How do I ensure my model quality evaluations with an evals dashboard are compliant with PII redaction requirements?

To keep model-quality evaluations compliant with PII redaction requirements, treat the evals dashboard as a controlled processing environment and make sure only de-identified data reaches it unless you have a clear legal basis and explicit controls for handling raw PII.

A practical compliance checklist:

1) Redact before data enters the dashboard

• Remove or mask PII at the source pipeline, not just in the UI.
• Apply redaction to:
  • prompts
  • model outputs
  • annotations/labels
  • traces/logs
  • attachments and metadata
• Use deterministic masking for repeated entities if you need to preserve eval usefulness.

2) Define what counts as PII for your org

Create a policy covering:

• direct identifiers: names, email, phone, SSN, passport, account numbers
• quasi-identifiers: location, DOB, employer, device IDs
• sensitive data: health, financial, biometrics, children’s data
• free-text fields, where PII often appears unexpectedly

3) Validate redaction with automated checks

Add tests that fail a build or block ingestion if:

• regex/entity detectors find PII
• raw logs contain unredacted identifiers
• attachments include unscanned text
• annotations include copied user data

Use layered detection:

• rules/regex for known formats
• NLP/entity recognition for names and places
• sampling-based manual review

4) Keep raw PII out of exports and logs

Ensure your dashboard and surrounding tooling:

• doesn’t store raw prompts by default
• excludes PII from experiment names, tags, and comments
• redacts before telemetry, analytics, and error reporting
• prevents clipboard/export/download leakage

5) Minimize retention

• Keep only what is needed for evaluation.
• Set short retention windows for raw inputs, if they must exist at all.
• Separate evaluation artifacts from production data.
• Ensure backups and replicas follow the same retention/redaction rules.

6) Restrict access

• Use role-based access control.
• Limit who can view even redacted evaluation data.
• Audit access to dashboards, exports, and admin settings.
• Require approval for any unredacted data access.

7) Be careful with prompt/output examples

Model eval dashboards often display examples that can accidentally include PII. To reduce risk:

• replace values with placeholders like [EMAIL], [PHONE], [NAME_1]
• preserve structure but not content
• redact both user input and model-generated continuations
• treat screenshots and shared reports as data exports

8) Document governance and accountability

Maintain:

• a data inventory
• redaction rules
• retention policy
• incident response process
• DPIA/PIA if applicable
• vendor/security review for the evals dashboard provider

9) Verify vendor capabilities

If you’re using a third-party dashboard, confirm:

• encryption in transit/at rest
• data residency options
• no training on your data without consent
• deletion guarantees
• subprocessors and access controls
• support for customer-managed keys, if required

10) Test the whole pipeline end to end

Run a “PII canary” test:

• insert fake but realistic PII into sample evals
• confirm it is redacted at ingestion
• confirm it does not appear in UI, search, exports, alerts, or logs

If you want, I can help you build a PII-safe evals pipeline design or a redaction policy/checklist tailored to your stack.