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Write an AI use policy by naming an owner, listing approved tools and tasks, setting data-handling and human-review rules, and explaining how staff report problems and how the policy gets updated. Treat it as a practical control for your specific business—not a universal legal template. Duties can depend on where you operate, your industry, contracts, the data involved, and the decisions AI supports.
The U.S. guidance cited below can help you organize that work. NIST’s AI Risk Management Framework (AI RMF) is voluntary and scalable; its Generative AI Profile addresses risks specific to generative AI. The FTC’s small-business cybersecurity guidance is useful for assessing vendors’ data practices, but it is not a ready-made AI policy.
Start with an owner, scope, and purpose
Before drafting rules, decide who is accountable for maintaining them. Name a policy owner who can answer staff questions, approve or coordinate requests for new tools, maintain the approved-tool list, and arrange reviews when the business changes how it uses AI.
State why the policy exists and who must follow it. Consider employees, temporary workers, and contractors who handle business work or information. Define what counts as AI for your organization in practical terms—for example, generative services that create or transform text, images, audio, code, or other content, as well as tools that analyze information or make recommendations.
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Keep the scope tied to actual work. A tool used to draft a public marketing post may raise different concerns from one used to analyze customer records or recommend a hiring decision. Make clear whether the policy applies to company-provided tools, personal accounts used for work, and AI features embedded in other software.
Inventory approved tools and uses
Do not approve a tool in the abstract and leave staff to guess how it may be used. Maintain a list of approved services alongside the tasks they may support, any restrictions, and the person or role responsible for approval. Examples might include brainstorming public-facing content, summarizing nonconfidential material, or assisting with code that a developer reviews before use.
Set a simple route for requesting a new service or a materially different use. Before approving it, consider the task, information the service will receive, consequences of errors, available safeguards, and whether the vendor’s terms fit the business’s obligations.
NIST’s AI RMF Playbook groups suggested risk-management work under Govern, Map, Measure, and Manage. These headings can help organize an internal review, but they are an adaptable aid—not a requirement to complete every suggested activity. NIST describes the AI RMF as voluntary, and says the Playbook is not a checklist or a set of steps that must be followed in full. NIST AI Risk Management Framework · NIST AI RMF Playbook · NIST AI RMF FAQs.
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Set clear data-handling boundaries
Tell staff what they may not enter into a tool unless the use has been explicitly reviewed and authorized. Depending on your business, this may include confidential business information, customer or employee personal data, credentials, regulated records, and information subject to contractual or confidentiality duties.
Do not assume that a service treats prompts, uploads, or generated outputs in a way that matches your expectations. Before permitting sensitive information, review the provider’s terms and controls, including:
- Whether submitted data is used to train or improve models, and whether that use can be disabled.
- Whether the provider shares data with other parties or subprocessors, and for what purposes.
- How long prompts, files, and outputs are retained, and whether the business can control retention.
- What deletion options are available and what deletion does—and does not—cover.
- Security and access controls, administrative features, and contractual commitments relevant to the intended use.
FTC small-business cybersecurity guidance recommends scrutinizing vendors’ data practices, including use, sharing, retention, and deletion. NIST’s Generative AI Profile also identifies privacy and information-security risks associated with third-party generative-AI integrations. Those sources support a careful review, not a blanket assumption that any particular tool is safe or unsafe. FTC: Cybersecurity for Small Business · NIST AI 600-1, Generative AI Profile.
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State that AI output is an aid or draft, not a substitute for accountable judgment. Assign a person to review output before it is relied on or shared, with the depth of review matched to the likely consequences of an error. Depending on the task, review may include checking factual claims, calculations, citations, code, tone, and whether the output is suitable for its audience.
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Set additional approval requirements for uses that could affect employment, eligibility, safety, finances, legal rights, or regulated advice. Identify who must approve those uses and what evidence or checks are needed. A small business may decide that some uses are not permitted at all unless specialist review and safeguards are in place.
This is a practical policy recommendation informed by NIST’s risk-management and evaluation approach; NIST does not prescribe one universal human-review rule for every business. The policy should match your real uses and risks. NIST released AI RMF 1.0 on January 26, 2023, and its Generative AI Profile, NIST AI 600-1, on July 26, 2024. As of October 4, 2026, NIST’s AI RMF overview says version 1.0 is being revised. NIST AI RMF overview · NIST AI 600-1.
Name prohibited uses and accountability
Make prohibited conduct concrete and relevant to your organization. Examples to consider include:
- Entering restricted information into a tool that has not been approved for that data.
- Bypassing access controls or other security safeguards to use an AI service.
- Presenting unverified generated material as checked or authoritative.
- Using AI in a high-impact process without the review or approval the policy requires.
- Using a service for a purpose its terms or the business’s contracts do not permit.
Explain that the employee remains responsible for appropriate use of an output, and that the business remains responsible for its decisions and communications. Do not label a use universally illegal unless you have jurisdiction- and context-specific support; write rules that reflect the business’s applicable obligations and risk tolerance.
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Decide what to disclose, record, and check for rights
Specify when staff must disclose AI assistance internally or to customers. The right rule may depend on the task, audience, customer commitments, and whether disclosure is needed to make the work understandable or trustworthy. Make the requirement clear enough that an employee can tell what to do without inventing a disclosure rule for every situation.
Tell staff what records to keep for material uses, such as the tool and version, purpose, relevant input sources, reviewer, and final approval. Avoid asking employees to retain sensitive prompts or files unnecessarily; records should support accountability without creating another store of confidential data.
Require appropriate source checking and rights review before publishing or distributing generated material. NIST’s Generative AI Profile notes that third-party generative-AI use can raise intellectual-property concerns. Do not promise that a business owns generated content or that it is copyrightable; those outcomes should not be assumed from an internal policy. NIST AI 600-1, Generative AI Profile.
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Train staff and establish an incident-reporting route
Explain how workers will learn the rules: for example, during onboarding, when an approved tool changes, and when a new use is authorized. Training should cover approved tools, data boundaries, review expectations, disclosure and recordkeeping rules, and how to request approval.
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Give staff a clear route for promptly reporting suspected data exposure, harmful or misleading output, a security issue, or a policy violation. Name the person or role that receives reports and describe who will triage them. The response process can include limiting further use, preserving relevant information, notifying the appropriate internal contacts, and assessing whether customers, vendors, regulators, or other parties must be contacted under applicable obligations. NIST’s Generative AI Profile identifies education, data protection, retention, and incident response among relevant governance practices. NIST AI 600-1.
Review and update the policy when circumstances change
Assign the policy owner to review it when the business adds or changes tools, workflows, vendors, contracts, data types, or applicable requirements. The owner should also confirm that the approved-tool list and contact details remain accurate. NIST describes its Playbook as a living resource, but the cited guidance does not prescribe a universal interval for reviewing a company policy. Choose a cadence that fits the business and revisit sooner when a meaningful change or incident warrants it. NIST AI RMF Playbook.
Use the outline as a starting point, not a legal guarantee
NIST’s AI RMF is voluntary and intended to be scalable across organizations of different sizes and sectors. The FTC’s cybersecurity guidance helps with vendor and data-security questions, but it does not determine every AI-specific legal duty. The sources here are U.S. federal guidance and general risk-management material; they cannot establish what a particular business must do without knowing its jurisdiction, industry, customer locations, data, contracts, and AI uses.
Before approving sensitive data use or consequential decisions, consider appropriate legal, privacy, security, or sector-specific review. The practical goal is a policy staff can follow, with a named owner and rules matched to the business’s actual tools and responsibilities.
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