Data Classification and Sensitivity Handling Questions

Key considerations

• Legal/IP: NDAs, client contracts, and ownership clauses can restrict disclosure of code, diagrams, and performance metrics. Trade secrets and patented algorithms require extra protection. Open-source license compatibility (MIT/Apache/GPL) must be respected.
• Privacy: PII/PHI under GDPR/CCPA/HIPAA must not be exposed. Even “hashed” identifiers can be re-identified if linked to other datasets—apply privacy impact analysis.
• Security: Remove credentials, keys, endpoints, internal URLs, and config that reveal infrastructure or attacker vectors.

Safe process (operational checklist)

1. Intake & classification
   • Tag projects as public/internal/confidential during planning.
   • Maintain an artifacts registry (code, diagrams, datasets) with sensitivity labels.
2. Legal & compliance gating
   • Require a short disclosure request form for proposed public artifacts (what, why, audience).
   • Legal/Privacy teams review for contract/NDA conflicts and, if applicable, obtain client consent.
   • Run a Data Protection Impact Assessment (DPIA) for datasets.
3. Sanitization standard operating procedures
   • Code: remove/replace credentials, provider-specific IDs, and customer-specific logic. Replace with generic interfaces and stubs. Add a comment block describing removed elements and why.
   • Architecture diagrams: abstract service names, replace hostnames/IPs with placeholders, redact internal-only components or show them as "private services". Keep data flows but omit implementation details that reveal vulnerabilities.
   • Data: never publish raw PII. Use one of: (a) synthetic data generated to match schema and statistical properties; (b) aggressive anonymization with k-anonymity/differential privacy where required; (c) sampled/aggregated metrics only. Document anonymization technique and residual risk.
4. Security review & automated checks
   • Run SAST/secret-scan tools and automated checks in CI before publishing.
   • Peer review by engineering lead for technical correctness and by privacy officer for data artifacts.
5. Licensing & contributor terms
   • Choose a license consistent with company policy; add CLA or contributor guidelines when accepting external changes.
6. Approvals & audit trail
   • Maintain an approval workflow (submit → legal/privacy/security review → engineering sign-off → publishing) and record decisions and redacted artifacts in the registry.
7. Monitoring & takedown
   • Publish under a controlled channel (company blog/GitHub org). Monitor for misuse, and have a defined takedown/rollback process.

Trade-offs & rationale

• Abstraction vs. usefulness: more redaction reduces risk but may reduce educational value. Use layered artifacts: a safe public version plus an internal deep-dive for partners under NDA.
• Automation reduces human error (secret scanning, templated redaction), but final legal sign-off is necessary for contractual risk.

This process balances legal/privacy compliance with the company goal of knowledge-sharing while creating a clear, auditable path to publish safe, useful artifacts.