rule and the employee’s actual decision. Avoid these design problems:
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- Task labels treated as permission. “Summarize” or “translate” does not establish that the input is safe or the outcome low-risk. Data sensitivity and consequence both matter.
- Vague data prohibitions. Tell employees what information categories are restricted and where they can find the organization’s definitions. Avoid relying only on a phrase such as “sensitive data.”
- Human review without a standard. Employees need to know what to verify and when another reviewer is required. Otherwise, “check the output” can become a superficial glance.
- No route for legitimate exceptions. A clear approval process lets the organization assess a useful new case without forcing employees to choose between improvising and abandoning work.
- Rules that are never revisited. A tool list, data rule, or approval path can become out of step with actual use. Assign ownership and set a review cadence and change triggers.
- No incident channel. Employees who do not know where to report a suspected exposure may delay action. Make the reporting route visible in the policy and training.
What should a concise employee policy say?
The following sample is a starting point for internal drafting, not a universal legal template. Adapt it to your tool list, data classification, contracts, sector rules, and applicable law.
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Use only AI tools approved for your role and the task. Do not enter confidential, personal, regulated, proprietary, security-sensitive, export-controlled, or third-party restricted information into an AI tool unless the tool and use are explicitly approved for that information. You remain responsible for checking AI-generated work before you use, share, or publish it. Obtain additional review for consequential decisions, follow intellectual-property and disclosure requirements, and report suspected data exposure, harmful output, or misuse through the designated incident channel.
Place that employee-facing statement alongside the practical materials that make it actionable: the approved-tool register, data-classification rules, use-case approval route, reviewer responsibilities, disclosure requirements, incident contacts, and training. Keep the details synchronized so the policy does not direct employees to an outdated process.
Frequently Asked Questions
Can employees use AI on a personal account for company work?
The policy should answer this explicitly rather than leaving account choice implicit. If personal accounts are not approved for company work, say so and direct employees to the approved-tool request process; if they are permitted, specify the account and data conditions.
What should a manager do when an employee reports a prompt containing sensitive data?
Treat it as a potential incident and use the organization’s security or privacy reporting procedure promptly. Preserve the relevant details through approved channels and let the designated response owners assess containment and any notification obligations rather than asking the employee to investigate independently.
Does an AI policy need to cover tools built into office or coding software?
Yes, if employees can submit work information to an AI feature, the policy should define whether that feature is approved and what data it may handle. The organization should distinguish approved deployments and integrations from similarly named services or personal accounts.
How often should an enterprise AI policy be reviewed?
There is no universal review interval established for every organization. Set a documented cadence that fits your risk and legal obligations, and bring reviews forward when tools, laws, business uses, or incidents materially change.
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