AI Governance Regulations: Identify What Applies to Your Work

AI governance regulations are the legal requirements that apply to how an organization develops, buys, or uses AI. To identify them, start with the actual activity, the people affected, the data involved, and the places where the organization operates. A general AI policy cannot answer those questions by itself.

Tools like fenbs can retain the sources, owners, and human approval decisions after specialists have assessed the requirements. The useful record explains why a particular use was allowed, what conditions apply, and who must check again when something changes.

Why should you start with the activity rather than the AI label?

The same model can help draft a public newsletter, rank applicants, or summarize health records. Those uses raise different questions even when the software is identical. Describe the action the organization will take because of the output, rather than stopping at a product name.

For example, a hypothetical retailer might use AI to draft an advertisement. The FTC advertising basics↗ (opens in a new tab) say advertising claims must be truthful, evidence-based, and neither deceptive nor unfair. The team should therefore review the claims it publishes, regardless of who drafted them.

Write a short use-case description before asking for approval: who uses the tool, what goes in, what comes out, and what a person does next. Include affected customers or employees, even when they never interact with the AI. Our guide to using AI at work provides broader everyday examples.

How do law, guidance, standards, and company rules differ?

Label each source before turning it into an instruction. A document can be useful without being a legal obligation. Conversely, a contract can create a requirement for your organization even when the referenced practice is ordinarily voluntary.

The NIST AI Risk Management Framework↗ (opens in a new tab) is intended for voluntary use. Following a framework is different from establishing that every applicable legal obligation has been met. Record that distinction so an assistant cannot quietly turn a recommendation into a supposed legal mandate.

Which questions help a US team find applicable requirements?

Build a screening list around the business rather than attempting a universal list of AI laws. The point is to route questions to the right reviewer. Counsel or the responsible compliance specialist can then confirm the applicable text, exceptions, and current effective dates.

• Activity: does the output affect hiring, credit, healthcare, advertising, education, or another regulated service?
• People: who experiences the result, and can a person challenge or correct it?
• Data: does the workflow use personal, confidential, health, or other restricted information?
• Location: which federal, state, local, or non-US requirements need review?
• Organization: are you developing the system, using it, selling it, or providing a service to someone else?
• Promises: what do contracts, privacy notices, and customer-facing statements say about this use?

Employment is a useful example of why the screening matters. The EEOC prohibited-practices guidance↗ (opens in a new tab) explains that employment discrimination rules cover hiring and other workplace decisions, including certain neutral practices with discriminatory effects. A purchasing decision should therefore involve the employment reviewer before an applicant-ranking workflow proceeds.

Make the review owner explicit; the separate guide to AI governance roles explains responsibility assignments. Use that person to resolve uncertain applicability rather than asking an assistant to choose a convenient interpretation.

What should an applicability record contain?

Keep the conclusion attached to its facts. A note saying “legal approved” is hard to use when the tool changes or a new colleague asks what was actually considered. A useful record lets a reviewer reconstruct the scope without reopening every meeting.

• The use case, business owner, and intended users.
• The official source and the relevant provision or guidance section.
• The date checked, document version where available, and person who assessed it.
• The facts that make it applicable, inapplicable, or still uncertain.
• Required actions, evidence location, unresolved questions, and next review trigger.

For health data, do not infer permission from a general cloud security statement. HHS cloud-computing guidance↗ (opens in a new tab) discusses HIPAA obligations and business associate arrangements for services involving protected health information. Let the privacy specialist assess the actual service and data flow.

The record can point to restricted evidence without copying it into a general workspace. In fenbs, keep approved references and the resulting decisions together through the shared work record.

When should a previously approved use be reviewed again?

Set review triggers when approval is recorded. A new data category, different audience, added automation, changed contract, or revised official source can invalidate an earlier assessment. A tool used only for drafting may need a different review if someone wants it to send the result automatically.

Give one person responsibility for checking the sources that matter to the use case. Record an unresolved change as a question with an owner, rather than silently retaining the previous conclusion. If a required condition is missing, route the decision to the person authorized to stop or restrict the use. Preserve the previous assessment and explain what changed so the next reviewer can follow the reasoning without reconstructing old conversations.

This is also a memory problem: the next chat needs the conditions, not just the final word “approved.” The explanation of an AI brain shows why work records should live outside individual conversations.

How to do this with fenbs?

1. Create a free account and add a project for one AI use case or a clearly bounded review.
2. Write the use-case facts and create tasks for the legal, privacy, security, or business questions that remain open. Name the responsible people in the plan.
3. Save official references in Links and listings. Record what the authorized person decided in Decisions and rules, including scope and conditions.
4. Connect a supported assistant through Integrations. Ask it to read the rules before working and save progress with the next review action before stopping.

fenbs does not determine which laws apply or provide legal approval. It is not the right fit when you need automated legal monitoring, a regulated evidence repository, or technical enforcement of a compliance control.

Start with the smallest unresolved review instead of importing an entire policy library. The small-team governance guide and AI governance hub provide nearby reading once the source, owner, and next decision are clear.