Clarify requirements & constraints
- Support GDPR + CCPA/CPRA types (access, portability, deletion, opt-out), multi-tenant, low-latency intake, auditable immutable logs, legal hold, RBAC, retention windows, SLA targets (e.g., 30 days), integration with many data stores (RDBMS, object stores, SaaS, logs).
High-level architecture
- Intake API + Portal (customer identity, consent proof) -> Orchestration Engine -> Discovery Services -> Action Workers -> Verification Service -> Denial / Escalation UI -> Audit & Reporting Store.
Core components
- Intake Gateway: TLS, WAF, rate limiting, captcha, adaptive MFA for high-risk requests.
- Identity Verification Service: KBA, OAuth/OIDC linkage, document verification, risk scoring (confidence threshold configurable).
- Discovery Engine: federated connectors + central metadata/index (PII catalog). Two-step pattern: fast index lookup (NLP, structured PII regex, fingerprinting) then targeted datastore queries for confirmation.
- Orchestration Engine: state machine (e.g., Temporal) to run workflows, handle retries, compensating actions, legal holds.
- Action Workers: per-datastore microservices to carry out deletion/export with safe-mode dry-run option.
- Denial Workflow: policy engine (attributes, retention holds, legal exceptions) + human review queue.
- Audit Trail: immutable, append-only ledger (WORM storage + signing), searchable (ELK or data warehouse), exportable for regulators.
Scalability & reliability
- Event-driven: Kafka/NSQ for discovery/action tasks; autoscaling workers; shard metadata store by tenant; use incremental scanning; cache search fingerprints in Redis.
- Bulk ops via batch jobs with rate control; circuit breakers to protect source systems.
- Use async UX: user gets secure tracking link, status updates, and estimated completion windows.
Minimizing false positives/negatives
- Multi-layer detection: combine deterministic rules (PII templates, hashed identifiers) + ML models (entity extraction, fuzzy match) + contextual signals (recent activity, auth ties).
- Confidence scoring: tiered actions — high-confidence auto-delete, medium-confidence require manual review, low-confidence ignore but log.
- Continuous feedback loop: human review outcomes feed model retraining; sampling & audits to measure precision/recall metrics.
Security & compliance
- Data in transit & at rest encrypted (KMIP/HSM for keys). Least privilege IAM for workers. Soft-delete + retention lockers for legal hold. Detailed RBAC + MFA for reviewers. Regular penetration tests and privacy impact assessments.
Trade-offs
- Full-text indexing improves speed but increases storage & attack surface—mitigate with encryption and access controls. Conservative thresholds reduce false deletions but increase manual work.
This design balances automation, auditability, and safety while scaling to enterprise volumes and regulatory needs.
