The compliance officer's guide to evaluating AI tools before they enter your stack

Your team wants to deploy a new AI tool. They are excited about the demo. They have already been using the free trial for two weeks. Now they need your approval.

Here is how to evaluate it in 30 minutes before it enters your compliance perimeter.

Step 1: Data flow audit

Three questions. If the vendor cannot answer all three clearly, stop here.

Where does the data go when it enters this tool? Is it stored on the vendor's servers, processed in memory and discarded, or sent to a third-party API? The answer should be specific. "Our servers" is insufficient. What country? What cloud provider? Is the data encrypted in transit and at rest?

Is input data used for model training? Many AI tools train on user input by default. If your compliance data, customer records, or financial information is entering a model training pipeline, you have a data governance problem. The answer must be unambiguous.

Can data be deleted on request? If you need to remove a customer's data from the tool's storage, is that possible? How long does it take? Is the deletion verifiable?

Step 2: Access control review

Who on your team will have access to this tool? Can you restrict access by role? Can you enforce the principle of least privilege?

Does the tool provide an audit log? Can you see who used the tool, when, and with what input data? If a compliance issue arises, can you reconstruct what happened?

If the tool does not offer role-based access and audit logging, it is a governance gap. Every user has the same access and no one can verify what anyone did.

Step 3: Output validation process

Before this tool goes live, define how your team will verify that the AI's outputs are correct. Every AI tool has a baseline error rate. The question is whether anyone will catch the errors.

Define the review step: who reviews AI outputs before they are acted on? What does the review consist of? Is it a glance or a structured check against source data?

Define the escalation: when someone finds an AI output that appears incorrect, what do they do? Who do they notify? How is it documented?

This process must exist before the tool is deployed. Not after the first error appears in a client-facing document.

Step 4: Vendor agreement check

Read the terms of service. Not the marketing page. The legal terms.

Does the agreement allow the vendor to use your data for training? Look for phrases like "improve our services" or "enhance our models." These are often data training provisions.

If you need a BAA or DPA, does the vendor offer one that specifically covers AI-processed data? Many generic BAAs were written before AI tools existed and do not address how AI processes, stores, or learns from protected data.

What are the vendor's breach notification obligations? If your data is exposed through the AI tool, how quickly will you be notified? What information will you receive?

Step 5: Exit strategy

What happens if this tool doubles its price next quarter? Gets acquired? Changes its data handling policies? Shuts down?

Can you export your data? In what format? How long does export take?

Can you replicate the workflow with a different tool? If the answer is no, you are building a dependency, not adding a capability. The value of the tool decreases the harder it is to leave.

The 30-minute investment

This evaluation takes 30 minutes per tool. The alternative, discovering a compliance gap after deployment, takes months to remediate. Legal review, incident documentation, data migration, user retraining, and the regulatory risk of having processed protected data through an ungoverned tool.

Thirty minutes is cheap insurance.

Book a governance sprint — a governance sprint includes building this evaluation process for your specific compliance requirements.