gdpr-compliant

name gdpr-compliant description Apply GDPR-compliant engineering practices across your codebase. Use this skill whenever you are designing APIs, writing data models, building authentication flows, implementing logging, handling user data, writing retention/deletion jobs, designing cloud infrastructure, or reviewing pull requests for privacy compliance. Trigger this skill for any task involving personal data, user accounts, cookies, analytics, emails, audit logs, encryption, pseudonymization, anonymization, data exports, breach response, CI/CD pipelines that process real data, or any question framed as "is this GDPR-compliant?". Inspired by CNIL developer guidance and GDPR Articles 5, 25, 32, 33, 35. GDPR Engineering Skill Actionable GDPR reference for engineers, architects, DevOps, and tech leads. Inspired by CNIL developer guidance and GDPR Articles 5, 25, 32, 33, 35. Golden Rule: Collect less. Store less. Expose less. Retain less. For deep dives, read the reference files in references/ : references/data-rights.md — user rights endpoints, DSR workflow, RoPA references/security.md — encryption, hashing, secrets, anonymization references/operations.md — cloud, CI/CD, incident response, architecture patterns 1. Core GDPR Principles (Article 5) Principle Engineering obligation Lawfulness, fairness, transparency Document legal basis for every processing activity in the RoPA Purpose limitation Data collected for purpose A MUST NOT be reused for purpose B without a new legal basis Data minimization Collect only fields with a documented business need today Accuracy Provide update endpoints; propagate corrections to downstream stores Storage limitation Define TTL at schema design time — never after Integrity & confidentiality Encrypt at rest and in transit; restrict and audit access Accountability Maintain evidence of compliance; RoPA ready for DPA inspection at any time 2. Privacy by Design & by Default MUST Add CreatedAt , RetentionExpiresAt to every table holding personal data at creation time. Default all optional data collection to off . Users opt in; they never opt out of a default-on setting. Conduct a DPIA before building high-risk processing (biometrics, health data, large-scale profiling, systematic monitoring). Update the RoPA with every new feature that introduces a processing activity. Sign a DPA with every sub-processor before data flows to them. MUST NOT Ship a new data collection feature without a documented legal basis. Enable analytics, tracking, or telemetry by default without explicit consent. Store personal data in a system not listed in the RoPA. 3. Data Minimization MUST Map every DTO/model field to a concrete business need. Remove undocumented fields. Use separate DTOs for create, read, and update — never reuse the same object. Return only what the caller is authorized to see — use response projections. Mask sensitive values at the edge: return ****1234 for card numbers, never the full value. Exclude sensitive fields (DOB, national ID, health) from default list/search projections. MUST NOT Log full request/response bodies if they may contain personal data. Include personal data in URL path segments or query parameters (CDN logs, browser history). Collect dateOfBirth , national ID, or health data without an explicit legal basis. 4. Purpose Limitation MUST Document the purpose of every processing activity in code comments and in the RoPA. Obtain a new legal basis or perform a compatibility analysis before reusing data for a secondary purpose. MUST NOT Share personal data collected for service delivery with advertising networks without explicit consent. Use support ticket content to train ML models without a separate legal basis and user notice. 5. Storage Limitation & Retention MUST Every table holding personal data MUST have a defined retention period. Enforce retention automatically via a scheduled job (Hangfire, cron) — never a manual process. Anonymize or delete data when retention expires — never leave expired data silently in production. Recommended defaults Data type Max retention Auth / audit logs 12–24 months Session / refresh tokens 30–90 days Email / notification logs 6 months Inactive user accounts 12 months after last login → notify → delete Payment records As required by tax law (7–10 years), minimized Analytics events 13 months SHOULD Add RetentionExpiresAt column — compute at insert time. Use soft-delete ( DeletedAt ) with a scheduled hard-delete after the erasure request window (30 days). MUST NOT Retain personal data indefinitely "in case it becomes useful later." 6. API Design Rules MUST MUST NOT include personal data in URL paths or query parameters. GET /users/{userId} Authenticate all endpoints that return or accept personal data. Extract the acting user's identity from the JWT — never from the request body. Validate ownership on every resource: if (resource.OwnerId != currentUserId) return 403 . Use UUIDs or opaque identifiers — never sequential integers as public resource IDs. SHOULD Rate-limit sensitive endpoints (login, data export, password reset). Set Referrer-Policy: no-referrer and an explicit CORS allowlist. MUST NOT Return stack traces, internal paths, or database errors in API responses. Use Access-Control-Allow-Origin: * on authenticated APIs. 7. Logging Rules MUST Anonymize IPs in application logs — mask last octet (IPv4) or last 80 bits (IPv6). 192.168.1.xxx MUST NOT log: passwords, tokens, session IDs, credentials, card numbers, national IDs, health data. MUST NOT log full request/response bodies where PII may be present. Enforce log retention — purge automatically after the defined period. SHOULD Log events not data: "User {UserId} updated email" not "Email changed from a@b.com to c@d.com" . Use structured logging (JSON) with userId as an internal identifier, not the email address. Separate audit logs (sensitive access, admin actions) from application logs — different retention and ACLs. 8. Error Handling MUST Return generic error messages — never expose stack traces, internal paths, or DB errors. "Column 'email' violates unique constraint on table 'users'" "A user with this email address already exists." Use Problem Details (RFC 7807) for all error responses. Log the full error server-side with a correlation ID; return only the correlation ID to the client. MUST NOT Include file paths, class names, or line numbers in error responses. Include personal data in error messages (e.g., "User john@example.com not found"). 9. Encryption (summary — see references/security.md for full detail) Scope Minimum standard Standard personal data AES-256 disk/volume encryption Sensitive data (health, financial, biometric) AES-256 column-level + envelope encryption via KMS In transit TLS 1.2+ (prefer 1.3); HSTS enforced Keys HSM-backed KMS; rotate DEKs annually MUST NOT allow TLS 1.0/1.1, null cipher suites, or hardcoded encryption keys. 10. Password Hashing MUST Use Argon2id (recommended) or bcrypt (cost ≥ 12). Never MD5, SHA-1, or SHA-256. Use a unique salt per password. Store only the hash. MUST NOT Log passwords in any form. Transmit passwords in URLs. Store reset tokens in plaintext. 11. Secrets Management MUST Store all secrets in a KMS: Azure Key Vault, AWS Secrets Manager, GCP Secret Manager, or HashiCorp Vault. Use pre-commit hooks ( gitleaks , detect-secrets ) to prevent secret commits. Rotate secrets on developer offboarding, annual schedule, or suspected compromise. .gitignore MUST include: .env , .env.* , *.pem , *.key , *.pfx , *.p12 , secrets/ MUST NOT Commit secrets to source code. Store secrets as plain-text environment variable defaults. 12. Anonymization & Pseudonymization (summary — see references/security.md ) Anonymization = irreversible → falls outside GDPR scope. Use for retained records after erasure. Pseudonymization = reversible with a key → still personal data, reduced risk. When erasing a user, anonymize records that must be retained (financial, audit) rather than deleting them. Store the pseudonymization key in the KMS — never in the same database as the pseudonymized data. MUST NOT call data "anonymized" if re-identification is possible through linkage attacks. 13. Testing with Fake Data MUST MUST NOT use production personal data in dev, staging, or CI environments. MUST NOT restore production DB backups to non-production without scrubbing PII first. Use synthetic data generators: Bogus (.NET), Faker (JS/Python/Ruby). Use @example.com for all test email addresses. 14. Anti-Patterns Anti-pattern Correct approach PII in URLs Opaque UUIDs as public identifiers Logging full request bodies Log structured event metadata only "Keep forever" schema TTL defined at design time Production data in dev/test Synthetic data + scrubbing pipeline Shared credentials across teams Individual accounts + RBAC Hardcoded secrets KMS + secret manager Access-Control-Allow-Origin: * on auth APIs Explicit CORS allowlist Storing consent with profile data Dedicated consent store PII in GET query params POST body or authenticated session Sequential integer IDs in public URLs UUIDs "Anonymized" data with quasi-identifiers Apply k-anonymity, test linkage resistance Mixing backup regions outside EEA Explicit region lockdown on backup jobs 15. PR Review Checklist Data model Every new PII column has a documented purpose and retention period. Sensitive fields (health, financial, national ID) use column-level encryption. No sequential integer PKs as public-facing identifiers. API No PII in URL paths or query parameters. All endpoints returning personal data are authenticated. Ownership checks present — user cannot access another user's resource. Rate limiting applied to sensitive endpoints. Logging No passwords, tokens, or credentials logged. IPs anonymized (last octet masked). No full request/response bodies logged where PII may be present. Infrastructure No public storage buckets or public-IP databases. New cloud resources tagged with DataClassification . Encryption at rest enabled for new storage resources. New geographic regions for data storage are EEA-compliant or covered by SCCs. Secrets & CI/CD No secrets in source code or committed config files. New secrets added to KMS and secrets inventory document. CI/CD secrets masked in pipeline logs. Retention & erasure Retention enforcement job or policy covers new data store or field. Erasure pipeline updated to cover new data store. User rights & governance Data export endpoint includes any new personal data field. RoPA updated if a new processing activity is introduced. New sub-processors have a signed DPA and a RoPA entry. DPIA triggered if the change involves high-risk processing. Golden Rule: Collect less. Store less. Expose less. Retain less. Every byte of personal data you do not collect is a byte you cannot lose, cannot breach, and cannot be held liable for. Inspired by CNIL developer GDPR guidance, GDPR Articles 5, 25, 32, 33, 35, ENISA, OWASP, and NIST engineering best practices.