A practical AI use policy tells employees which tools and tasks are approved, what information they may enter, when a person must check an output, who approves exceptions, and how to report an incident. Build it around the AI your business actually uses, assign a named owner, and review it whenever tools, data, contracts, or business processes change. The policy should make safe behavior easier, not become a generic statement that AI is either always safe or always forbidden.
What an AI use policy should accomplish
Your policy is an internal operating rule, not a promise that every AI use is lawful or risk-free. It should let an employee answer five questions before using a tool:
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- Is this tool approved for the task?
- May I enter this particular information?
- What level of human checking is required?
- Who can approve a new or higher-risk use?
- What do I do if the output or the data handling looks wrong?
It should cover both standalone chatbots and AI features built into software the company already licenses. A feature hidden inside a document editor, customer-service system, recruiting platform, or analytics product still processes business information and needs an owner, a purpose, and appropriate controls.
A policy alone does not establish compliance with privacy, employment, consumer-protection, intellectual-property, records-management, or sector rules. Requirements depend on your location, industry, contracts, the people affected, and the specific data and decisions involved. Treat the policy as one part of governance, alongside security controls, contractual reviews, and professional advice where the stakes justify it.
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Choose an owner and define the scope
Assign accountable roles
Name one role as policy owner. That person maintains the document, the approved-tool register, training records, and review schedule. Give a different role, committee, or executive the authority to approve exceptions when independence is useful. In a small business, the owner might be an operations manager with an external privacy or security adviser available for difficult cases.
List the people and systems covered: employees, contractors, temporary staff, interns, consultants, and automated workflows that handle company data. State whether the policy applies when staff use a personal account or device for company work. If personal use is prohibited, say so plainly; if it is allowed for low-risk work, define the boundary.
State the business purpose
Explain why the company permits AI. Examples include drafting internal documents, summarizing material that is already public, generating code subject to review, translating routine text, or exploring ideas without confidential data. A purpose statement helps reviewers reject uses that are unrelated to the business or cannot be supervised.
Define key terms
Use ordinary language for terms such as “AI tool,” “personal data,” “confidential information,” “high-impact decision,” “human review,” and “incident.” Include generative systems, predictive models, recommendation engines, transcription, image and audio generators, and AI features supplied as part of another product when they can create, rank, classify, or transform content.
Inventory the tools, data, and use cases you already have
Do not begin by trying to regulate every hypothetical system. Start with a short inventory and expand it as people disclose new uses. For each entry, record:
- tool and supplier name, version or plan, and account owner;
- business purpose and the team using it;
- input data types, including whether they identify a person;
- where data is stored, how long the supplier retains it, and whether it is used to improve a model;
- users, customers, workers, or other people who could be affected;
- the consequence if the output is wrong, biased, unavailable, or disclosed;
- the person responsible for reviewing the output;
- contract, sector, or jurisdiction requirements that apply; and
- approval date, decision maker, restrictions, and next review date.
Include informal use discovered through interviews, expense claims, browser extensions, scripts, and AI functions switched on by default in existing software. An inventory that lists only centrally purchased applications will miss the most common route for unapproved data exposure.
Use a simple, documented risk tier
A tiering model gives employees a quick route to the right control. It is a drafting method, not a legal classification. Adjust the thresholds to your business and record why a use was placed in a tier.
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| Tier | Typical use | Minimum controls | Approval |
|---|---|---|---|
| Tier 1: low impact | Brainstorming or rewriting text that contains no restricted information; summarizing public material. | Use an approved account, follow the data rules, and perform a basic accuracy and quality check. | Pre-approved use cases may be used by trained staff. |
| Tier 2: internal or operational | Drafting internal procedures, analyzing non-public business information, coding, or preparing a staff-facing report. | Approved tool and supplier review; access controls; documented owner; meaningful human review before reliance. | Owner approval for the use case and any material change. |
| Tier 3: high impact | Processing sensitive personal data, making or recommending decisions about employment, eligibility, credit, health, safety, or access to services; or making a customer-facing commitment. | Formal risk assessment, legal or specialist review as needed, strict data minimization, qualified human decision maker, testing, logging, and an escalation route. | Named senior approver before a pilot or production use. Stop if risks cannot be adequately mitigated. |
When a use fits more than one tier, apply the higher tier. Compare sensitivity and identifiability of the input, the people affected, the consequence and reversibility of an error, supplier safeguards, and whether a qualified person can genuinely review the result rather than simply click “approve.”
Maintain an approved-tool and approved-use register
What approval should examine
Before adding a tool, document the business purpose and the exact workflow. Ask the supplier for applicable privacy, security, retention, deletion, access, breach-notification, and model-training terms. Check whether data leaves the country or region relevant to your obligations, whether administrators can control user access, and whether the supplier offers an account suitable for business information.
Record what the tool is approved to do, not just its name. A tool may be acceptable for public text but not for customer records, source code, regulated information, or automated decisions. Note prohibited features, maximum data sensitivity, required settings, and the person who can answer questions.
Separate a pilot from production use
A limited pilot should use synthetic, public, or de-identified data whenever possible. Define success and failure conditions, test representative cases, and specify who can stop the pilot. Production approval should follow only after the supplier terms, access controls, review workload, and incident process are in place.
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Control access and changes
Use individual accounts, least-privilege permissions, multi-factor authentication where available, and a process for removing access when someone leaves or changes roles. Review the register when a supplier changes its terms, a model or feature changes materially, a new data type is introduced, or an incident occurs.
Write clear rules for data entry
Employees need categories they can apply in seconds. Your definitions should match the company’s existing information-classification scheme if one exists.
| Category | Examples | Rule for AI tools |
|---|---|---|
| Allowed | Published web copy, public product specifications, or invented examples. | May be entered into an approved tool for an approved purpose. Check that the output does not accidentally include restricted material. |
| Approval or de-identification required | Internal plans, non-public financial figures, source code, customer-support records, employee information, or data that could identify a person. | Use only a tool approved for that data type and purpose, or remove identifying and confidential details first. Follow any retention and access restrictions. |
| Prohibited unless an explicit exception is approved | Passwords, authentication secrets, encryption keys, unreleased personal records, highly sensitive health or financial details, or information subject to a contract that forbids disclosure. | Do not enter it. If the business believes a legitimate need exists, stop and obtain written approval from the policy owner and the relevant privacy, security, or legal authority. |
“The tool is free” is not a data-handling justification. An employee must know whether prompts and uploaded files are retained, reviewed by people, used to train a model, or shared with subprocessors. If those terms are unclear, treat the tool as unapproved for non-public information.
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Require data minimization: submit only what the task needs, remove names and direct identifiers when they are unnecessary, and avoid uploading an entire database to answer a narrow question. Do not attempt to evade restrictions by splitting a confidential record across multiple prompts or accounts.
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AI output is a draft or recommendation unless the policy explicitly defines a low-risk automated action. A reviewer must have enough subject knowledge, time, and authority to reject the result. Reviewing only spelling is not meaningful review when the output affects a customer, worker, payment, safety decision, or legal position.
Checks for ordinary business content
- Verify factual statements, dates, calculations, names, quotations, and links against a reliable source.
- Check that the output answers the requested task and does not invent details or citations.
- Remove confidential information that the tool reproduced unnecessarily.
- Review tone, accessibility, translation quality, and unintended discriminatory or offensive language.
- Check for copied or closely imitated material before publishing or incorporating it into a product.
Additional checks for consequential work
For a high-impact use, retain the input context, output, reviewer identity, decision, and reason for accepting or rejecting the recommendation when your records obligations permit. Test for foreseeable errors and groups that might be treated differently. Give the responsible person a way to investigate, correct, and explain an outcome; a nominal sign-off that cannot change the result is not a safeguard.
When to reject or escalate
Reject an output that is unsupported, internally inconsistent, unsafe, discriminatory, copied, or outside the tool’s approved purpose. Escalate when the reviewer cannot verify a claim, the tool exposes restricted data, a person challenges an AI-assisted decision, or the output could create a material financial, legal, health, safety, or reputational harm.
Set a higher-risk approval and escalation path
Name a single contact route, such as a security mailbox or internal ticket category, and state the expected response time for urgent cases. Employees should not have to decide whether an event is “serious enough” before reporting it.
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Use a stop-and-escalate rule for suspected data leakage, a malicious or manipulated prompt, a model output that recommends unsafe action, a supplier notice of unauthorized access, or a complaint about an AI-assisted decision. Preserve relevant logs and prompt/output records according to your incident and retention procedures; do not forward sensitive material to another unapproved tool while investigating.
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State employee responsibilities and prohibited behavior
Write responsibilities as actions rather than abstract principles. Employees should:
- use only their assigned account and approved tools;
- follow the data categories and remove unnecessary identifiers;
- label or disclose AI assistance when a contract, law, customer instruction, or internal rule requires it;
- perform the review required for the risk tier and keep required records;
- protect prompts, uploaded files, generated content, and access credentials;
- report suspected exposure, harmful output, policy violations, or supplier changes promptly; and
- complete training before using Tier 2 or Tier 3 workflows.
Prohibited behavior should include uploading restricted information to an unapproved service, using another person’s account, disabling logging or security settings, presenting unverified generated content as fact, allowing an AI system to make a high-impact decision without the required human authority, and using generated code, images, text, or data when the business cannot meet its license or attribution obligations.
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Training should use examples from the employee’s actual tools and tasks. Demonstrate a safe prompt, an unsafe prompt containing personal or confidential information, an invented citation, and the steps for reporting an incident. Explain that a tool’s polished answer is not evidence of accuracy.
Give contractors and new hires the policy before access is granted. Record completion, provide a short refresher when a tool or rule changes, and make the current approved-tool register easy to find. Managers should discuss the policy in team workflows rather than relying on a one-time acknowledgement.
Enforcement should be predictable and proportionate. Explain which violations trigger coaching, access suspension, investigation, or disciplinary action, while preserving a safe channel for good-faith reporting. A rule that is never communicated or enforced will not protect the business.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Monitor use and review the policy
Choose a small set of records that show whether controls operate: approved tools and owners, completed training, high-risk approvals, review logs where required, incidents and corrective actions, and dates of supplier or policy reviews. Avoid collecting more prompt content than you need, especially when logs themselves may contain personal or confidential information.
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Review the policy at a defined interval and after a material change. Triggers include a new model or feature, a supplier-term change, a new jurisdiction or customer contract, an incident, a new category of personal data, or a workflow that begins affecting people in a different way. Record the decision, the evidence considered, and any new restrictions.
The Federal Trade Commission’s small-business cybersecurity guidance emphasizes communicating and enforcing policy, maintaining an inventory of assets and data, assessing suppliers, controlling access, training staff, and keeping an incident-response plan. Those controls support AI governance even though the guidance is not an AI-policy law. The FTC’s September 2025 Compliance Plan for OMB Memoranda M-25-21 is an agency-specific example: it discusses unauthorized exposure of nonpublic data, output accuracy and hallucinations, potential plagiarism, professional obligations, coordinated tool evaluation, and workforce training. It governs the FTC and is not a mandate for private companies.
Use a security framework as supporting structure
NIST Cybersecurity Framework 2.0 is a free, voluntary, flexible way to organize related security work. Its functions are Govern, Identify, Protect, Detect, Respond, and Recover. A small business with modest or no cybersecurity planning can use NIST’s small-business quick-start material to structure inventory, access, monitoring, and response activities. The framework is not an AI policy template and does not replace privacy or sector requirements.
Map your AI controls to those functions: governance for ownership and approval; identify for tool and data inventory; protect for access and minimization; detect for monitoring and incident signals; respond for escalation and containment; and recover for correction, notification, and lessons learned. Keep the AI policy readable even if the underlying security documentation is technical.
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The Information Commissioner’s Office guidance addresses UK data-protection obligations and says context-specific controls cannot be prescribed exhaustively. It also states, “This guidance does not provide generic ethical or design principles for the use of AI.” That limitation matters: a UK business using personal data must assess its own processing, risks to people’s rights and freedoms, and proportionate technical and organizational measures. Low assessed risk does not erase legal duties, and a project may need to stop if risks cannot be adequately mitigated.
Businesses elsewhere should identify their own privacy regulator, employment and consumer rules, contractual restrictions, records obligations, and sector requirements. A policy written for a U.S. federal agency or for UK data protection cannot simply be adopted unchanged by a private company in another country. Obtain jurisdiction- and sector-specific review before treating a sample policy as ready for production.
A practical policy structure you can adapt
Keep the employee-facing document short, then link it to registers and procedures. This structure covers the decisions employees need:
- Purpose and scope: why AI is permitted, who and what it covers, and the effective date.
- Definitions: AI tool, restricted information, personal data, human review, high-impact use, and incident.
- Ownership: policy owner, approver, security or privacy contact, and escalation channel.
- Approved tools and uses: register location, permitted tasks, data limits, account requirements, and prohibited features.
- Data handling: allowed, approval-required, de-identified, and prohibited categories; retention and deletion rules.
- Review and accountability: reviewer qualifications, checks, records, correction, and rejection requirements.
- Higher-risk work: advance approval, testing, monitoring, decision authority, and stop conditions.
- Employee conduct: security, disclosure, licensing, access, and prohibited behavior.
- Training and enforcement: required training, acknowledgements, reporting protection, and consequences.
- Incident response and review: immediate actions, evidence preservation, notifications, corrective action, and review triggers.
Write one or two concrete examples under each rule. For example: “A support agent may summarize a de-identified ticket in the approved internal assistant; the agent must verify the summary before sending it.” Examples remove ambiguity better than a page of slogans.
Implement the policy in a 30-day sequence
- Days 1–5: discover. Interview teams, review expenses and software settings, list tools and use cases, and identify the most sensitive data and highest-consequence decisions.
- Days 6–10: classify. Apply your risk tiers, select initial approved tools, mark prohibited uses, and identify missing supplier terms or access controls.
- Days 11–15: draft. Write the employee rules, assign owners, define the approval form and incident route, and have the relevant privacy, security, HR, procurement, or legal reviewers examine it.
- Days 16–20: test. Run representative low-risk and higher-risk scenarios, including a bad citation, an accidental personal-data entry, an unsafe recommendation, and a supplier change. Confirm that employees know what to do.
- Days 21–25: train and restrict. Train affected staff, remove unnecessary access, configure approved accounts, and publish the register and reporting channel.
- Days 26–30: launch and measure. Obtain acknowledgements, begin recording approvals and incidents, check that reviews occur, and schedule the next policy review.
After launch, improve the rules from real questions and near misses. If employees repeatedly ask whether a common task is allowed, add that task and its data boundary to the register instead of leaving the answer to individual guesswork.
Quick Recap
Common policy failures and their fixes
- Blanket permission: “Use AI responsibly” gives no operational answer. Replace it with approved tools, data categories, review steps, and an owner.
- Blanket prohibition: Staff may use unapproved tools secretly to meet deadlines. Permit defined low-risk work while controlling sensitive and consequential uses.
- Tool-only approval: A tool approved for public text is assumed safe for customer records. Approve a tool and a use case together.
- Human review in name only: A reviewer lacks time or expertise. Set qualifications, checks, authority to reject, and records appropriate to the consequence.
- No supplier review: The business never checks retention, training, access, deletion, or breach terms. Make those questions part of procurement and renewal.
- No incident route: Employees hesitate while data or a harmful output remains in circulation. Provide one contact, immediate containment steps, and a non-punitive good-faith reporting path.
- Stale document: A model, feature, contract, or law changes while the policy remains unchanged. Set review triggers and an accountable owner.
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