Evaluation quality improves when you test on real workflows. But "real workflows" often contain sensitive data: customer details, employee information, contracts, incident tickets, and internal IP. If you treat evaluation datasets as harmless test data, you create privacy, security and compliance risk.
Handling sensitive data in AI evaluations is about two goals at once: measure performance realistically, and keep data protected throughout the evaluation lifecycle.
Classify evaluation data before you collect it
Start with data classification. You need to know what you are handling before you build pipelines around it:
- Define evaluation tiers based on sensitivity (public, internal, confidential, regulated).
- Tag datasets with owners, permitted uses and retention periods.
- Align classification to enterprise standards (see data classification).
Minimise: store the least you can, for the shortest time
Minimisation is your strongest control:
- Prefer short excerpts over full documents.
- Prefer hashed or tokenised identifiers over raw IDs.
- Strip attachments unless they are required for the scenario.
- Keep separate datasets for evaluation and training to avoid accidental reuse.
See data minimisation for practical patterns.
Redact PII and secrets with a repeatable pipeline
If you use real text, redaction should be systematic, not manual. A typical pipeline:
- Detect sensitive entities (names, emails, phone numbers, account IDs, secrets).
- Apply consistent replacements so the text remains coherent.
- Keep a secure mapping only if you truly need reversibility.
This mirrors production-grade PII handling (see PII redaction pipelines and data loss prevention).
Use synthetic data intentionally
Synthetic data helps when you cannot store real data, but it can also mislead you if it is too clean. A balanced approach:
- Use synthetic examples to expand coverage of edge cases.
- Validate synthetic cases against real distribution patterns.
- Keep a smaller, heavily controlled real dataset for realism checks.
See synthetic data for trade-offs.
Secure human review workflows
Human evaluation is often where sensitive data leaks, because it leaves the controlled system boundary. Controls should include:
- Access control. Reviewers get least privilege and are audited.
- Secure environments. Avoid copying data into unmanaged tools.
- Guidance. Clear rules about what can be exported or discussed.
This is an operations function with training and audit evidence (see human review ops).
Define retention and deletion from day one
Evaluation data accumulates quickly. Without retention controls, you build a permanent shadow data store. Define:
- Retention periods per dataset tier.
- Deletion workflows and verification (including backups and derived artefacts).
- Evidence of deletion for audits.
See data retention and deletion for patterns.
Make the process auditable
Finally, keep an audit trail:
- Who created the dataset and why.
- Which systems contributed data.
- Who accessed it and when.
- Which evaluations used it and what versions were tested.
This supports governance and incident response if something goes wrong.
Realistic evaluation is essential for AI quality. But quality without data discipline becomes risk. With classification, minimisation, redaction and secure review, you can measure what matters without exposing what should be protected.