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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →A useful AI compliance checklist starts with an inventory of how your team actually uses AI, then assigns an owner, scopes applicable obligations, assesses risks, and records controls and follow-up. It is a practical risk-management process—not a universal legal checklist or proof that your business complies with every law.
What an AI compliance checklist can—and cannot—do
For a small team, the checklist should help you answer five questions for each AI use: What is it for? Who and what data could it affect? What could go wrong? What safeguards are in place? Who reviews whether those safeguards still work?
NIST’s AI Risk Management Framework (AI RMF) 1.0 is voluntary guidance, not a legal requirement by itself. Its four functions—Govern, Map, Measure, and Manage—offer a useful way to organize your work. NIST’s companion Playbook suggests actions, but NIST’s AI Risk Management Framework resource center says the Playbook is neither a checklist nor a set of steps that every organization must follow in full. Use it as adaptable guidance, not as a mandated sequence or a compliance certificate.
Applicable legal duties depend on where your business operates, where affected people are located, your sector, and whether you build or deploy AI. Scope those obligations separately; this general process cannot determine every jurisdiction’s requirements.
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Build the checklist in nine steps
1. Assign an accountable owner
Name one person to maintain the inventory and coordinate reviews. A small team can combine responsibilities, but record who approves a use, who owns its day-to-day operation, and who receives incident reports. This is a practical governance choice, not a NIST-prescribed staffing rule.
2. Inventory AI uses across the business
List more than standalone chatbots. Include AI tools purchased directly, AI features embedded in existing software, internally built systems, and pilots—even if they are not yet used with customers. Keep one entry for each distinct purpose or workflow, since the same tool can create different risks in different uses.
For each entry, record:
- System, product, or vendor name; business purpose; and status, such as pilot or active use.
- Business owner and team using it; whether your organization develops the system, deploys it, or both.
- Users and people who may be affected by its outputs or decisions.
- Inputs, outputs, data sensitivity, and where the system is used.
- Applicable review date, decision-maker, and links to internal records or vendor documentation.
This is a practical inventory template, not a verbatim NIST requirement. Include systems used informally by staff so that approval and safeguards are not limited to tools purchased by IT.
3. Scope applicable laws and other obligations
For each use, record the countries or regions where the business operates and where customers or other affected people are located; the relevant sector; your role in the AI lifecycle; and any contracts or customer commitments that apply. Have qualified, jurisdiction-specific advisers assess relevant privacy, consumer-protection, employment, health, financial, children’s-data, intellectual-property, and AI-specific requirements where appropriate. Do not treat NIST guidance or this checklist as a substitute for that analysis.
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Document what information enters the system, what it returns, and where the information goes. Note whether it includes personal, confidential, regulated, or children’s information; who can access it; and whose decisions, opportunities, or treatment may be affected. For a small organization beginning to formalize information-security and privacy risk management, NIST Special Publication 1314, published in July 2024, is an introductory resource for small entities.
5. Assess risk and set a review level
Consider possible harm to people and the business, not just whether the software produces a technically plausible answer. Record risks involving privacy and security, reliability, unfair outcomes, transparency, and whether someone can challenge or correct an error. Give closer review to uses involving consequential decisions, sensitive information, public-facing content, or actions the system can take without human approval.
For generative AI, NIST’s Generative AI Profile, released July 26, 2024, provides additional guidance on risks distinctive to generative systems. It can inform your assessment, but it does not replace legal scoping or a review of the specific tool and use.
6. Choose controls and evidence for each use
Match safeguards to the assessed risk. For each control, write down the responsible person and what record will show that it is operating. Depending on the use, consider:
- Allowed and prohibited purposes, including uses that require advance approval.
- Data restrictions and approved tools; instructions not to enter sensitive data unless the use and service are approved.
- Human review before consequential decisions, external publication, or other defined actions.
- Checks for accuracy, fairness, source quality, and misleading or fabricated output where relevant.
- Access controls, logging, documentation, vendor conditions, and retention or deletion practices.
- Escalation routes and stop conditions—for example, a serious error, unexpected data exposure, or an output that could harm someone.
Possible evidence includes an approval record, a completed review, a test result, an access list, or an incident log. These are practical options for a team to derive from risk management; NIST does not prescribe this exact control list.
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7. Review external AI vendors before sharing data
Before sending business or personal information to an external service, review its terms and settings for retention, use of inputs for training or model improvement, access, deletion, security, incident notification, and subprocessors. Record your findings and any contractual allocation of responsibilities. The NIST small-entity guide supports managing security and privacy risk; it is not a specific AI vendor contract form.
8. Train staff and provide a reporting route
Tell employees which tools are approved, what information may be entered, what outputs must be checked, and how to report unexpected behavior or a suspected incident. Record who completed the guidance and refresh it when tools, terms, or approved uses change. Keep the directions short enough that staff can follow them during their actual work.
9. Monitor, review, and update
Set a review date for each use and reassess sooner after a material change in model, data, purpose, users, vendor terms, or applicable law. Review incidents, complaints, and observed performance; record decisions and corrective actions. Treat approval as an ongoing process: NIST frames AI risk management across design, development, deployment, and use, rather than as a one-time signoff.
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Use a compact record for each AI use
A spreadsheet is often enough to start. Keep an inventory row for each use and a linked review record for its risk assessment, controls, and changes. The following fields turn the steps above into a maintainable working checklist:
| Record section | Fields to capture |
|---|---|
| Identification | Tool or system; vendor; purpose; status; business owner; users; develop/deploy role |
| People and data | Affected people; input and output types; sensitivity; data destination; access; retention or deletion considerations |
| Scope | Operating and affected-person locations; sector; relevant contracts; legal review owner or status |
| Risk and decision | Potential harms; likelihood or severity rating if your team uses one; review level; approver; approval date |
| Safeguards | Allowed/prohibited uses; data rules; human review; output checks; access and vendor controls; stop conditions |
| Evidence and upkeep | Control evidence location; staff training record; next review date; incidents; changes; corrective actions |
Do not collect fields just to make a spreadsheet look comprehensive. Each entry should help someone make a decision, operate a safeguard, or show what was reviewed.
Keep sensitive information out of unapproved AI tools
Make the rule operational rather than relying on a broad warning to “be careful.” Publish a short approved-tools list, say which data categories may be entered into each approved service, and explain how staff can get an exception reviewed. If a tool’s retention, training use, access, or deletion terms are unknown, do not assume that business or personal data is protected; pause that use until the owner has checked the terms and settings.
Give staff a simple alternative when a tool is not approved: use a non-sensitive example, remove identifying or confidential details where that is safe and permitted, or ask the designated owner for review. A reporting route should make it easy to flag accidental disclosure or an unexpected output quickly.
Address customer-facing AI claims and outputs
For U.S. businesses, the FTC’s September 25, 2024 announcement of Operation AI Comply described actions involving deceptive AI claims, fake reviews, purported AI legal services, and AI-enabled business-opportunity claims. The practical lesson is to substantiate claims about what AI can do and prevent deceptive AI-generated reviews or other misleading outputs. The announcement is illustrative; it is not a complete analysis of the laws that apply to a particular business.
FTC Chair Lina M. Khan said, “Using AI tools to trick, mislead, or defraud people is illegal.”
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