Situation & goal: Build a scalable product + engineering program enabling GDPR/CCPA data-subject-requests (DSRs): subject access (SAR), right to be forgotten (deletion), consent management, portability, and legally-required record-keeping — minimizing risk and operational cost while preserving user trust.
Product plan (what, why, priority):
- Core features (MVP, 8–12 weeks): DSR intake portal (self-serve + admin), authenticated SAR/Deletion requests, export/portability bundles, consent management UI, audit log & retention policy. Priority = compliance + low-touch automation.
- Next (12–24 weeks): Granular dataset mapping, automated downstream purge connectors, workflow SLA dashboard, reporting for legal/ops.
Engineering design (how):
- Data mapping: Create a Privacy Data Catalog linking data subjects → data stores, schemas, retention. Store metadata in a canonical Privacy Graph DB.
- Request lifecycle service: central microservice handling requests, state machine (Received, Verified, In Progress, Completed, Denied), idempotency keys, retry.
- Connectors: pluggable adapters for services (RDBMS, S3, analytics, third-party APIs) to collect, delete, or redact data.
- Consent store: immutable ledger (append-only) storing consent events (who, when, scope, source). Expose SDKs for frontend/backends.
- Audit trail: tamper-evident audit log (write-ahead log + hash chaining + retained per legal retention) with export for regulators.
API endpoints (examples):
- POST /dsr/requests — submit request (type, subject id, proof)
- GET /dsr/requests/{id} — status + timeline
- POST /dsr/requests/{id}/verify — attach verification evidence
- POST /dsr/requests/{id}/action — run export/delete/redact
- GET /consent/{subjectId} — fetch consents
- POST /consent/{subjectId} — record consent change
- GET /audit/requests/{id} — immutable audit record (admin, read-only)
Admin UX & ops:
- Admin console: queue with SLA indicators, request detail (affected systems, recommended actions), one-click “run connectors” with dry-run option, manual override, canned responses, escalation flows.
- Verification workflow: integrated identity verification options (email OTP, OAuth, KBA) configurable by risk level.
- Reporting: metrics (time-to-verify, time-to-complete, volume by type), compliance SLA dashboard, exportable compliance packages.
Security & privacy controls:
- Least privilege access, field-level encryption, key management, MFA for admins.
- Rate-limiting, anti-abuse checks, fraud detection for forged requests.
Audit, retention & legal:
- Keep full immutable audit trail for each DSR: timestamps, actor, actions, outputs (hash of exported dataset), legal reason if denied — retention policy configurable (meet GDPR/CCPA minimums).
- Data portability output: structured JSON/CSV with schema, hashed file for integrity, secure expiring download link.
- Deletion records: store deletion proof (hash of deleted record snapshot + connector logs) without retaining subject personal data beyond legally required metadata.
Internal processes:
- Intake triage: automated verification -> auto-complete low-risk SAR/export/delete -> manual review for complex requests (e.g., conflicting rights, legal holds).
- Escalation to legal for holds, conflicting requests, or data retention exceptions.
- SLAs: configure per regulation (e.g., GDPR 30 days) with escalation thresholds and audit alerts.
- Training: quarterly compliance training for support & ops; playbooks for common request types.
- Post-incident: blameless reviews and update connectors/tests.
Testing & monitoring:
- End-to-end tests for each connector, dry-run deletion simulations, synthetic DSR load testing.
- Monitoring: alerts on increasing denial rates or missed SLAs, connector failures, suspicious request patterns.
Success metrics:
- Compliance coverage (% of systems mapped)
- Automated completion rate (% DSRs auto-resolved)
- Median time-to-complete vs SLA
- Number of manual interventions per 100 DSRs
Trade-offs & risks:
- Full automation vs false deletions — mitigate with verification, dry-runs, and legal holds.
- Breadth-first mapping vs depth — start with high-risk/high-volume systems.
This plan balances product usability, engineering safety, and legal rigor to operationalize GDPR/CCPA responsibilities while minimizing ongoing manual work.
