How to Evaluate Regulatory AI Tools for Compliance Research

Before judging a tool, decide what you're asking it to do. The standard of reliability should match the task.

Low risk: summarizing a document you provide, drafting an internal explanation, brainstorming questions to ask a supplier.

Medium risk: getting oriented on an unfamiliar topic, comparing how two markets approach something, outlining a regulatory pathway.

High risk: stating a specific limit, citing a regulation, confirming current approval status, or supporting a compliance decision.

A tool can be perfectly fine for the first group and unsuitable for the third. Most problems come from using it for high-risk tasks with low-risk levels of checking.

When you evaluate a tool, look at six things.

1. Sources and citations

Does the tool show where its statements come from? The strongest tools link each claim to a specific source you can open and read. Be cautious of tools that provide citations that look plausible but don't actually support the claim, or that point to sources that can't be found. A citation is only useful if you can verify it.

2. Currency

Regulations change. Find out how current the tool's information is and how it handles updates. A tool can be accurate about the rules as they stood and wrong about the rules as they are now. If it can't tell you when information was last updated, treat everything as potentially stale.

3. Coverage

Check which markets, topics and materials the tool handles well. Many tools are strong on a few well-documented areas and weak elsewhere, but present every answer with the same confidence. Test it on your own specific areas, such as food contact materials in the markets you sell into.

4. Handling of uncertainty

A good tool says when it doesn't know, or when a question is ambiguous or unsettled. A tool that always answers confidently, including when no clear answer exists, is more dangerous than one that sometimes says "I'm not sure."

5. Data confidentiality

Compliance work often involves confidential information: formulations, supplier details, unreleased product plans. Find out what happens to what you enter. Is it stored? Used to train models? Visible to the vendor? Check this against your company's policies before uploading anything sensitive.

6. Traceability

Can you reconstruct how an answer was produced and keep a record of it? If you rely on a tool's output in a decision, you may need to show how you reached it.

Reading marketing claims tells you little. Testing tells you a lot. Here's a method you can run in an afternoon.

Step 1: Build a test set. Write 10 to 15 questions in your own area where you already know the correct answer from a primary source. Include:

• Straightforward factual questions

• Questions about a specific limit, definition or requirement

• Questions where the correct answer is "it depends" or "this is unsettled"

• Questions about something that changed recently

• One or two questions with a false premise, to see whether the tool corrects you or plays along

Step 2: Run the same set through each tool. Use identical wording so the comparison is fair.

Step 3: Check every answer against your known answers and the cited sources. Don't just ask whether it sounds right. Open the sources and see whether they say what the tool claims.

Step 4: Score each tool. For each answer, note whether it was correct, partly correct, wrong, or wrong but stated confidently. Wrong and confident is the category to watch most closely.

Step 5: Repeat with a few new questions later. Tools change, and so does the regulatory picture, so a tool that performed well once should be rechecked periodically.

The point isn't to find a perfect tool. It's to learn where each one is trustworthy and where it isn't, so you can use it for the right tasks.

• Citations that don't hold up when you open them

• No way to see sources at all

• Identical confidence on easy and hard questions

• Vague claims about being "trained on regulations" without detail on what and how current

• Reluctance to explain data handling

• Promises that a tool will "ensure compliance" or replace expert review

That last one is a particular warning sign. Compliance is a responsibility that stays with your organization, and no tool can take it on for you.

Even a good tool should sit inside a process that includes human checking. A sensible rule of thumb:

• For low-risk uses, a light review is fine.

• For anything that informs a real decision, verify specific facts against primary sources and have someone qualified review the conclusion.

• Record what was checked and by whom.

Writing this into a short internal guideline stops everyone from inventing their own standards. Keep it simple: what tools are approved, what they can be used for, what must be verified, and what information must never be entered.

The most effective teams treat AI as a fast, tireless assistant whose work always gets checked, not as an authority. That only works if someone on the team knows the subject well enough to notice when an answer looks off. A confident but wrong answer is hardest to catch for people who are new to the topic.

That's why foundational knowledge matters more, not less, as AI tools spread. If you're also comparing broader software options, our guide toregulatory intelligence tools covers the wider landscape.

Learn to use AI productively and safely withAI/ML for Food Contact Regulatory Compliance, give your team shared access through oursubscription plans, which include a Compliance Coach GPT, or browse the fullcourse catalog.