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A small business can set a useful AI governance policy without building a formal compliance department: name an owner, record approved tools and uses, set rules for data and human review, and create a way to report problems. Scale the checks to the potential impact of each use. The NIST AI Risk Management Framework (AI RMF) is a voluntary structure for organizing that work—not a universal legal safe harbor.
Start with a named owner and a clear scope
Write down who and what the policy covers: employees, contractors, AI tools, connected systems, and business activities. Assign one person to maintain the inventory, approve routine tools, explain the rules to staff, and update the policy. Name an escalation contact and identify who must approve higher-risk uses. In a small company, one person may hold several of these responsibilities; what matters is that staff know who is accountable.
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State the policy’s purpose in practical terms: to let the business use AI where it is useful while protecting people, information, and business operations. Make clear that employees must follow applicable contracts and laws as well as the policy.
Build an inventory before approving uses
Track the use case, not just the product name. The same AI service can present very different risks when used to draft an internal outline versus assess a worker or make a customer decision. For each tool or workflow, record:
- Tool and vendor, business purpose, and staff or contractors who use it.
- Information entered, including whether it contains personal, confidential, or otherwise sensitive data.
- Who may be affected by the output and where it goes, such as an internal document, customer communication, or business system.
- Whether the output influences a consequential decision, who checks it, and how errors can be corrected or escalated.
Require staff to seek approval before adopting a new AI service or connecting one to business systems. This keeps shadow use—tools adopted without review—from bypassing the business’s privacy and security checks.
Use risk tiers to decide how much review is needed
A simple tiering system helps match oversight to the likely consequences. The tiers below are a practical way to apply risk management; they are not a taxonomy mandated by NIST.
Lower impact: reversible, easy-to-check work
Examples may include brainstorming or drafting an internal outline with non-sensitive information. Allow lighter checks where a person can readily verify the result and mistakes are easy to undo. Staff should still check outputs before relying on them.
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Higher impact: decisions that materially affect people or the business
Uses involving employment, eligibility, finances, health, safety, legal rights, or sensitive customer decisions warrant heightened review. Consider disallowing a use unless the business has appropriate expertise and safeguards. Assess potential impact, data sensitivity, reversibility, exposure to customers or other external parties, vendor controls, and the staff capacity to supervise and respond. If the business cannot check an output or manage its effects, do not approve that use.
Set permitted uses and firm boundaries
List approved tools and tasks, along with any conditions on their use. Tell staff not to present unchecked generated material as verified fact, rely on AI output as the sole basis for consequential decisions, or enter protected or confidential information into an unapproved service. Explain how to request approval for a new tool or use case and who will decide.
Rules should fit the business’s actual work, contracts, and legal obligations. A blanket permission to use any AI tool is difficult to reconcile with responsible oversight; a blanket ban may be unnecessary where a low-impact task can be safely checked. Define the boundaries that staff can apply in their day-to-day work.
Rank #3
Protect data and assess vendors before use
Classify the information staff handle and state what may be entered into each approved tool. Identify prohibited data and any permitted data that requires extra safeguards. Before approving a vendor, review how it handles prompts and outputs, its retention and deletion options, access controls, security practices, and relevant contract terms. Do not assume that information entered into a tool is private or handled in a particular way without checking the vendor’s terms and settings.
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Require human review where an output matters
For any use that could materially affect a customer, worker, or business decision, name the person responsible for reviewing the output before it is acted on. Set checks appropriate to the task, including accuracy, unsupported claims, bias, privacy, and context. Customer-facing material should be checked before it is sent; consequential recommendations should not become decisions without accountable human review.
Tell reviewers what to do when an output is uncertain, inaccurate, or potentially harmful: correct it, withhold it, or escalate it to a named person. Keep appropriate records for consequential uses so the business can understand what happened and address an error. NIST offers risk-management and trustworthiness guidance, but does not prescribe one universal human-review threshold for every small business.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Monitor use, handle incidents, and review the policy
Give staff a simple route to report inaccurate outputs, suspected data exposure, security events, or unexpected effects on people or operations. Specify who can pause a tool or use case, assess the impact, and determine whether notification obligations apply. Do not leave employees to decide on their own how to handle a serious incident.
Set a review date and revisit the inventory and rules when tools, workflows, vendors, or relevant requirements change. Check reported incidents and whether staff are following the policy; provide additional guidance or training when a gap appears. The NIST AI RMF Playbook offers suggested actions for reaching outcomes in the framework, which can help turn broad policy commitments into internal tasks.
Best Value
Use NIST as a framework, not a legal guarantee
NIST describes the AI RMF as voluntary and intended to improve how organizations incorporate trustworthiness considerations into AI design, development, use, and evaluation. NIST AI RMF 1.0 was released on January 26, 2023, and its Generative AI Profile was released on July 26, 2024. NIST says the framework is being revised, so check its AI Risk Management Framework page for current status before relying on a particular version.
The Generative AI Profile applies the AI RMF to generative AI and offers suggested actions for governing, mapping, measuring, and managing risks. NIST describes a profile as an implementation of the framework’s functions, categories, and subcategories for a particular setting or technology, based on the user’s requirements, risk tolerance, and resources. It is a resource to adapt—not a ready-made policy or a certification of compliance.
Neither using the AI RMF nor adopting a general small-business policy establishes which laws apply to a particular company. Duties depend on location, industry, data, customers, and use case. For AI that affects employment, credit, health, safety, eligibility, or other consequential decisions, get advice specific to the relevant jurisdiction and sector rather than treating a voluntary framework as sufficient.
Quick Recap
A practical policy checklist
- Name the policy owner, escalation contact, and approvers.
- Define covered people, tools, systems, and activities.
- Inventory each tool and use case, including data, affected people, output destination, and reviewer.
- Set risk-based approval requirements and prohibited uses.
- Document data rules and vendor privacy, retention, deletion, access, security, and contract checks.
- Require named human review and correction or escalation for consequential or customer-facing outputs.
- Provide an incident-reporting route, authority to pause use, and a process to assess impact and notification duties.
- Set a review date and trigger reviews when tools, workflows, vendors, or requirements change.
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