A small team can build a useful AI compliance checklist by assigning an owner, listing every AI tool and use, scoping the laws that apply, mapping data and affected people, assessing risks, and setting controls that can be checked over time. Treat the checklist as a living record—not proof of legal compliance. The right legal obligations depend on where your business and customers are, your sector, and whether you build or use AI.
What an AI compliance checklist can—and cannot—do
A checklist helps your team see where AI is being used, decide which uses need closer review, and record how risks are handled. It can cover purchased AI services, AI features inside other software, internally built systems, and pilots that have not yet reached production.
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It cannot establish that your business complies with every applicable law. NIST’s AI Risk Management Framework (AI RMF) is voluntary guidance, not a legal safe harbor. NIST says the framework is being revised, so check its current materials when adopting it. The framework and its Playbook can help organize risk work, but they do not replace legal advice or jurisdiction-specific analysis.
NIST’s Playbook offers suggested actions under four functions. NIST’s AI Resource Center states that the Playbook “is neither a checklist nor set of steps to be followed in its entirety.” Use it as adaptable guidance rather than a mandatory sequence.
Start with a small-team ownership model
Name one person to maintain the AI inventory and coordinate reviews. That person does not have to do every task: a small team can assign data, security, legal, and business questions to the people best placed to answer them. Record who approves a use and who responds if it causes a problem. This is a practical way to make ownership clear, not a staffing rule imposed by NIST.
For each AI use, choose a review depth that reflects its potential impact. Give more attention to uses involving consequential decisions about people, sensitive information, public-facing material, or actions taken without a person checking first. A simple spreadsheet is often enough to begin; dedicated governance software or outside advisers are optional.
Build the checklist around NIST’s four functions
| NIST function | Question for your team | Practical record |
|---|---|---|
| Govern | Who owns, approves, and oversees this use? | Owner, decision-maker, staff guidance, escalation route, and review schedule. |
| Map | What is the system for, what data does it use, and who could be affected? | Inventory entry, purpose, users, inputs, outputs, data flows, and affected people. |
| Measure | What could go wrong, and how will the team identify it? | Risk assessment, checks for errors or unfair outcomes, and evidence from testing or monitoring. |
| Manage | What will the team do to reduce risk and respond when conditions change? | Controls, approval conditions, incident handling, corrective actions, and reassessment triggers. |
These functions are a useful organizing structure, not a required workflow. NIST’s Generative AI Profile, released July 26, 2024, adds guidance for risks distinctive to generative AI; it can inform reviews of tools that generate text, images, code, or other content.
Use this AI checklist for each tool or use
- Identify the use. Record the system or vendor, what the team uses it for, the business owner, whether your organization develops or deploys it, who uses it, and whether it is a pilot or in regular use.
- Describe who and what it affects. Note the people who may rely on, receive, or be affected by the output. Record the system’s inputs and outputs, including whether a person reviews them before they are used.
- Map the data. Identify whether inputs or outputs contain personal, confidential, regulated, or children’s information. Note where data goes, who can access it, and whether the system returns information to other tools or people.
- Scope the obligations. Record the locations where your business operates and where customers or other affected people are located, your sector, your role in the AI lifecycle, and relevant contracts. Identify potentially applicable privacy, consumer-protection, employment, health, financial, children’s-data, intellectual-property, and AI-specific requirements. Get qualified advice for the jurisdictions and activities that apply to your organization; this general checklist is not an exhaustive legal map.
- Assess potential harm. Consider risks to individuals and the business, including privacy or security exposure, unreliable outputs, bias or unfair outcomes, lack of transparency, and difficulty correcting or contesting an error. Record why the use needs routine or deeper review.
- Choose controls. Specify allowed and prohibited uses, data restrictions, access limits, required human review, output checks, vendor conditions, logging or documentation, escalation steps, and conditions for pausing use. For each control, state what evidence would show it is operating—for example, a review record or a documented approval.
- Review the vendor relationship. Before sending business or personal information to an external AI service, check its terms for retention, use of data for training or model improvement, access, deletion, security, incident notice, subprocessors, and how responsibilities are allocated in the contract.
- Prepare staff. Tell employees which tools are approved, what information they must not enter, what they need to verify before relying on an output, and how to report unexpected behavior. Keep a record of who received the guidance.
- Schedule reassessment. Review the entry when the model, data, purpose, users, vendor terms, or relevant legal requirements materially change. Also review incidents, complaints, observed performance, and any corrective actions.
Keep an AI inventory the team can maintain
Use one row per distinct system or use, rather than a list of vendor names alone. The same service may support several activities with different data, users, and consequences; document those separately when their risks or controls differ.
Rank #3
- System or vendor name, purpose, and status (pilot or active).
- Business owner, approver, and whether the organization develops or deploys the system.
- Users, affected people, and the locations relevant to the use.
- Inputs, outputs, data sensitivity, data destinations, and access.
- Potential harms and the reason for the chosen review depth.
- Approved and prohibited uses, required human checks, and vendor conditions.
- Evidence that controls operate, review date, incidents, and corrective actions.
NIST SP 1314, published in July 2024, is an introductory starting point for small entities building information-security and privacy risk management. It can help a team think about protecting information around AI use; it is not an AI vendor contract template or a substitute for identifying applicable law.
Set rules that prevent sensitive-data mistakes
Make the rules specific enough that staff can follow them before they paste information into a tool. State which services are approved, which data categories may be entered, what must be removed or anonymized, and who can approve an exception. If the team cannot confirm how an external service handles a particular type of information, do not treat that uncertainty as approval to share it.
Rank #4
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Pair the rules with a practical alternative: tell employees whom to ask when they are unsure and where to report accidental disclosure or unexpected output. That gives the team a path to respond rather than relying only on a blanket warning.
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For U.S. businesses, the FTC’s September 25, 2024 announcement about 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 an AI system can do and to prohibit deceptive AI-generated reviews or other misleading outputs. The announcement is an enforcement example, not a complete analysis of the laws that may apply to a particular business.
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Make the checklist a recurring process
Keep the inventory and decisions together so the team can see what was approved, under what conditions, and what changed afterward. A lightweight review cadence is more useful than a one-time signoff: revisit a use after a material change and record incidents and fixes as they occur. NIST frames AI risk management across design, development, deployment, and use, rather than as a single approval event.
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