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A practical AI use policy tells employees which tools and tasks are allowed, what information they may enter, when a person must check an output, who approves exceptions, and how to report a problem. Build it around the AI your business actually uses, assign a named owner, and review it whenever tools, data, contracts, or business processes change. A policy is an operating control—not a guarantee of legal compliance—so adapt it to your jurisdiction, sector, contracts, and the people affected by each use.
What an effective AI policy must accomplish
Employees usually need an answer at the moment they are about to paste data into a chatbot or rely on generated text. A useful policy therefore converts broad principles into decisions they can make quickly:
- Which standalone tools and AI features in existing software are approved?
- Which business tasks are allowed, restricted, or prohibited?
- What data may be entered, and what must be removed, anonymized, or kept out?
- What human review is required before anyone acts on an output?
- Who can approve a new tool, a new data type, or a higher-risk use?
- Where do employees report an exposure, harmful output, suspected copying, or another incident?
The policy should name its owner, explain its scope, state the approval route, and include enforcement and review arrangements. The Federal Trade Commission’s small-business cybersecurity guidance emphasizes communicating, updating, and enforcing policies, while its September 2025 internal AI plan illustrates coordinated tool evaluation and implementation decisions. That plan governs the FTC; it is an example for private organizations, not a private-sector mandate.
Start with an inventory of real AI use
Do not begin by trying to regulate every hypothetical system. List the tools and features people already use or are asking to use, then describe the task, the input data, the output, and the decision that follows. Include AI embedded in email, office, customer-service, recruiting, analytics, development, design, and other software as well as separate chatbots and generators.
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Record each use case
For every proposed or existing use, record:
- Business purpose: the result the employee is trying to achieve.
- Tool and supplier: product name, account owner, relevant plan or deployment, and the person responsible for the relationship.
- Inputs: whether the prompt includes public, internal, confidential, personal, regulated, or otherwise restricted information.
- People affected: customers, workers, applicants, patients, students, suppliers, or members of the public.
- Output and action: whether the result is a draft, an internal analysis, a customer communication, or an input to a decision.
- Failure consequence: what could happen if the output is inaccurate, biased, copied, leaked, or unavailable.
- Review: who can meaningfully check the result before it is used and what evidence they will check.
This inventory gives the policy a manageable scope and reveals where a general permission is unsafe. It also supplies the facts needed for vendor review, access controls, training, and incident response.
Assign ownership and approval authority
One person or function must maintain the policy and the approved-tool register. Depending on the organization, that may be an information-security lead, privacy lead, operations manager, legal counsel, or a cross-functional group. The owner should have authority to pause a use, request more safeguards, and require a review when circumstances change.
Define the decision makers
- Policy owner: publishes the current version, keeps the inventory, coordinates reviews, and communicates changes.
- Tool or use-case reviewer: assesses the business purpose, data, supplier terms, security controls, impact, and available human review.
- Business approver: accepts the residual risk and confirms that the use is necessary and proportionate.
- Incident contact: receives urgent reports and coordinates containment, investigation, notifications, and corrective action.
- Employees and contractors: follow the rules, complete training, protect credentials, review outputs, and report problems.
State who may approve an exception and how long it lasts. An exception should identify the specific tool, task, data, users, safeguards, expiry or review date, and the person who accepted the risk. Do not let an informal chat message become a permanent approval.
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A simple risk model helps employees know when ordinary safeguards are enough and when specialist review is required. Use the following questions for every use:
- How sensitive and identifiable is the input data?
- How could the use affect customers, workers, applicants, or other people?
- How serious and reversible would an error be?
- What do the supplier’s privacy, security, retention, and reuse terms allow?
- Can a qualified person actually check the output before action?
These questions apply the context-specific, risk-based approach described by the Information Commissioner’s Office (ICO) and the FTC’s concerns about nonpublic information, supplier safeguards, and output accuracy. They are a drafting method, not a universal regulator-defined scoring system.
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| Use profile | Typical policy treatment | Minimum control |
|---|---|---|
| Routine internal drafting with public or non-sensitive input | May be allowed in an approved tool | Follow approved-tool and output-review rules; do not present unverified claims as fact |
| Internal work containing confidential, personal, or restricted information | Requires a tool and data-use approval before entry | Confirm supplier terms, limit access, minimize or de-identify data where possible, and document the review |
| Customer-facing material or a commitment made on the business’s behalf | Requires named human review before release | Check facts, calculations, sources, tone, confidentiality, and copied material |
| Use that can materially affect a person or create significant business harm | Requires heightened approval and escalation | Assess impact, legal duties, human oversight, alternatives, and whether the use should proceed at all |
Do not treat a “low-risk” label as a legal exemption. The ICO notes that low risk does not remove data-protection duties, and a project may need to stop if risks cannot be adequately mitigated.
Maintain an approved-tool and use register
Approval should cover both the supplier and the intended use. A tool that is acceptable for public marketing ideas may not be acceptable for employee records or customer complaints. Keep a register employees can consult without guessing.
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Information to record
- Tool name, version or deployment, account owner, and approved user group.
- Permitted purposes and prohibited purposes.
- Allowed input categories and examples of data that must not be entered.
- Supplier privacy, security, retention, training, deletion, access, and breach-notification terms reviewed.
- Whether prompts or outputs may be used to improve the supplier’s service.
- Required human review and the person or role accountable for it.
- Approval date, next review trigger, and the status of any exception.
Approval workflow
- Submit the request: describe the business purpose, users, data, output, affected people, and consequence of error.
- Check necessity: confirm that AI is appropriate and that a less risky method would not meet the need.
- Assess the supplier: review privacy, security, retention, access, deletion, subcontractor, and incident terms, along with existing contracts.
- Assess the use: identify sensitivity, impact, reversibility, accuracy needs, and the reviewer’s competence.
- Set controls: restrict accounts, minimize data, require review, log use where appropriate, and define escalation.
- Record the decision: approve, approve with conditions, reject, or request more information.
- Revisit it: review after a material product, supplier-term, data, process, or legal change.
The FTC’s 2025 plan says the agency permits internal generative-AI tools that meet its privacy and security standards for nonpublic information. For a business, the transferable lesson is to make approval depend on documented safeguards; the FTC’s particular governance structure and standards do not automatically apply to private organizations.
Write clear data-handling rules
Employees should not have to interpret a long privacy notice while composing a prompt. Put the rule in three practical categories and define the business’s own examples.
| Category | Employee rule | Examples to define internally |
|---|---|---|
| Allowed in an approved tool | Use only for listed purposes and approved accounts; still remove unnecessary details | Public information and internal material that the business has classified as suitable for that tool |
| Approval or de-identification required | Obtain approval before entry, or remove identifiers and other details so individuals and confidential matters cannot reasonably be identified | Internal plans, customer-service records, employee information, or supplier information, according to the organization’s classifications |
| Must not be entered | Keep out of AI tools unless a documented exception expressly permits it | Nonpublic information, credentials, secrets, regulated records, or information restricted by contract or law |
The FTC identifies unauthorized exposure of nonpublic information as a risk. The ICO explains that using AI to process personal data engages UK data-protection requirements. Therefore, a tool’s convenience is not permission to paste in a complete file, transcript, database export, or customer message.
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Minimize before you prompt
- Use the smallest amount of information needed for the task.
- Remove names, account numbers, contact details, identifiers, confidential terms, and unnecessary free text.
- Replace real records with synthetic or generalized examples when the task does not require the original.
- Keep credentials, access tokens, encryption keys, and security details out of prompts and uploaded files.
- Check whether the tool retains inputs, permits supplier access, uses content for service improvement, or shares it with subcontractors.
- Delete temporary files and outputs according to the business’s retention rules.
When the data cannot be adequately minimized and the supplier terms are not acceptable, do not use that tool for the task.
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AI output is a draft or recommendation, not an authority. A named employee must check material output before the business relies on it, with stronger review for customer-facing content and decisions affecting people.
What the reviewer checks
- Facts: names, dates, claims, quotations, legal or technical statements, and current information.
- Calculations: figures, formulas, units, assumptions, and totals using an independent method where the result matters.
- Sources: whether cited sources exist, actually support the statement, and are appropriate for the purpose.
- Completeness: omissions, hidden assumptions, contradictory instructions, and missing exceptions.
- Confidentiality: whether the output reveals input data, another customer’s information, internal strategy, or a supplier secret.
- Copied material: wording or images that may be plagiarized, improperly licensed, or misleadingly attributed.
- Suitability: tone, accessibility, cultural context, and whether a person could be harmed or unfairly treated.
The FTC’s internal plan specifically calls out accuracy, hallucinations, potential plagiarism, and professional obligations. If the reviewer cannot verify a consequential claim, the employee must correct it, remove it, obtain authoritative evidence, or escalate rather than publish it.
Set stronger controls for consequential uses
Require heightened approval when a use involves sensitive personal data, a consequential decision, a customer-facing commitment, or an output that could materially harm a person or the business. Examples include recommendations about employment, eligibility, access to services, health, safety, credit, insurance, legal rights, or significant financial commitments.
Heightened-review questions
- What decision is being supported, and who is accountable for the final decision?
- What rights, opportunities, or services could be affected?
- Is the input accurate, relevant, lawfully obtained, and limited to what is necessary?
- Can the output be explained, challenged, corrected, and reversed?
- What evidence shows that the system performs adequately for this context?
- What alternative process is available if the system is unavailable or unreliable?
- Do employment, consumer-protection, sector, contractual, or data-protection rules impose additional duties?
Do not allow an AI score or recommendation to become an automatic decision merely because it appears objective. A qualified person must have enough information, authority, time, and independence to reject the output. If risks cannot be adequately mitigated, stop the project.
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Give employees an incident and escalation route
Make reporting easy and non-punitive for good-faith reports. The policy should say whom to contact, what information to include, and what employees should do immediately.
Reportable events
- Confidential, personal, regulated, or restricted information entered into an unapproved tool.
- A suspected account compromise, unauthorized access, or unexpected supplier disclosure.
- A materially false, unsafe, discriminatory, defamatory, or otherwise harmful output.
- Fabricated citations, invented sources, incorrect calculations, or copied material that reached another person.
- An AI-assisted decision or message issued without the required human review.
- A supplier change that alters retention, training use, security, access, or data location.
Immediate response
- Stop using the affected tool or output and preserve relevant prompts, files, messages, and timestamps without spreading the sensitive content further.
- Notify the designated incident contact through the approved channel; urgent privacy or security events should not wait for a routine help-desk queue.
- Contain access, revoke or rotate exposed credentials, withdraw an incorrect message, or pause the affected workflow as directed.
- Record who was affected, what data or output was involved, which safeguards failed, and what action was taken.
- Escalate to privacy, security, legal, or senior management when the event may trigger contractual, regulatory, or customer-notification duties.
- After containment, update the tool approval, training, workflow, or policy so the same failure is less likely to recur.
Do not promise employees that every incident can remain confidential or that reporting automatically prevents consequences. Promise prompt handling, fair investigation, and clear instructions.
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Training content
Training should cover the approved-tool register, data categories, minimization, prompt and file handling, output verification, copied material, customer communications, higher-risk uses, and incident reporting. Show examples from the employees’ actual work. Record attendance and provide refresher training when a tool, policy, process, or risk changes.
Access controls
- Use business-managed accounts rather than personal accounts for business work.
- Give access only to people who need the tool and task.
- Use strong authentication and remove access when roles change.
- Separate experimentation from production workflows and customer records.
- Keep administrator rights and supplier-management rights limited.
Monitoring and assurance
Monitor the controls you can legitimately and proportionately monitor: approved-tool usage, completion of training, exception expiry, access reviews, incident trends, and whether required human review is documented. The goal is to detect drift and improve controls, not to collect more personal data than necessary.
Review the policy when a new tool or feature is introduced, supplier terms change, a new data category or user group is added, an incident occurs, a business process changes, or applicable law and contracts change. At each review, retire unused approvals and confirm that the register still matches reality.
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Use a supporting security framework without treating it as an AI law
The NIST Cybersecurity Framework (CSF) 2.0 is a free, voluntary, flexible structure for related security work. Its functions are Govern, Identify, Protect, Detect, Respond, and Recover. A small business can use its quick-start guide for organizations with modest or no cybersecurity plans to organize supplier review, access controls, training, incident response, and recovery.
CSF 2.0 is not an AI-use policy template and is not a legal requirement. Map only the controls that help your actual uses; do not create paperwork that employees cannot follow. If you consult a draft guide on using AI for CSF analysis or reporting, verify its status rather than treating a draft as finalized guidance.
Adapt the policy to law, sector, and contracts
The ICO’s AI resources are aimed at public, private, and third-sector organizations, but its guidance is specifically about UK data protection. It states that context-specific controls cannot be exhaustively prescribed and that its guidance does not provide generic ethical or design principles for AI. Other jurisdictions, industries, contracts, and professional duties may impose additional requirements.
Before adoption, have the policy checked against:
- The locations of the business, workers, customers, and data subjects.
- Sector rules governing health, finance, education, employment, children, safety, or other sensitive activities.
- Customer, supplier, employment, confidentiality, intellectual-property, and data-processing contracts.
- Records of processing, retention schedules, security policies, accessibility duties, and existing incident plans.
- Any obligation to inform people that AI is used, provide a human route, explain a decision, or honor a data-subject request.
An AI policy supports compliance; it does not replace a privacy notice, security program, records schedule, risk assessment, contract review, or professional advice.
Copy-ready policy structure
Use this outline as a starting document, then replace each bracketed concept with your organization’s actual roles, tools, classifications, and contacts:
1. Purpose and principles
Why the business uses AI; accuracy, confidentiality, fairness, accountability, and human oversight.
2. Scope
Workers, contractors, systems, embedded AI features, standalone tools, and business data covered.
3. Ownership and approval
Policy owner, tool reviewer, business approver, exception authority, incident contact, and review triggers.
4. Approved tools and uses
Current register, permitted tasks, prohibited tasks, approved accounts, and conditions.
5. Data handling
Allowed, approval-or-de-identification-required, and prohibited information; minimization and retention rules.
6. Human review
Required reviewer, checks for facts, calculations, sources, confidentiality, copied material, tone, and impact.
7. Higher-risk uses
Additional assessment, approval, documentation, affected-person safeguards, and stop conditions.
8. Security and access
Authentication, least privilege, account ownership, file handling, logging, and supplier controls.
9. Training and communication
Required training, acknowledgement, refreshers, and where employees find the current register.
10. Incidents and exceptions
Reporting channel, immediate actions, escalation, investigation, exception conditions, and expiry.
11. Monitoring and review
Control checks, version history, review triggers, and policy withdrawal or amendment process.Implementation checklist
- Name the policy owner and incident contact.
- Inventory current AI tools, embedded features, users, data, outputs, and downstream decisions.
- Publish the three data-entry categories with examples employees recognize.
- Create the approved-tool and use register, including supplier terms and review dates.
- Define ordinary and heightened approval, including who can reject or pause a use.
- Write the human-review checklist and assign accountability for customer-facing and consequential outputs.
- Connect AI incidents to the existing security, privacy, and business-continuity response process.
- Train affected workers and record completion.
- Restrict access to approved accounts and remove personal or unmanaged use where appropriate.
- Set review triggers, inspect exceptions, and update the policy when tools, data, processes, or obligations change.
The result should be short enough to use, specific enough to enforce, and flexible enough to change. Start with the tasks your employees actually perform, then expand only when a new use has a clear owner, approved tool, suitable data handling, proportionate review, and a way to respond when something goes wrong.
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