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Build an AI use policy around risk, not a blanket ban. Permit low-risk assistance in approved tools, block sensitive data from public services, and require stronger approval, testing, documentation, and human decision-making as an AI use becomes more consequential. Every employee remains responsible for prompts and outputs, while a named owner maintains an inventory, training, incident process, and review cycle.
The practical method below turns those principles into a policy your staff can follow: define scope, record use cases, classify risk, approve tools and vendors, set data rules, require review and disclosure, protect workers, monitor compliance, respond to incidents, and update the document when technology or obligations change.
Why should a business use a risk-based AI policy?
A risk-based policy distinguishes harmless productivity help from uses that can affect a person’s rights, livelihood, safety, or access to services. Brainstorming an internal agenda is not equivalent to ranking job applicants or generating a legal conclusion. Treating both activities identically either creates unnecessary friction or leaves serious exposure unmanaged.
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The National Institute of Standards and Technology (NIST) describes its AI Risk Management Framework (AI RMF) as voluntary guidance for incorporating trustworthiness into the design, development, use, and evaluation of AI systems. AI RMF 1.0 was released on January 26, 2023, and its Generative AI Profile on July 26, 2024. The NIST AI Resource Center says more than 240 organizations contributed over an 18-month development process. Use the current framework as a reference rather than freezing your policy to one edition.
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- Allow with baseline safeguards: low-risk drafting, formatting, brainstorming, or summaries of non-sensitive material.
- Allow with documented controls: customer communications, code, analysis, or workflow recommendations that need testing and an accountable approver.
- Gate or prohibit: high-impact or rights-affecting decisions unless formal assessment, specialist review, and a documented human decision-maker are in place.
This approach also addresses shadow AI. Employees who cannot get an approved tool for a legitimate task may quietly use a personal account, creating data and audit gaps. A clear allow-with-controls route is usually safer than an unqualified prohibition.
How do you define the policy’s purpose and scope?
Open with a plain-language purpose statement: AI may support work, but it does not transfer accountability from employees or managers. State which activities are covered and how the policy fits with existing rules.
Write a scope that matches your workforce
Cover employees, contractors, interns, temporary staff, vendors, and anyone operating a system on the company’s behalf. Include internally hosted models, software features that embed generative AI, automated decision tools, and public services accessed from company devices or accounts. CMS guidance uses a similarly broad scope for people working on its behalf.
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Cross-reference privacy, information security, records retention, acceptable-use, employment, procurement, accessibility, and intellectual-property policies. Explain which rule controls if requirements conflict, and identify the policy owner and effective date. Define terms such as “generative AI,” “approved tool,” “confidential information,” “high-impact use,” “human review,” and “material AI contribution.”
How do you create an inventory of AI use cases?
Require a short record before a team adopts an AI system or materially changes its use. The State Department requires personnel to report use cases through an inventory with an owner and purpose; NIST organizes risk work through Govern, Map, Measure, and Manage functions.
Fields to require for every entry
- Named business owner and technical or vendor contact.
- Business purpose, expected benefit, users, and affected people.
- Tool, model, version, vendor, deployment location, and connected systems.
- Data categories entered, output destination, retention period, and deletion method.
- Risk tier, approval date, required reviewers, tests performed, and known limitations.
- Whether a human makes the final decision and how a person can challenge an outcome.
Inventory entries should cover pilots, not only production systems. Mark an entry inactive when a tool is retired, and retain its approval and incident history according to your records schedule.
What risk tiers and approval gates should the policy use?
Set controls in proportion to potential harm. The following model is practical for most businesses and can be mapped to your sector’s legal requirements.
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| Tier | Typical uses | Minimum controls |
|---|---|---|
| Low | Brainstorming, formatting, summarizing non-sensitive material, drafting internal text | Approved tool, baseline training, data check, and human review before use |
| Moderate | Customer communications, code generation, analysis, workflow recommendations | Documented owner, data review, output testing, manager or specialist approval, and retained records |
| High or rights-impacting | Hiring, firing, promotion, pay, performance scoring, legal conclusions, safety, health, credit, or other decisions that materially affect a person | Formal impact assessment, enhanced testing, legal and privacy review, named human decision-maker, escalation route, and documented rationale |
Keep humans responsible for consequential decisions
An unreviewed model output must never be the sole basis for a consequential decision. Require the responsible manager to examine relevant evidence, record the reasoning, and be able to reject the output. Federal guidance emphasizes specific risk assessments and guidelines rather than broad blanket blocks.
Define approval evidence
For moderate and high tiers, store the assessment, test results, reviewer names, version of the tool, date approved, and conditions of use. Reapproval is required after a material model, data source, workflow, or vendor change.
How do you approve tools and control vendors?
Maintain a published approved-tools list and a lightweight intake form so employees know what they may use. Procurement, security, privacy, and legal specialists should review higher-risk tools before deployment.
Questions for vendor due diligence
- Are customer prompts, files, or outputs used to train a shared model?
- What are the retention, deletion, backup, and legal-hold terms?
- Which security controls, certifications, access logs, and administrative controls are available?
- Where are data and backups processed, and which subprocessors can access them?
- How quickly will the vendor notify you of a breach or material service change?
- What confidentiality, intellectual-property, indemnity, audit, and exit rights appear in the contract?
Federal guidance calls for negotiating vendor terms that protect confidentiality, cybersecurity, privacy, and data-protection requirements. Do not assume a paid plan automatically provides the controls your data classification requires; verify the contract and configuration.
What data can employees put into an AI tool?
Use a simple employee test: if you would not paste the information into an unaffiliated external service or send it to an unknown contractor, do not paste it into a public AI tool. Public tools are prohibited for confidential, personal, regulated, or client information unless an authorized, secure arrangement explicitly permits it.
Prohibit or restrict these categories
- Passwords, API keys, encryption keys, security findings, and other credentials or secrets.
- Unreleased financial results, strategy, merger information, or other market-sensitive material.
- Personal, health, biometric, payment, customer, employee, or regulated records.
- Privileged legal advice, confidential investigations, and protected complaints.
- Confidential source code, proprietary designs, trade secrets, and restricted client deliverables.
Make safe handling practical
Require approved secure channels, the minimum necessary data, and anonymization or aggregation where possible. Explain that removing a name may not anonymize a small dataset if other fields can identify a person. CMS guidance says sensitive data should be shared only for official purposes, through secure channels, with authorized people who have a need to know.
Configure identity controls, logging, and data-loss-prevention rules where available. A technical block is useful, but it cannot replace classification training and a route for legitimate exceptions.
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When must a human review AI output?
The employee who submits, publishes, or acts on AI-assisted work remains accountable for its accuracy and consequences. Review must occur before an output reaches a customer, regulator, employee decision, production system, or public audience.
Use a documented review checklist
- Verify factual claims, calculations, citations, completeness, and dates against trustworthy sources.
- Check for hallucinated policies, invented quotations, unsafe instructions, and hidden assumptions.
- Test for discriminatory patterns, accessibility barriers, and inappropriate inferences about sensitive traits.
- Remove confidential or personal information that appeared in the output and confirm the destination is authorized.
- Assess copyright, licensing, attribution, trade-secret, and other intellectual-property concerns.
- Confirm the result meets the assignment, sector rules, contract terms, and records requirements.
CMS instructs users not to rely solely on AI-generated output for policies or decisions and to validate it with trustworthy sources, expert consultation, and independent research. IRS guidance likewise states that employees are responsible for their prompts and for using, publishing, or distributing the outputs.
Set disclosure rules
Require disclosure when AI materially contributed to customer-facing, regulated, research, or public content, unless a documented exception applies. The notice should identify that AI was used, its purpose, relevant limitations, safeguards, and meaningful uncertainties. Keep a record of the human reviewer and the final source material.
How should an AI policy protect employment fairness and worker rights?
Ban uses that discriminate, harass, retaliate, infer sensitive traits without a lawful basis, or make employment decisions without appropriate human oversight. Recruiting, screening, monitoring, scheduling, evaluation, and discipline deserve a formal impact assessment and specialist review.
Required safeguards for workplace systems
- Test outcomes across relevant demographic and accessibility groups before and during use.
- Provide accommodation, explanation, appeal, and complaint routes that do not depend on the AI system.
- Tell workers what data is collected, why it is used, and how long it is retained where applicable.
- Prohibit retaliation against anyone who reports a suspected AI harm or policy breach in good faith.
- Assess effects on job displacement, job quality, equity, protected activity, compensation, health, safety, and worker data.
For a high-impact employment use, a manager must make and document the final decision using evidence they can explain. An automated score may inform review only within the approved conditions.
What training, monitoring, and enforcement does the policy need?
Training should be mandatory before access and refreshed periodically. Cover approved systems, prohibited data, prompt hygiene, hallucinations, bias, security, intellectual property, disclosure, human review, and incident reporting. The Responsible AI Institute template emphasizes employee competence and approved systems to reduce shadow-AI risk.
Assign ownership and keep evidence
Name a policy owner with authority to pause a use. Maintain the AI-use register, approval records, training acknowledgements, monitoring results, incidents, corrective actions, and exceptions. Give employees an escalation mailbox or ticket route and publish expected response times.
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Use proportionate enforcement
Coach a first-time low-risk mistake, require retraining when needed, and suspend access while investigating repeated or reckless violations. Apply existing disciplinary procedures consistently, with due process and no retaliation for good-faith reporting.
Measure whether controls work
Review approval completion, training completion, unapproved-tool detections, review defects, incident volume, time to containment, and overdue corrective actions. Investigate both false positives that block legitimate work and false negatives that allow risky use.
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How should employees report and respond to an AI incident?
Require immediate reporting of confidential-data entry, materially wrong customer content, discriminatory output, prompt injection, account compromise, copyright complaints, unsafe recommendations, or use of an unapproved system. The policy should make clear that reporting early is safer than trying to hide or silently correct the mistake.
- Contain: stop the workflow, revoke or rotate exposed credentials, and restrict access to the affected tool or file.
- Preserve: save relevant prompts, outputs, settings, timestamps, and recipient information where lawful and necessary.
- Escalate: notify the designated security, privacy, legal, HR, or safety owner using the published route.
- Assess: identify affected people, data, decisions, customers, and contractual or regulatory notification duties.
- Correct: retract or amend harmful content, notify impacted parties when required, and apply compensating controls.
- Learn: document root cause, corrective action, owner, deadline, and whether the risk tier or approved-tools list must change.
CMS requires prompt reporting of suspected or verified privacy breaches. IRS guidance assigns responsibility for prompts and outputs, so an employee cannot avoid accountability by saying that a model produced the text.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should a business handle exceptions?
Allow exceptions only when they are time-limited, documented, approved by named roles, and paired with compensating controls. Record the business need, data involved, tool and model, start and end dates, reviewers, monitoring plan, and conditions such as redaction or sandboxing.
High-impact exceptions should require senior risk, legal, privacy, or security sign-off. An exception must not silently become normal practice; convert it into a standard approved use only after the ordinary assessment and testing are complete.
How often should the AI policy be reviewed and updated?
Schedule a quarterly or semiannual review, and trigger an immediate review after a material incident, new high-impact use, major vendor change, or legal or regulatory development. NIST notes that AI RMF 1.0 is being revised, so link internal guidance to the current framework version instead of embedding assumptions that may age.
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Maintain version control
- Version number, effective date, owner, and approving authority.
- Change log describing new tools, risk controls, definitions, or legal requirements.
- Employee communication record, training update, and acknowledgement status.
- Archived prior versions and the date each was retired.
When a change affects daily behavior, announce it before enforcement begins and give staff a clear path to ask questions.
What should the finished policy contain?
Use this outline as a completeness check before approval:
- Purpose and principles
- Scope and definitions
- Approved tools and prohibited tools
- Risk tiers and approval workflow
- Data classification and prompt rules
- Human review, disclosure, and citation requirements
- Employment, fairness, accessibility, and worker-rights safeguards
- Vendor and procurement requirements
- Training and acknowledgement
- Monitoring, records, and audits
- Incident reporting and response
- Exceptions and enforcement
- Review cadence, owner, and version history
A policy is effective when an employee can answer three questions without guessing: which tool may I use, what data may I enter, and who must review the result before it matters?
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Frequently Asked Questions
Does an AI policy need to be a standalone document?
Not necessarily. A short AI standard can set the rules and point to existing privacy, security, records, employment, procurement, and intellectual-property policies, provided employees can find all requirements in one navigation path. Use a standalone document when cross-policy conflicts or a large number of AI workflows would otherwise make the rules difficult to follow.
How can a small business run approvals without creating a large committee?
Combine roles where staffing is limited, but keep conflicting decisions separate: the person requesting a high-risk use should not be the sole approver or final decision-maker. A named owner can maintain the inventory while an external privacy, legal, or security adviser reviews only moderate and high-risk cases.
What evidence demonstrates that an AI control is working?
Keep dated approval records, test results, reviewer sign-offs, training acknowledgements, access logs, and incident metrics. Compare those records over time and sample completed work for factual, privacy, bias, and intellectual-property defects rather than measuring success only by the number of blocked prompts.
When should a customer be told that AI helped produce a service or message?
Follow applicable law, contract terms, sector rules, and the expectations of the affected person. Where disclosure is required or material to trust, state the AI’s purpose, the human oversight provided, and meaningful limitations instead of implying that a person performed work the system actually generated.
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