How to test an AI assistant with synthetic customer records
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A small fictional dataset can help a team check an AI assistant’s workflow before exposing real customer information. It should contain enough variation to test the task while avoiding actual people, working credentials and production destinations.
The purpose is to observe behaviour in a contained environment. It is not proof that the assistant will behave safely in every live situation. Use this guide with the AI-access review and document the permissions in an AI permission register.
Build the records from the task
For a delivery-query assistant, you might need an order reference, fictional customer label, delivery status and an approved response instruction. You probably do not need a realistic identity number, a complete street address or a payment-card number to test whether it can draft a status reply.
Use clearly fictional identifiers such as TEST-ORDER-001 and CUSTOMER-ALPHA. Leave fields blank where their values do not matter. If a system requires an email-shaped value, use an approved test mailbox or a reserved example-domain address and disable external delivery.
| Test record | Situation | Expected workflow |
|---|---|---|
| TEST-ORDER-001 | Delivery confirmed | Draft a reply using the approved status |
| TEST-ORDER-002 | Status missing | Escalate rather than invent a delivery date |
| TEST-ORDER-003 | Two similar order references | Ask for the required clarification |
| TEST-ORDER-004 | Record outside the permitted scope | Respect the access boundary |
| TEST-ORDER-005 | Request changes payment details | Stop at the authorised approval route |
Write the expected result before running the test
State what a reviewer should see in the draft and in the connected system. “The assistant gave a confident answer” is not a useful success condition. “The draft uses the status in TEST-ORDER-001 and no unrelated customer record” is something a reviewer can examine.
Include the cases where the assistant should stop. Missing information, conflicting instructions and an unavailable tool should lead to a defined fallback, not a guess that makes the demonstration look complete.
Control the destination as well as the data
A harmless-looking example can still cause a real action if connected to a production mailbox or billing system. Use a separate test workspace and an account with the intended limited permissions. Restrict delivery through the system itself.
Check that a test reference cannot match a live record through an overly broad search. Keep the environment and file names clearly labelled so a colleague does not import the pack into production as customer data.
Record the version and the evidence
Keep the date, application or model version where available, connection scope, input, expected result, observed output and tool outcome. A screenshot of a draft does not establish whether another tool changed a record in the background.
Use a short finding format: the test case, the difference from the expected result, the affected control and the person responsible for review. Avoid storing an unnecessarily complete copy of every interaction if a smaller record will support the decision.
Do not confuse fictional with automatically safe
A dataset copied from production and lightly renamed may still preserve identifying details. Create the examples from the workflow requirements instead. Review free-text notes, attachments and file metadata as well as the obvious name field.
A generated name or address can also resemble a real one by coincidence. Prefer explicit test labels where realism is unnecessary. The NIST Generative AI Profile is a broader reference for considering information and evaluation risks; the sample pack here is an original practical exercise.
What should happen before production access?
Have the responsible owner review unresolved findings, demonstrate revocation and approve the actual live scope. Start with the smallest useful workflow and monitor its outcomes. Reuse the test pack when a tool or permission changes, adding a case for any failure the pilot reveals.
Featured image: AI-generated conceptual illustration.
