How to Build an AI Evaluation Set Without Exposing Customer Data

AI teams need representative examples to measure whether a system answers correctly, but evaluation sets can become an overlooked store of personal or confidential information. A useful evaluation set reflects the task while including only the data needed to assess behavior.

Set rules before collecting examples

Define the questions, outcomes, and error types to measure. Confirm who approves examples and where they may be stored. Use synthetic or transformed data when it preserves the behavior being tested; if real examples are necessary, remove identifiers and unrelated fields.

• Limit access to people responsible for evaluation.
• Track source, approval, and retention for each dataset version.
• Test whether redaction changes the task or model behavior.
• Keep evaluation results separate from customer records.

Review the set when the use case or data source changes. Informatix Systems lists AI integration and data services. Contact Informatix Systems to discuss responsible AI evaluation practices.