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Businesses can create an effective employee AI-use policy by listing the ways AI is used, classifying each use by risk, and matching it with clear rules for approved tools, data, human review, and escalation. The policy should allow useful, low-risk work while requiring stronger approval and safeguards when AI could expose sensitive information or affect a person’s rights, employment, health, finances, or safety. It should also make clear that employees and managers—not the AI system—remain accountable for decisions and work products.
A blanket ban can push use out of view; unrestricted use can expose company or customer information and put unreliable output into circulation. A practical policy gives employees rules they can follow, names the people responsible for applying them, and changes as tools and risks change.
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An AI-use policy explains which AI systems employees may use for work, what information they may enter, which uses need approval, and how people must check and disclose AI-assisted work. It is an operating rule for the business, not a promise that a tool is safe or a substitute for privacy, security, employment, records, procurement, or intellectual-property requirements.
Set the purpose in plain language: AI may support work, but it does not take responsibility away from the people who choose a tool, provide its inputs, act on its output, or publish the result. Define the scope to include employees, contractors, interns, vendors, and other people working on the company’s behalf where appropriate. CMS guidance uses a similarly broad scope for people working on its behalf, though government guidance should not be mistaken for a private company’s legal obligation.
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Explain how the policy relates to existing company rules. If a rule on confidential data, records retention, customer communications, or workplace conduct already applies, say so rather than creating a conflicting AI-only exception. Define key terms employees will encounter, such as generative AI, approved tool, sensitive data, AI-assisted content, and consequential decision.
How should a business build the policy?
Build it around real use cases and risk, rather than starting with a list of popular tools. The sequence below helps the company identify what is happening, set controls before use, and assign responsibility for keeping the policy operational.
1. Inventory proposed and existing uses
Ask teams to register each work use of AI, including informal workflows that have already started. For every use case, record:
- A named business owner and the purpose of the use.
- The tool or vendor, the people who will use it, and the business process it supports.
- The types of information entered, including whether it is personal, confidential, regulated, or customer-provided.
- Who may be affected by the output and where the output will go—for example, an internal draft, customer message, or employment recommendation.
- Expected benefit, known limitations, retention considerations, and an initial risk rating.
The U.S. Department of State’s AI-use inventory policy calls for reporting use cases with an owner and purpose. NIST’s AI Risk Management Framework (AI RMF) offers a useful organizing structure through its Govern, Map, Measure, and Manage functions. The framework is voluntary, not a certification or a substitute for an organization’s own legal review. NIST released AI RMF 1.0 on January 26, 2023, and its Generative AI Profile on July 26, 2024; its framework material notes that AI RMF 1.0 is being revised, so a policy should point to the current version rather than hard-code assumptions indefinitely.
2. Assign risk tiers and approval gates
Use a small number of understandable tiers and spell out what employees may do at each one. The following model adapts the risk-based approach reflected in NIST and federal guidance; it is a starting point, not a universal legal classification.
| Risk tier | Typical uses | Minimum controls |
|---|---|---|
| Low | Brainstorming, formatting, summarizing non-sensitive material, or drafting internal text | Use an approved tool; complete baseline training; review the output before relying on it. |
| Moderate | Customer communications, code, analysis, or workflow recommendations | Name an owner; review the data; test the output; obtain manager or specialist approval as required. |
| High or rights-impacting | Hiring, firing, promotion, pay, performance scoring, legal conclusions, health, safety, credit, or other decisions that materially affect a person | Complete a formal assessment; document the human decision-maker; conduct enhanced testing; obtain legal, privacy, or other specialist review; escalate concerns. |
For a high-impact decision, an unreviewed model output must not be its sole basis. A designated person must understand the relevant evidence, evaluate whether the output is appropriate, and make the decision under the company’s established process. The policy should also identify uses that are prohibited outright, such as using AI to discriminate, harass, retaliate, or infer sensitive traits without a lawful basis.
Do not rely on vague approval language such as “ask your manager” alone. Identify which roles approve moderate and high-risk uses, what information they need, and where the decision is recorded. U.S. Code language concerning federal agencies supports restricting access based on specific risk assessments and using guidelines rather than relying only on broad blocks. That is useful context for an allow-with-controls approach, but it does not itself create a private-sector rule.
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3. Approve tools and assess vendors
Maintain a current approved-tools list and an intake route for employees who need a tool that is not on it. Approval should consider the intended use and data, not just the product name: one tool or account configuration may be suitable for non-sensitive drafting and unsuitable for a regulated workflow.
For a vendor review, ask how prompts and outputs are used, whether they are used to train or improve services, how long they are retained, how deletion works, and what confidentiality and security controls apply. Also review subprocessors, processing locations, audit rights, breach notification, and intellectual-property terms. For higher-risk tools, involve procurement, security, privacy, and legal reviewers before use. Federal guidance specifically emphasizes negotiating vendor terms and protecting confidentiality, cybersecurity, privacy, and data.
4. Set data rules employees can apply in the moment
Give employees a simple default: if they would not put information into an unaffiliated external service or send it to an unknown contractor, they should not paste it into a public AI service. Then name the categories the policy restricts, such as:
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- Passwords, authentication tokens, private keys, and other credentials or secrets.
- Unreleased financial information and confidential business plans.
- Personal information, health information, customer records, and regulated data.
- Privileged legal advice, confidential source code, and information protected by a customer or partner agreement.
Prohibit entry of restricted information into public tools unless an authorized person has approved the use and suitable protections are in place. For approved work, require the minimum necessary information, anonymization where practical, and an approved secure channel. CMS guidance says sensitive information should be shared only for official purposes through secure channels with authorized people who need to know.
What can employees put into an AI tool at work?
Employees may use an approved tool for a task within its permitted purpose and only with information the tool is authorized to handle. Whether a tool is “approved” does not automatically mean every kind of data or use is approved.
For example, an employee might use an approved system to brainstorm headings from a general topic or format non-sensitive text. A draft that includes customer details, unreleased figures, confidential code, or personal information needs a different assessment. The employee should check the tool’s approved use and the data classification before entering anything; when the answer is unclear, they should ask the policy owner or the designated privacy or security contact rather than test the boundary with real data.
Make the policy distinguish between public AI services and approved enterprise or otherwise protected environments, if the business uses both. Describe the approved configuration and the data it is cleared to handle. Do not assume that a vendor’s general privacy statement, a paid account, or a “business” label settles questions about retention, training, access, or contractual protections.
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When must a person review AI output?
Every AI-assisted output must be checked by a responsible person before it is used or shared. The depth of review should match the consequence: a formatting suggestion needs less scrutiny than a customer recommendation, code change, public claim, or employment decision.
The person submitting, relying on, or publishing the work should check its factual accuracy and completeness, and look for bias, privacy or security problems, intellectual-property concerns, and conflicts with the assignment or applicable rules. Do not treat a confident tone, citations supplied by a model, or a plausible-looking explanation as proof that the output is correct. CMS advises against relying solely on AI-generated output for policies or decisions and recommends validation using trustworthy sources, expert consultation, and independent research.
Require disclosure when AI materially contributed to customer-facing, regulated, research, or public content, unless a documented exception applies. The policy should say who decides whether disclosure is needed and where it should appear. CMS guidance recommends communicating the use and purpose of generative AI as well as relevant limitations, safeguards, and uncertainties. A disclosure does not replace validation, permission to use the tool, or a person’s responsibility for the final result.
How should the policy protect workers and customers?
Employment and other rights-affecting uses need a specific assessment before deployment, not just the ordinary tool approval. Recruiting, screening, workplace monitoring, scheduling, performance evaluation, and discipline can affect access to work and the treatment of employees, so require review by appropriate legal, privacy, HR, and technical specialists.
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Address the effects the system could have on fairness, accessibility, worker data, compensation, job quality, health and safety, and protected activity. Keep accommodation, complaint, and appeal routes available. The policy should forbid discriminatory or retaliatory uses and require a meaningful human decision-maker for consequential employment outcomes. U.S. federal workforce principles cited in the relevant U.S. Code identify issues such as equity, job displacement, worker data, compensation, and health and safety; businesses can use those issues as prompts for assessment without presenting federal workforce principles as a blanket private-employer mandate.
How can a business prevent shadow AI and enforce the rules?
A policy reduces shadow AI only if employees can find an approved way to do useful work and understand what to do when it is unavailable. Pair training and a simple tool-request process with an accountable owner, an escalation route, and proportionate monitoring.
Train people and record acknowledgement
Training should cover approved systems, prohibited data, prompt hygiene, unreliable or fabricated output, bias, security, intellectual property, disclosure, human review, and incident reporting. Require employees to acknowledge the policy and refresh training periodically or when meaningful changes are made. The Responsible AI Institute’s template emphasizes employee competence and use of approved systems as ways to reduce shadow-AI risk.
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Keep evidence of approvals and compliance
Assign an owner with authority to maintain the policy and coordinate with security, privacy, legal, HR, procurement, and business teams. Retain the AI-use register, approval decisions, training completion, monitoring and audit results, incidents, corrective actions, and approved exceptions in line with the company’s records rules. State Department guidance calls for clear responsibility, monitoring, auditing, documentation, and regular testing.
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Monitor in a way that is proportionate to the risks and consistent with applicable law and company commitments. The policy should explain what monitoring is done, who can access records, and how concerns are reviewed. Enforcement can range from coaching for a first low-risk mistake to retraining, suspension of access, investigation, or existing disciplinary procedures for reckless or repeated violations. State plainly that good-faith reporting will not lead to retaliation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should employees do after an AI-related incident?
Tell employees to report suspected problems immediately through a named security, privacy, legal, or incident-response route. Examples include entering confidential data into an unapproved service, sending materially false AI-generated content to a customer, discriminatory output, prompt injection, account compromise, a copyright complaint, or an unsafe recommendation.
Give responders a clear sequence: contain further access or use, preserve relevant prompts and outputs where lawful and consistent with records rules, notify the appropriate owners, assess potential effects on people and data, and document corrective action. CMS guidance calls for prompt reporting of suspected or verified privacy breaches. IRS guidance assigns employees responsibility for both their prompts and their use, publication, or distribution of AI-generated output; that principle makes prompt and output handling part of incident response, not an afterthought.
Exceptions should be written down, time-limited, approved by named roles, and paired with compensating controls. High-impact exceptions should require senior risk, legal, privacy, or security sign-off. A verbal “one-time” exception is difficult to review and can quietly become routine use.
How often should an AI-use policy be reviewed?
Set a regular review at least quarterly or semiannually, and review sooner after a material incident, a new high-impact use, a major vendor change, or a legal or regulatory development. Record the version, effective date, owner, approver, change log, and how employees were informed.
Make the policy easy to update without losing accountability: keep the core principles and responsibilities in the policy, and maintain changeable details—such as approved tools, intake forms, and named contacts—in controlled supporting records. NIST’s AI RMF material notes that version 1.0 is being revised, one reason to reference the current framework rather than treating a particular edition as permanent.
What should the finished policy contain?
Use this outline as a coverage check. Adapt it to the organization’s size, data, sector, and jurisdictions rather than copying it without review.
- Purpose, principles, scope, and definitions.
- Approved tools, prohibited tools, and a request or intake process.
- Risk tiers, assessment requirements, and approval workflow.
- Data classification, prompt rules, and secure channels.
- Human review, disclosure, and citation or attribution requirements.
- Employment fairness, accessibility, and worker-rights safeguards.
- Vendor and procurement requirements.
- Training, acknowledgement, monitoring, records, and audits.
- Incident reporting and response.
- Exceptions, enforcement, policy owner, review cadence, and version history.
Before publishing, test the draft against real examples employees encounter: a summary of a public report, a customer reply, a code suggestion, a résumé-screening workflow, and an employee performance recommendation. If staff cannot tell what data is safe, who approves the use, or who must verify the result, revise the rule or add an operational route to answer those questions.
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Does a company need a separate AI policy if it already has a data security policy?
Usually, the AI policy should complement rather than replace existing rules. Security policies may cover data access and handling but not explain model-output review, disclosure, use-case approval, or who owns a particular AI workflow.
Is the NIST AI Risk Management Framework mandatory for private businesses?
NIST describes the AI RMF as intended for voluntary use. A business may use it to structure its governance, but should separately check the laws, contracts, and sector requirements that apply to its own operations.
Who should own an employee AI policy?
The business should name one accountable policy owner, while assigning specific review duties to functions such as security, privacy, legal, HR, and procurement. That prevents approval responsibility from becoming an informal task that no team is answerable for.
Should employees disclose AI use to customers?
Set disclosure rules by context and materiality, especially for customer-facing, regulated, research, or public content. The policy should identify who determines whether disclosure is required and how to handle cases where a contract or applicable rule sets a specific expectation.
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