LLM Compliance: A Practical Review for Workplace Use

LLM compliance is the work of checking that a particular use of a large language model, an AI system that generates language, meets the requirements that apply to your organization. The review must cover the actual data, service, users, actions, and decisions involved. A vendor’s assurances alone cannot answer every question about your use.

fenbs can keep the human-approved operating rules and review decisions available to connected assistants. It does not certify compliance or decide which obligations apply. Its useful role is retaining what your people agreed and the work needed to put that agreement into practice.

What exactly are you approving?

Approve a defined use case rather than “AI” as a category. Drafting an internal summary from public sources is different from assessing an employee or sending a statement to a customer. Write the purpose, intended users, approved information, expected output, and consequences of a mistake in ordinary language.

If you need a basic explanation of the technology, start with what an LLM is. For the workplace review, focus on what the system will actually do. Include connected tools: an assistant that can change a record or send a message has a different operating scope from one that only produces a draft.

Consider a hypothetical HR team preparing a plain-language summary of an approved benefits handbook. A useful boundary might allow drafting from that handbook but require an HR reviewer to approve publication. The same approval should not silently extend to deciding an individual employee’s entitlement or interpreting an unresolved policy conflict.

Which requirements need specialist review?

Ask the responsible legal, privacy, security, and business owners to identify requirements for the specific work. Relevant questions can include contracts, confidentiality, intellectual property, record retention, employment decisions, and sector-specific rules. Record who checked applicability rather than asking the model to declare its own use compliant.

The NIST AI Risk Management Framework↗ (opens in a new tab) provides voluntary guidance for managing AI risks. It can organize your review, but adopting it does not replace applicable law or your organization’s contracts. Keep the source and status of each requirement visible so an internal preference is not mistaken for a legal obligation.

A useful review output has three parts: what is approved, what is prohibited, and what still needs an answer. If nobody can explain who owns an unresolved requirement, do not hide it inside an optimistic approval summary. Give it a separate question, responsible reviewer, and next action.

Can the proposed data enter this service?

Map the information from its original location through the AI service to the resulting output. Include uploaded files, prompts, connected systems, generated documents, logs, and any later sharing. The fact that a file is accessible to an employee does not by itself mean the employee may send it to a new service.

• Account and terms: identify the exact service, account type, agreement, and organization that controls the account.
• Data use: review training terms, retention, deletion, support access, and any settings or exceptions relevant to the work.
• Access: check who can read the sources, chats, outputs, and connected systems, including people outside the immediate project.
• Minimum input: decide which information is necessary and whether an approved substitute or summary can answer the question.
• Exit: identify where records must remain if access ends or the organization changes providers.

Check these points against the provider’s current documentation and your own agreement. A training restriction does not automatically answer retention, access, or contractual questions. If you are considering a self-managed model, open-source LLMs need their own license and deployment review; having the model files does not finish the organization’s assessment.

How will people check output and control actions?

Define what a reviewer must inspect before the output is used. “Human review required” is too vague if the reviewer has no original sources, no time, or no ability to reject the result. Specify the evidence, the review question, and the action that remains blocked until approval.

For example, a benefits summary reviewer could check every eligibility statement against the approved handbook, confirm that exceptions remain visible, and approve the exact version to publish. If the assistant cannot find support for a statement, the workflow should produce an unresolved question. It should not fill the gap with a plausible policy.

NIST’s generative AI risk profile↗ (opens in a new tab) recommends checking generated sources and citations. Build that into the task rather than treating a source list as proof that the text is correct. Open the cited material and verify that it supports the relevant claim.

Record the required review boundary in fenbs Decisions and rules, so connected assistants can read the approved instruction before helping with the project.

What does a useful approval record contain?

A decision record should explain the approved use well enough that someone else can recognize when the work falls outside it. Include the human decider, business owner, evidence reviewed, conditions, exclusions, and events that require another review. Keep links to restricted evidence in the system approved to hold it.

Use: Draft an internal benefits-handbook summary.
Inputs: The approved handbook and public reference material only.
Output: A draft for HR review; no automatic publication.
Approval: A named HR reviewer checks every policy statement.
Escalation: Conflicting sources or individual eligibility questions.
Revisit when: The handbook, service terms, or permitted use changes.

The DOJ’s compliance-program evaluation guidance↗ (opens in a new tab) asks about AI governance, accountability, and controls for intended use. Your record should make the actual arrangement understandable, rather than collecting impressive terms with no connection to daily work.

How to do this with fenbs

1. Create a free account and add a project for one proposed workplace use case. Describe its purpose and current review status.
2. Add tasks for the data, contractual, security, and output checks your people identify. State the evidence needed and leave unverified checks open.
3. Record the human decision in Decisions and rules. Separate continuing rules from the approval of one specific use, and keep unresolved questions open.
4. Connect a supported assistant through Integrations. Ask it to read the project context before helping and save progress, including waiting decisions, before stopping.

fenbs is free for one person, no credit card. It is not the right fit for enforcing data-loss prevention, blocking prohibited prompts, or satisfying specialist retention requirements. Put those controls in suitable systems and use fenbs for the permitted coordination and human decision record.

When should the approval be revisited?

Revisit it when the purpose, information, audience, provider terms, connected permissions, or possible consequences change. Review actual mistakes and near misses as well. An assistant that begins sending the summary to employees has crossed a different boundary from an assistant that drafts it for HR.

Ask users to demonstrate the process with a missing source and a prohibited input before expanding access. Check that they know where to ask questions and who can authorize an exception. The AI governance hub covers related review topics; keep each approval tied to the work it actually permits.