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

To make sure your model quality evaluations and evals dashboard are compliant with PII redaction requirements, treat PII handling as part of the evaluation pipeline, not just the dashboard UI.

Practical checklist

1) Redact before data reaches the dashboard

• Detect and remove or mask PII at ingestion for:
  • prompts
  • model outputs
  • reference answers / labels
  • annotations and reviewer comments
  • traces, logs, tool calls, and metadata
• Prefer pre-display redaction plus storage-time redaction if possible.

2) Use a PII detection policy

Define what counts as PII for your organization, for example:

• names
• email addresses
• phone numbers
• physical addresses
• account numbers
• government IDs
• IP addresses
• credentials / secrets
• free-text fields that may contain personal data

Then decide per field:

• redact completely
• mask partially
• hash/tokenize
• drop entirely
• retain only if explicitly approved

3) Redact in all evaluation artifacts

Make sure the following are sanitized consistently:

• evaluation datasets
• ground truth labels
• experiment metadata
• failure case examples
• screenshots / exports / CSV downloads
• API responses from the evals system
• audit logs, if they include content payloads

4) Minimize what gets stored

Only keep what you need for evaluation:

• avoid storing raw prompts if a redacted version is sufficient
• store unique IDs instead of full user records
• strip out free-form text where not needed
• keep only aggregated metrics when possible

5) Separate identifiers from content

Use a joinable surrogate key:

• case_id
• conversation_id
• user_id as internal tokenized ID

Keep the mapping to real identities in a separate, access-controlled system.

6) Restrict access and retention

• limit dashboard access by role
• use row-level or field-level permissions if available
• set retention policies for raw evaluation data
• delete or re-redact historical records when policies change

7) Validate redaction automatically

Add tests and scans to your pipeline:

• regex checks for common PII patterns
• NER/entity detectors for names and locations
• secret scanning for API keys/tokens
• sampling-based human review
• unit tests for known PII examples

8) Review dashboard export paths

Even if the UI is redacted, check:

• CSV exports
• API access
• downloadable artifacts
• alerts / notifications
• cached previews
• browser logs if applicable

9) Maintain an audit trail

For compliance, log:

• what redaction method was applied
• when it was applied
• which policy/version was used
• who accessed raw vs. redacted data
• exceptions and approvals

10) Align with legal/privacy requirements

Depending on your jurisdiction and data type, you may need controls for:

• GDPR
• CCPA/CPRA
• HIPAA
• SOC 2 / ISO 27001 internal controls
• company-specific privacy policy

Recommended architecture

A safe pattern is:

1. Collect raw eval data in a restricted system
2. Run PII detection/redaction automatically
3. Store redacted copies in the evals database/dashboard
4. Expose only redacted data to evaluators and dashboards
5. Keep raw data separate with tight access and short retention

Good operational rule

If a human evaluator can see it in the dashboard, assume it must be treated as potentially persistent and exportable, and therefore fully compliant with your redaction policy.

If you want, I can also help you design:

• a PII redaction policy for evals
• a redaction pipeline architecture
• or a compliance checklist specifically for an OpenAI-style evals dashboard.