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An AI use policy employees can follow is a short decision guide, not a technical treatise. It should tell people which tools and tasks are approved, what data they may enter, when a human must check or override an output, when AI use must be disclosed, how to keep records, and exactly where to report a problem. Assign an owner, use examples that match your workplace, and connect the policy to existing privacy, security, records, intellectual-property, accessibility, anti-discrimination, and professional-conduct rules.
The practical approach is to start with a controlled policy: permit low-risk assistance in approved systems, block restricted data and high-impact decisions without review, train each role, and maintain a living inventory of tools and use cases. The sections below provide a structure you can adapt and a launch process that makes the rules usable on a busy workday.
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A complete policy covers the entire path from choosing a system to retiring it. Employees need operational instructions, while managers and control functions need ownership, records, review gates, and an escalation route.
| Policy section | What employees should be able to answer | Control owner or evidence |
|---|---|---|
| Purpose and scope | Does this rule cover my role, contractor status, tool, data, and work product? | Policy owner, approvers, definitions, and links to existing rules |
| Approved tools and use cases | May I use this named system for this task and this data class? | Current inventory with owner, purpose, retention, and review status |
| Allowed and prohibited uses | What is permitted, restricted, or banned? | Examples that reflect real workflows |
| Human review | What must I check, who can override the output, and when must I escalate? | Review checklist and high-impact approval record |
| Transparency and provenance | When must I disclose AI assistance, and what must I preserve? | Disclosure levels and prompt/output retention rules |
| Training and enforcement | What training is required, and what happens after a violation? | Role-based training records and named exception approver |
| Incidents and change management | How do I report leakage, bias, a harmful answer, or a changed tool? | Reporting channel, response steps, review cadence, and decommissioning process |
NIST’s AI Risk Management Framework 1.0, released January 26, 2023, describes governance as policies and practices that are documented, transparent, and implemented effectively. Its Generative AI Profile, released July 26, 2024, adds risks and controls specific to generative systems. NIST’s framework is voluntary; map it to your actual legal, contractual, and sector duties.
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How do you define scope and ownership?
Open with a scope employees can apply without legal interpretation. State who is covered, which systems count as AI, which information and outputs are covered, and who can approve exceptions.
Define the covered people, systems, and work
- Include employees, temporary workers, contractors, interns, and anyone operating a system on the organization’s behalf.
- Define artificial intelligence broadly enough to include generative text, image, audio, and code tools; predictive models; automated decision systems; and vendor features embedded in ordinary software.
- Cover prompts, uploaded files, generated outputs, model-generated rankings or recommendations, and work products that incorporate AI assistance.
- State that existing privacy, security, records-management, intellectual-property, anti-discrimination, accessibility, and professional-conduct requirements continue to apply.
Name accountable roles
Identify a policy owner, business approvers, and contacts for privacy, security, legal, records, accessibility, and human-resources questions. A business owner should accept the use case and risk; a technical or security owner should assess the system and data path; and a policy owner should maintain the text, inventory, training, and review calendar. NIST recommends documenting roles, responsibilities, communication, inventory, and review rather than leaving them implicit.
NIST says that policies, processes, procedures, and practices for mapping, measuring, and managing AI risks should be in place, transparent, and implemented effectively.
Which AI tools and use cases are approved?
Publish a current register instead of a vague statement that employees may use “approved AI.” The register lets a worker match a system and task to a data boundary before entering a prompt.
| Register field | Required detail |
|---|---|
| System and provider | Product name, version or model where material, and system owner |
| Permitted purpose | Specific tasks, such as summarizing public material or explaining code |
| Data classes | Public, internal, confidential, personal, health, regulated, proprietary, or security-sensitive data allowed or barred |
| Retention and training settings | Prompt/output retention, account separation, model-training use, deletion controls, and audit access |
| Review status | Approval date, risk tier, next review, conditions, and decommissioning decision |
| Escalation contact | Named person or queue for uncertainty, incidents, and exceptions |
Require approval before production use, even when a pilot appears harmless. Reassess a system when its model, hosting location, retention behavior, connected data, or contractual terms change. Include third-party systems and AI features purchased through another department; unlisted “shadow AI” is still an organizational use.
What can employees use AI for?
Allow low-risk assistance when the tool is listed for that purpose and a person checks the result before relying on it. Label examples as organization-specific rather than universal permission.
- Brainstorming ideas or outlines from public or non-confidential material.
- Formatting, rewriting for clarity, or converting an approved document into a different structure.
- Summarizing non-confidential content that the employee is authorized to process.
- Translation, provided a qualified person checks meaning, tone, and sensitive terminology.
- Explaining code or suggesting tests without exposing secrets, credentials, personal data, or proprietary source code unless an approved secure process expressly allows it.
- Drafting internal options, with a human author responsible for accuracy, suitability, and the final decision.
Every example should carry the same conditions: use an approved system, minimize the data, verify the output, and follow disclosure and record rules. A permitted task can become restricted when the input is sensitive or the output will affect a person’s rights, safety, employment, money, or access to services.
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What uses or inputs must be prohibited?
Write prohibitions in concrete verbs. Employees should not have to infer that a “free” public chatbot is unauthorized for company information.
Protect restricted information
Unless an approved secure process specifically permits it, prohibit entering personal, health, confidential, proprietary, regulated, classified, export-controlled, security-sensitive, or otherwise restricted information into public or unauthorized AI systems. CMS responsible-use guidance and the Internal Revenue Service GenAI policy both make this an operational rule, not merely a recommendation.
Block unacceptable behavior
- Do not use AI to deceive, impersonate, defraud, harass, create unlawful content, or bypass security controls.
- Do not upload credentials, authentication tokens, encryption keys, unreleased vulnerabilities, or data that could enable an attack.
- Do not publish, send, file, or deploy generated material without the review required for that work.
- Do not use an AI output as the sole basis for an employment, legal, safety, financial, benefits, eligibility, or similarly significant decision.
- Do not evade a procurement, privacy, records, accessibility, or security review by calling a system a “personal” tool.
If a business need appears to conflict with a prohibition, stop and use the exception route. An exception should identify the data, purpose, controls, duration, approver, and exit condition; it should never be granted informally in a chat message.
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Can employees paste company data into ChatGPT?
Not into a public or otherwise unauthorized service. An employee may use company data only when the specific system and use case are approved, the data class is permitted, contractual and technical safeguards are in place, and the employee follows the required minimization and retention settings.
Make the boundary visible with a simple classification table:
| Information type | Default treatment | What to do instead |
|---|---|---|
| Public information | May be allowed in an approved tool for an approved task | Check the register and remove unnecessary context |
| Internal but non-sensitive material | Use only where the register permits it | Prefer a managed account with stated retention controls |
| Personal, health, financial, client, employee, or regulated data | Do not enter unless a documented secure process allows it | Use approved de-identification or an authorized system and consult privacy |
| Trade secrets, source code, credentials, security data, or restricted government information | Prohibited in public or unauthorized tools | Use an approved isolated environment or a non-AI workflow |
Data minimization still applies in an approved environment: remove names, identifiers, unnecessary columns, and full documents when a smaller excerpt will answer the question. Never assume that deleting a chat later erases provider logs, backups, training copies, or downstream exports; the system owner must document those behaviors.
When must a human review an AI decision?
A human must review any output before it is relied on, and the review must be meaningful when the result could materially affect a person, the organization, or the public. The reviewer needs authority, competence, enough context to challenge the system, and a real ability to override or escalate it.
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Use a review checklist
- Accuracy: verify claims, calculations, citations, dates, and assumptions against reliable sources.
- Suitability: confirm that the answer addresses the actual task, audience, jurisdiction, and current policy.
- Privacy and confidentiality: check that the output does not reveal protected information or infer sensitive traits.
- Fairness and discrimination: look for disparate treatment, proxy variables, stereotyping, and missing groups.
- Accessibility: test whether language, format, and interaction work for people with disabilities.
- Copyright and intellectual property: check permissions, attribution, license terms, and confidential material.
- Security: inspect code, links, files, and instructions for malicious content, secrets, or unsafe actions.
- Uncertainty: identify unsupported claims and tell the recipient what remains unknown.
For high-impact uses, record the reviewer, evidence checked, decision, overrides, and escalation. The Information Commissioner’s Office says senior management should review and sign off an AI system’s intended use so it matches the organization’s risk appetite. Its guidance is under review, so obtain current legal advice for applicable data-protection duties.
How should the policy protect fairness, accessibility, and rights?
Do not let an opaque score or generated recommendation become an unreviewed judgment about a person. Require a documented impact review before deployment in hiring, performance management, lending, insurance, benefits, education, healthcare, safety, law enforcement, access to services, or other contexts where an error can materially harm someone.
The review should identify affected groups, the decision’s legal and business purpose, data sources, known limitations, testing results, accommodations, appeal or correction routes, and the person who can stop the system. Provide a human contact who can explain the decision in understandable terms; “the model said so” is not an adequate reason. Consider language access and disability accommodations in both the interface and the human review process.
Do employees have to disclose AI-generated work?
Set disclosure rules by audience and risk instead of treating every autocomplete suggestion the same way. Tell employees when to disclose assistance to colleagues, customers, regulators, or other stakeholders, what purpose the system served, what limitations remain, and whether a person verified the result.
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| Situation | Recommended disclosure approach |
|---|---|
| Internal brainstorming or formatting with no sensitive content | Follow the team’s local convention; disclose when the work product could be mistaken for unaided analysis |
| External communication, customer deliverable, or public content | Disclose AI assistance when required by contract, law, audience expectations, or the policy; identify the human reviewer |
| High-impact recommendation or regulated submission | Record the system, purpose, material inputs and outputs, reviewer, limitations, and approval; provide any legally required notice |
| Creative or technical work with rights implications | Disclose enough to support provenance, licensing, and attribution decisions |
CMS responsible-use guidance states that users remain responsible for the impact and accuracy of content produced with generative tools. A disclosure should never imply that a machine, rather than the accountable employee or organization, made the final decision.
What records and incident reporting are required?
Define a proportionate record, with more detail for higher-risk uses. A useful record includes the tool and model, date, purpose, material prompts and outputs, data classes, reviewer, approvals, disclosure, limitations, and any incident or override.
Give employees a simple incident route
- Stop the activity and preserve the relevant prompt, output, account, time, and recipient information without spreading the material further.
- Report accidental disclosure, harmful or fabricated content, suspected discrimination, security issues, copyright concerns, or policy violations to the named privacy, security, legal, or policy contact.
- Describe what data or people may be affected, which system was involved, and whether anything was published, sent, executed, or used in a decision.
- Follow containment instructions, such as revoking access, recalling a message, isolating code, notifying an affected person, or pausing the use case.
- Record the decision, corrective action, owner, and date for reopening or closing the issue.
Do not promise that every prompt will be retained forever. Set retention periods by purpose, risk, contractual duty, and records law, then document deletion and access controls. IRS guidance calls for documentation and reporting; CMS guidance likewise emphasizes communicating safeguards, limitations, uncertainty, and incidents.
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How do you train employees and enforce the policy?
Provide role-based AI literacy rather than a single annual slide deck. Everyone needs the data boundary, approved-tool lookup, review checklist, disclosure rule, and reporting route. Developers need secure coding and model-integration training; managers need impact assessment and override duties; reviewers need bias, accessibility, source verification, and documentation skills; procurement and legal staff need vendor and contract controls.
Use short practice scenarios: a public summary, an employee spreadsheet, a customer complaint, a hiring recommendation, and a prompt containing credentials. Require a knowledge check and keep completion records. Explain consequences in advance, distinguishing an honest mistake that is reported quickly from deliberate circumvention or concealment. State who can approve an exception and that no manager may waive privacy, security, safety, or legal duties unilaterally.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Article 4 of the EU AI Act says providers and deployers must take measures to ensure, to their best extent, a sufficient level of AI literacy for staff and other people operating AI systems on their behalf.
How often should an AI policy and tool inventory be reviewed?
Set a routine review interval, assign the owner, and trigger an out-of-cycle review after a material change. At each review, reconcile the inventory with purchasing records, access logs, and business uses; check incidents and overrides; confirm training; and retire systems that no longer meet requirements.
- Review after a new model, feature, plug-in, connector, hosting location, or retention setting is introduced.
- Review after a material incident, near miss, complaint, discrimination finding, or security advisory.
- Review when laws, regulations, contracts, or sector guidance change.
- Review when the data processed, affected population, decision purpose, or risk appetite changes.
- Before decommissioning, export required records, revoke access, remove integrations, notify users, and confirm deletion obligations.
NIST governance guidance calls for monitoring, periodic review, inventory maintenance, and safe decommissioning. The EU AI Act’s applicability depends on the system, role, territory, and implementation timeline, so local legal review is necessary before treating a general policy as a complete compliance program.
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Most organizations need a controlled model: allow useful low-risk work in approved systems while imposing strong gates around sensitive data and consequential decisions. Choose deliberately by comparing the trade-offs below.
| Model | Strengths | Risks and workload | Best fit |
|---|---|---|---|
| Permissive | Fast experimentation, broad adoption, low initial administration | Higher confidentiality, accuracy, discrimination, and audit risk; difficult to know what tools are in use | Rarely appropriate beyond tightly bounded, public-data experimentation |
| Controlled | Useful assistance with a clear data boundary, review gates, inventory, and training | Requires owners, approvals, records, and continuing maintenance | Most workplaces with varied tasks and moderate to high information risk |
| Prohibition-heavy | Simple message and lower immediate exposure | Shadow use, lost productivity, weak literacy, and no approved path for legitimate needs | Temporary response during an incident or where law, contract, or safety rules require a ban |
How do you write and launch the policy step by step?
Use the following sequence to turn principles into a document employees can use at the moment of choice.
- Map current use. Interview teams, inspect procurement and access records, and collect examples of prompts, outputs, and embedded AI features. Do not assume that an absence of approvals means an absence of use.
- Classify data and decisions. Mark public, internal, confidential, personal, health, regulated, proprietary, and security-sensitive information. Identify decisions involving employment, rights, safety, money, or access to services.
- Set the risk tiers. Define low-risk assistance, restricted use requiring extra approval, and prohibited use. Tie each tier to a review, record, and escalation requirement.
- Build the approved-tool register. Record owners, purposes, permitted data, retention, model-training settings, review dates, and contacts. Include vendor features inside ordinary applications.
- Draft employee rules first. Use short commands, examples, a decision checklist, and an obvious “stop and ask” route. Put legal definitions and control procedures in supporting sections rather than hiding the rule.
- Design the human-review gate. Specify who reviews, what they check, when they may override, and what evidence they record. Require meaningful review for high-impact outcomes.
- Set disclosure and record rules. Define audiences, disclosure detail, prompt/output retention, access, deletion, and provenance requirements.
- Test scenarios. Ask employees from different roles to classify realistic prompts. Rewrite any rule that produces inconsistent answers.
- Train and publish. Deliver role-based training, record completion, publish the register and reporting channel, and make managers responsible for local adoption.
- Measure and revise. Track tool coverage, training, incidents, review overrides, exceptions, and overdue inventory reviews. Update after material changes and retire unsafe uses.
What questions should employees ask before using an AI tool?
- Is this exact tool approved for this exact task?
- Does my prompt contain personal, confidential, regulated, proprietary, or security-sensitive information?
- Could the output affect someone’s employment, rights, safety, money, or access to services?
- Have I checked facts, sources, bias, accessibility, privacy, intellectual property, and security?
- Must I disclose AI assistance, preserve a record, or obtain an additional approval?
- If I am unsure, have I stopped and contacted the policy owner or privacy or security team?
What are common policy failures and how can you fix them?
| Failure | Why it fails | Fix |
|---|---|---|
| “Use AI responsibly” with no examples | Employees cannot classify a prompt or know when to stop | Add allowed, restricted, and prohibited scenarios tied to data classes |
| One blanket ban | People work around it, and the organization gains no literacy or inventory | Permit bounded low-risk work and provide an approved route |
| Approved-tools list with no data conditions | A permitted system may still be unsafe for a particular file or decision | Record purpose, data class, retention, and conditions for every entry |
| Human review as a checkbox | A reviewer may simply accept an authoritative-looking error | Require evidence checks, challenge authority, override power, and high-impact records |
| No disclosure or provenance rule | Recipients cannot judge reliability, rights, or licensing implications | Set audience-specific disclosure and retention requirements |
| No owner or review date | The policy and inventory become stale as tools and laws change | Name an owner, approver, cadence, triggers, and decommissioning process |
What legal and jurisdiction limits should you state?
Make clear that a general policy is not a substitute for advice on applicable law, contracts, collective agreements, professional duties, or sector rules. NIST’s framework is voluntary. ICO guidance is under review. The EU AI Act applies according to its scope and implementation rules, which depend on the system and the organization’s role. Requirements also vary with purpose, data, sector, and location.
Have local counsel and relevant control functions review the final text, especially for employment decisions, health or financial information, regulated submissions, children’s data, cross-border transfers, accessibility, and public-sector work. Keep the employee guide stable and readable, while maintaining detailed procedures and the tool register as controlled documents.
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Can contractors use the same AI policy as employees?
They can follow the same core rules, but their contracts should expressly cover approved systems, confidentiality, ownership of outputs, incident reporting, records, and return or deletion of data. Give contractors access only to the tools and data their assigned work requires, and name the internal manager who remains accountable.
Should an AI policy ban employees from using personal accounts?
A policy should prohibit personal accounts for company work unless a documented exception approves them. Personal accounts usually bypass organizational retention, access, contractual, and incident-response controls, making it difficult to investigate or delete information.
How should managers handle an employee who reports an accidental prompt disclosure?
Treat prompt reporting as a containment and learning opportunity, not as a reason to hide the event. Preserve the facts, involve privacy and security contacts promptly, limit further access or sharing, assess notification duties, and update training or tool controls when the review identifies a systemic cause.
What should happen when an AI vendor changes its model without notice?
Pause uses whose risk depends on model behavior, confirm the provider’s change notice and settings, and have the system owner reassess accuracy, bias, security, retention, and contractual commitments. Record the decision to continue, restrict, retest, or remove the tool before normal use resumes.
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