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What an AI usage policy must do
A policy connects your business reason for using AI with controls that employees and contractors can follow. It should cover generative AI assistants, predictive models, automated decision tools, embedded vendor features and systems built internally or by third parties. It also needs an owner, version number, effective date and next review date so people know which rules apply.
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At minimum, the policy should answer these questions:
- Who is covered: employees, contractors, temporary staff, subsidiaries and vendors.
- Which tools and environments are approved, including production systems, development sandboxes and browser-based services.
- What uses are allowed, restricted or prohibited.
- What data may be entered, under which lawful basis and for how long it may be retained.
- When a human must review, approve, correct or explain an AI output.
- How accuracy, bias, security and privacy risks are tested and monitored.
- When AI assistance must be disclosed to customers, applicants, regulators or other audiences.
- How people report incidents, and which sanctions apply to deliberate or negligent misuse.
Do not treat the document as a legal safe harbor. NIST describes its AI Risk Management Framework (AI RMF) as voluntary, and legal duties still depend on your facts, jurisdictions and the role your organization plays in an AI system.
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1. Set the policy’s objectives and scope
Start with a short statement of why the business uses AI. Typical objectives include reducing routine work, improving service response, helping employees find information or supporting analysis. Pair each objective with boundaries: AI may assist a person, but it does not independently make a decision that affects someone’s legal rights, employment, health, safety, credit or access to essential services unless the use has gone through the required specialist review.
Define who and what is covered
State whether the policy applies to employees, contractors, interns, agencies, suppliers and customers who access your systems. Include every route by which AI can enter the business: approved applications, developer APIs, plug-ins, office-suite features, customer-facing chatbots, models hosted in your own environment and AI embedded in a vendor’s product. Cover subsidiaries and geographic offices if they share systems or data.
Assign ownership
Name a policy owner, a business sponsor for each use case and the functions that can approve risk. Security, privacy or legal specialists should be involved where personal, confidential, regulated or client-restricted information is processed. Procurement should review vendor terms before a tool is used in production. Record an accountable person even when the model or service is supplied by another company.
Control the document itself
Put the version, approval authority, effective date and review date at the top. Keep a change log. Review at least annually and sooner after a material model, vendor, law, incident or business change.
2. Build an AI and data-use inventory
You cannot set sensible rules until you know where AI is being used. Ask every team to record proposed and existing uses, including informal experiments that have not reached production.
Fields to capture for each use case
- Business process and intended outcome.
- Model, application, vendor and hosting location.
- Users, administrators and any external recipients of outputs.
- Data classes involved: public, internal, confidential, personal, regulated or client-restricted.
- People affected by the output, such as customers, employees, applicants or patients.
- Whether the output informs, recommends or determines an outcome.
- Geographies, jurisdictions and cross-border transfers.
- Retention period for prompts, uploaded files, outputs, logs and feedback.
- Existing controls, test evidence, known limitations and a fallback process.
Include “shadow AI”: browser tools, personal accounts, copied customer text, code assistants and spreadsheets that call an external model. An approved-tool list is useful only when employees know why an unapproved service is unsafe and where to request an approved alternative.
3. Classify uses by risk and impact
Use a simple three-tier model at first, then add detail as your inventory grows. Risk should reflect the potential harm to people and the business, not just the sophistication of the model.
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| Tier | Typical characteristics | Required treatment |
|---|---|---|
| Low impact | Internal drafting, summarizing public material or generating ideas where a person checks the result and no sensitive data is used. | Approved tool, basic training, human review and ordinary security controls. |
| Moderate impact | Work that uses internal information, supports customer communications or influences an operational decision without being the sole determinant. | Documented owner, privacy and security review, test evidence, defined reviewer and monitoring plan before production. |
| High impact | Employment, credit, health, safety, legal-rights, biometric or other consequential decisions, or systems likely to cause significant harm if wrong or discriminatory. | Escalation to qualified privacy, employment, security and legal specialists; formal impact assessment, stronger validation, human decision authority, correction or appeal route, ongoing monitoring and a stop-use threshold. |
A use can move tiers when the data, model, audience or decision changes. For example, an internal résumé-formatting experiment may be low impact, while ranking applicants for rejection is a high-impact employment use.
4. Create an approval gate before production
Require sign-off before an AI use reaches customers, employees, applicants or other people who may be affected. The gate should be proportionate: a low-impact drafting tool needs less evidence than a system that recommends a hiring or lending decision, but every use should have an identifiable owner.
- Business review: state the problem, intended outcome, users, benefits and a non-AI fallback.
- Privacy and data review: identify personal or confidential information, lawful basis, purpose, minimization, retention and access controls.
- Security review: examine authentication, permissions, logging, integrations, prompt-injection exposure, data leakage and vendor incident terms.
- Legal and employment review: escalate regulated or consequential uses, contractual restrictions, intellectual-property questions and jurisdiction-specific duties.
- Procurement review: confirm the vendor, hosting, sub-processors, service terms, deletion commitments and permitted use of submitted data.
- Testing and acceptance: document representative test data, accuracy and robustness results, bias checks, known limitations and the threshold for stopping use.
- Recorded decision: store the owner, approvers, date, model version, limitations, reviewer duties, monitoring schedule and fallback procedure.
Do not approve a broad category such as “AI for hiring” without identifying the exact task. Screening, interview transcription, candidate communication and final selection have different impacts and controls.
5. Protect confidential and personal data
The safest default is to prohibit employees from entering personal, confidential, regulated or client-restricted information into an unapproved AI service. Make the rule visible in the tool list, onboarding material and data-handling training.
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Use a data decision rule
- Public data: may be used in an approved tool when the employee verifies the output and licensing requirements.
- Internal data: use only an approved environment with access controls appropriate to the information.
- Personal, regulated or client-restricted data: require a documented purpose, lawful basis, minimization, retention limit, access restriction and contract review before processing.
- Secrets and credentials: never paste passwords, private keys, access tokens or security details into a prompt.
Minimization means using only the fields and records needed for the task. Remove names, identifiers and free-text details when they are not necessary. Define how prompts, uploaded files, outputs, telemetry and audit logs are retained and deleted. Make sure permissions prevent one customer, employee or project from being exposed to another.
State the lawful basis and privacy notice
For UK personal-data processing, the Information Commissioner’s Office (ICO) says an organization must identify a lawful basis for AI processing and include the basis and purposes in its privacy notice. The ICO’s lawfulness guidance was updated on October 28, 2024. A policy should assign someone to document that analysis rather than assuming that a vendor’s terms settle it.
6. Keep humans accountable
AI can assist a decision-maker, but accountability stays with a person who has authority and enough understanding to challenge the output. The reviewer must check material facts, recognize model limits, correct errors and document the decision when the stakes justify a record.
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Specify review duties
- Check important claims against reliable records before sending or acting on them.
- Do not accept a confident answer as proof of accuracy.
- Look for missing context, fabricated sources, discriminatory patterns and unsafe recommendations.
- Override the system when evidence conflicts with the output.
- Give affected people a correction or appeal route when an AI-assisted outcome may harm them.
- Record the AI contribution when the decision, contract or law requires traceability.
For high-impact uses, the human must be able to stop or reverse the process. A nominal “human in the loop” who merely clicks approve is not meaningful oversight.
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Testing should match the real task, population and failure consequences. Before launch, define what acceptable performance means and what result triggers escalation or stop-use.
Accuracy and robustness
Use representative cases, edge cases and known difficult examples. Check whether results change unexpectedly when prompts, data formats or model versions change. Record the test set, date, model version, reviewer and limitations. Re-test after a material model, data or workflow change.
Fairness and discrimination
Examine outcomes across relevant groups and investigate unexplained differences. The ICO says unjust discrimination from AI processing breaches the fairness principle and calls for data-protection-by-design, testing and ongoing monitoring. Do not infer that a neutral-looking input guarantees a neutral outcome; proxies can reproduce protected characteristics.
Security and misuse
- Test prompt injection and attempts to make the system reveal hidden instructions or data.
- Check whether a user can retrieve another person’s records through prompts or connected tools.
- Limit plug-ins, external actions and file access to what the task requires.
- Log administrative events and material outputs while respecting data-retention rules.
- Define variance thresholds, escalation contacts and the condition that suspends the system.
Monitor in operation, not only at launch. Review complaints, overrides, error rates, security alerts and demographic patterns at the interval appropriate to the risk.
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Require employees to check the provenance, license and confidentiality status of material used to train, prompt or publish AI-generated work. Do not upload a client’s restricted material merely because a service accepts the file. Preserve attribution and license obligations when an output incorporates third-party content.
Specify when people must disclose AI assistance. Disclosure may be required by law, contract, customer expectation or an internal rule. Your policy can require a plain-language notice for customer-facing content, a record in regulated workflows or an internal label for substantial AI assistance. The requirement should depend on the use and audience rather than an arbitrary percentage of machine-generated text.
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9. Address hiring and other consequential uses
Hiring deserves a separate control because an error can affect a person’s livelihood and may be difficult to detect. On November 6, 2024, the ICO reported almost 300 recommendations to recruitment-tool providers. The recommendations included accurate data collection, minimization, transparent explanations, retention periods and checks to mitigate discrimination. The ICO’s Ian Hulme summarized the risk: “AI can bring real benefits to the hiring process, but it also introduces new risks that may cause harm to jobseekers if it is not used lawfully and fairly.”
Your policy should therefore require specialist review before AI ranks, filters, scores or rejects applicants. Define the human decision-maker, evidence they must examine, accommodations for affected people, a way to challenge an outcome and a deletion schedule for applicant data. Apply the same escalation principle to credit, health, safety, biometric identification and decisions affecting legal rights.
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10. Train, monitor and enforce
Role-based training
Everyone needs the baseline data rule, approved-tool list and reporting channel. Developers need instruction on model and data changes, testing, logging and secure integrations. Managers need to recognize high-impact use and make sure reviewers have time and authority to challenge outputs. Recruiters, support staff and analysts need examples tailored to their workflows.
Attestations and reporting
Use periodic attestations for staff with access to sensitive or consequential systems. Provide a simple channel for reporting accidental disclosure, suspicious output, discriminatory results, policy violations and suspected compromise. Make clear that reporting a mistake promptly is safer than concealing it.
Proportionate consequences
State that deliberate or negligent misuse can lead to access removal, retraining, disciplinary action, contract remedies or other proportionate consequences under existing employment and supplier rules. Apply the same process consistently and document investigations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.11. Map the policy to recognized frameworks and laws
NIST AI Risk Management Framework
NIST describes AI RMF 1.0 as a voluntary framework released January 26, 2023, to incorporate trustworthiness into the design, development, deployment, use and evaluation of AI systems. Its Playbook organizes actions under Govern, Map, Measure and Manage. NIST released the Generative AI Profile, NIST-AI-600-1, on July 26, 2024. The framework is designed to scale across organization sizes and sectors; NIST says more than 240 organizations from industry, academia, civil society and government contributed to its development.
Use the functions as a management map: govern ownership and rules; map context, data and impacts; measure performance and harms; manage decisions, controls and improvement. NIST remains voluntary and does not replace applicable law or specialist advice.
European Union exposure
If your organization operates in, supplies or affects people in the European Union, map each system to its AI Act role and risk category. The European Commission says general-purpose-AI provider obligations include technical documentation, a copyright policy and a public summary of training content, with those obligations applying from August 2, 2025. The Commission published scope guidelines on July 18, 2025. Whether a duty applies depends on your role, the system’s classification and geography; have qualified counsel assess the facts.
United Kingdom personal data
For UK personal-data processing, use the ICO’s guidance on auditing AI so processing is fair, lawful and transparent and risks to people’s rights and freedoms are assessed and managed. Combine privacy-by-design, bias testing, monitoring, a documented lawful basis and an accurate privacy notice. ICO guidance can change as legislation develops, so set a review trigger for legal updates.
12. Choose a policy maturity level
A small business can begin with a practical baseline and add controls as the inventory and risk grow. The two models below are design choices, not substitutes for legal advice.
| Control area | Lightweight baseline | Mature governance policy |
|---|---|---|
| Scope | Internal productivity tools and clearly approved vendors. | Internal, customer-facing, developer, supplier and high-impact systems across subsidiaries and regions. |
| Approval | Named business owner with security and privacy checks for sensitive data. | Recorded business, security, privacy/legal and procurement approvals with specialist escalation by risk tier. |
| Data rules | Approved-tool list; no personal, confidential, regulated or client-restricted data in unapproved services. | Data classification, lawful-basis record, minimization, retention schedule, access controls and contract review for each use. |
| Human review | Check all material outputs before use. | Defined reviewer authority, override, correction and appeal processes for consequential outcomes. |
| Testing | Representative accuracy checks and a documented fallback. | Accuracy, robustness, bias, prompt-injection and leakage testing with thresholds and versioned evidence. |
| Monitoring | Periodic owner review and incident reporting. | Scheduled metrics, complaint and override analysis, change-triggered reassessment and stop-use criteria. |
| Documentation | Inventory, owner, approved tool and policy version. | Impact assessments, test sets, model versions, decisions, logs, contracts and change history. |
| Training | Annual baseline training and acknowledgment. | Role-based training, periodic attestations and refresher training after material changes. |
| Incident response | Central reporting channel and access removal when needed. | Severity levels, containment, investigation, notification, remediation and lessons-learned review. |
| Jurisdictional overlays | Identify where employees and customers are located. | Map EU AI Act, UK data-protection and other applicable obligations to each system and role. |
13. Put the rules into a usable policy
Write in plain language, then attach procedures for approvals, data classification, testing and incidents. The policy itself can follow this outline:
AI Usage Policy — version 1.0
Owner: [policy owner]
Effective date: [date]
Next review: [date]
Purpose and scope
Approved tools and permitted uses
Prohibited and restricted uses
Data, privacy, confidentiality and retention
Risk tiers and approval gate
Human review and accountability
Testing, monitoring and change control
Intellectual property and disclosure
Training, reporting and sanctions
Incident response
Records and exceptions
Keep exceptions time-limited and documented. Each exception should name the approver, affected data, compensating controls, expiry date and condition for renewal. Avoid a permanent “temporary” workaround.
14. Prepare for incidents and stop-use decisions
Define an incident before the first failure occurs. Examples include sending personal data to an unapproved service, a model exposing another customer’s information, a materially wrong recommendation, discriminatory output, a prompt-injection compromise or use of an unlicensed work.
- Stop the affected workflow or revoke the tool’s access when continued use could increase harm.
- Preserve relevant prompts, outputs, logs, model version, recipients and timestamps while observing retention and privacy requirements.
- Notify the designated security, privacy, legal and business contacts.
- Assess affected people, data, jurisdictions, contracts and reporting duties.
- Correct or contain the result, communicate with affected parties when required and provide a review or appeal route.
- Document root cause, remediation, retraining, vendor action and the condition for safe restart.
Set stop-use criteria in advance—for example, an unresolved data leak, a material fairness failure, test performance below the approved threshold or a model change that has not passed reassessment.
15. Review the policy as the business changes
An annual review is the minimum cadence, not the only trigger. Reassess after a material model or vendor change, a new data source, a new country, a security or privacy incident, a change in business purpose, a complaint trend or a legal update. Retire uses that no longer have a clear owner, benefit or safe fallback.
Track a small set of useful records: inventory coverage, approval status, training completion, reported incidents, review overrides, test results, complaints, open corrective actions and policy exceptions. The point is not to collect metrics for their own sake; it is to show whether controls work and where a decision needs human attention.
Quick Recap
Implementation checklist
- Publish a named owner, version, effective date and review date.
- Inventory every AI use, including experiments and embedded vendor features.
- Classify uses as low, moderate or high impact and escalate consequential cases.
- Maintain an approved-tool list and prohibit sensitive data in unapproved services.
- Require business, security, privacy/legal and procurement approval before production use.
- Document lawful basis, purpose, minimization, retention and access for personal data.
- Assign a human decision-maker with authority to correct, override and stop the system.
- Test accuracy, robustness, bias, prompt injection and data leakage before launch and after material changes.
- Define disclosure, intellectual-property and license checks.
- Train by role, collect periodic attestations and provide a simple reporting channel.
- Set incident steps, sanctions, stop-use thresholds and a safe fallback.
- Review at least annually and whenever the model, vendor, law, incident or business purpose changes.
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