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Set up a small-business AI use policy by listing the tools and tasks staff actually use, then spelling out which uses are allowed, which need approval, and which are off-limits. Add clear rules for information entered into AI services, human checks on outputs, a named policy owner, staff training, incident reporting, and regular review. The policy is a practical risk-control layer—not proof of legal compliance.
Start with your actual AI use
Before writing rules, make a short inventory of the AI products people already use or want to use. Include browser-based tools, features built into other software, and use through personal accounts. For each tool and task, record who uses it, the purpose, the information entered, who receives the output, and what decision or action the output could affect. This inventory is a practical way to focus the policy; it is not a specific NIST requirement.
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Use the inventory to identify differences that a blanket “AI is allowed” or “AI is prohibited” rule would miss. Drafting a social post from public information is not the same risk as summarizing employee records or using generated recommendations to make a consequential decision.
Sort uses into three policy categories
Use categories employees can apply quickly. These examples are policy-design choices, not universal legal classifications; your obligations depend on where you operate, your industry, contracts, data, and actual use.
| Category | Example rule |
|---|---|
| Allowed | Routine brainstorming, outlining, or drafting using public or non-sensitive information, with a person checking the result before it is used. |
| Approval required | Any use involving personal, confidential, customer, employee, financial, or contract-restricted information; a new AI service; or an output that may affect people, finances, safety, legal rights, regulated work, or important business operations. |
| Prohibited | Any use the business has determined it will not permit, such as entering credentials into an unapproved service or using an unreviewed AI output to make a consequential decision without an accountable human decision-maker. |
Be specific about what “approval required” means: employees pause and ask the named policy owner before using the tool or entering the information. Do not treat an AI service as approved just because it is popular, paid, or marketed for business.
Approve tools and settings—not just brands
Keep a compact list of approved services. For each, state the permitted business tasks, who may use it, any required account or configuration, and who owns the approval. A service may be approved for public-information drafting but not for handling customer data or making decisions about employees.
Review the service’s current terms, privacy information, security options, and account settings for the specific use being considered. A paid plan, enterprise label, or vendor assurance does not automatically establish that the service is suitable. Terms and settings can change, so record when the approval was made and revisit it after a material change.
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Unless the specific service and use have been reviewed and approved, staff should not enter confidential company information, customer or employee personal data, financial details, credentials, regulated information, or material restricted by a contract. Give employees examples drawn from your business—for instance, customer correspondence, payroll records, unpublished pricing, or account access details—rather than relying only on labels such as “sensitive.”
Tell staff what to do when they are unsure: stop, do not paste the material, and ask the policy owner. The legal meaning of personal, regulated, or protected information varies by jurisdiction and industry, so adapt these rules to the information and obligations your business actually has.
Require people to verify AI output
AI output can be inaccurate, incomplete, or unsuitable for the context. Make a person accountable for checking it before it is used. Depending on the task, that means verifying facts, calculations, citations, code, customer-facing claims, and whether the answer leaves out an important qualification.
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Scale the review to the possible impact. A draft for internal brainstorming may need a quick check; an output affecting a customer, employee, payment, legal obligation, safety issue, or important operation needs more careful review by someone with authority and relevant knowledge. AI may assist a decision, but the policy should identify who remains responsible for the decision. NIST’s AI Risk Management Framework identifies validity and reliability, accountability, transparency, explainability, privacy, and safety as trustworthiness considerations; a particular human-review process is a practical way to apply those ideas, not a procedure prescribed by NIST.
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Name one person responsible for maintaining the policy and a clear approver for exceptions or new uses. In a small business, these may be the owner or manager. Keep approval records lightweight: note the service, purpose, information involved, expected benefit, risks considered, safeguards, decision, approver, and review date.
Give employees a known channel to report accidental information entry, misleading or harmful output, suspected bias, security concerns, or other AI-related incidents. The goal is prompt reporting so the business can assess what happened and respond—not to discourage staff from raising a concern.
Publish, train, and revisit the policy
Make the rules easy to find and explain them in plain language. Training should cover how to check the approved-tool list, which information must not be entered without approval, how to verify outputs, when a disclosure may be needed, and how to escalate a question or incident. State any customer or employee disclosure rule that applies to your business rather than assuming one universal rule.
Review the policy when someone proposes a new tool or materially different use, when a service’s terms or configuration change, after an incident, or when business obligations change. A fixed schedule—such as at least once a year—is a useful management choice, but NIST’s sources do not prescribe that cadence.
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The NIST AI Risk Management Framework (AI RMF) is voluntary guidance for managing AI risks and considering trustworthiness throughout AI design, development, use, and evaluation. Its FAQ says the framework is intended for voluntary use; it does not certify a small-business policy or establish that a business has met applicable laws or contracts. NIST’s Generative AI Profile, AI 600-1, is a companion resource focused on generative AI.
For surrounding business controls, NIST’s Cybersecurity Framework 2.0 Small Business Quick-Start Guide, SP 1300 is an introductory cybersecurity resource for small businesses. The small-business quick-start guide page also points to voluntary privacy guidance. NIST’s Risk Management Framework Small Enterprise Quick Start Guide, SP 1314 introduces broader information-security and privacy risk management for small, under-resourced entities. These resources address different scopes; select what helps with your actual risks and obligations, rather than treating any guide as a substitute for them.
NIST’s April 14, 2026 announcement of an initial public draft on cybersecurity for non-employer firms cites 34.8 million U.S. small businesses and says 81.9% have no paid employees besides their owner or owners, citing the U.S. Small Business Administration Office of Advocacy. Those figures describe the U.S. small-business population, not AI use or adoption of AI policies. The draft is U.S.-focused cybersecurity guidance, not a final AI-policy rule.
What to put in the written policy
- Purpose and the people, tools, and work covered.
- Approved tools and permitted tasks, plus approval-required and prohibited uses.
- Information staff may not enter without explicit approval.
- Human verification expectations and who is accountable for AI-assisted work.
- Applicable customer or employee disclosure rules.
- Account, access, and security expectations.
- The policy owner, training, reporting channel, exception process, and review triggers.
This is a useful starting structure, not a universal legal checklist. The applicable duties cannot be determined without details such as your location, sector, workforce, contracts, data, and AI use cases. If your business handles regulated information or plans consequential AI uses, seek advice qualified for your jurisdiction and industry.
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