Framework: assess risks across ingestion, storage, analytics by likelihood (Low/Med/High) and impact (Low/Med/High), prioritize by risk score = likelihood × impact, propose mitigations (technical/process), estimate effort (S/M/L) and approximate cost (low <$5k, med $5–50k, high >$50k), assign owner (Data Eng, Security, Infra, Privacy Officer), and track with tickets, SLAs, and stakeholder reporting.
Top risks (prioritized):
- Unauthorized access to PII in storage
- Likelihood: Medium, Impact: High → Priority: High
- Mitigations:
- Strong IAM + least privilege, role-based access, separation of dev/prod (Effort: M, Cost: Low)
- Data encryption at rest with KMS and key rotation (Effort: M, Cost: Low-Med)
- Regular access reviews & automated anomaly detection (Effort: M-L, Cost: Med)
- Owner: Data Platform Lead / Security
- KPIs: number of users with PII access, access review completion rate
- Insecure ingestion exposing PII in transit or logs
- Likelihood: Medium, Impact: High → Priority: High
- Mitigations:
- TLS for all endpoints, mutual TLS for internal services (Effort: S-M, Cost: Low)
- Masking/redaction at the edge and avoid PII in logs (Effort: M, Cost: Low)
- Schema validation and tokenization for sensitive fields (Effort: M-L, Cost: Med)
- Owner: Data Engineering (ingestion owners)
- KPIs: % of pipelines using TLS, incidents of PII in logs
- PII in analytics/result sets shared improperly
- Likelihood: High, Impact: Medium → Priority: High
- Mitigations:
- Column-level masking, differential privacy or aggregation rules (Effort: M-L, Cost: Med)
- Query-layer access controls, approval workflows for export (Effort: M, Cost: Low)
- Automated scans to detect PII in analytic tables (Effort: M, Cost: Med)
- Owner: Analytics Platform Owner + Privacy Officer
- KPIs: blocked exports, PII detection alerts
- Data retention and deletion failures
- Likelihood: Medium, Impact: Medium → Priority: Medium
- Mitigations:
- Automated lifecycle policies, retention labels, deletion workflows (Effort: M, Cost: Low)
- Periodic audits and reconcilement with business requirements (Effort: S, Cost: Low)
- Owner: Data Governance / Data Eng
- KPIs: % of datasets compliant with retention policy
- Poor data lineage and classification causing missed controls
- Likelihood: High, Impact: Medium → Priority: High
- Mitigations:
- Catalog with automated PII classification (Effort: M, Cost: Med)
- Enforce metadata requirements in CI for pipelines (Effort: S, Cost: Low)
- Owner: Data Engineering + Governance
- KPIs: % of datasets tagged, coverage of lineage
Tracking remediation & reporting:
- Create JIRA/ServiceNow tickets for each mitigation with priority, owner, ETA.
- Use a remediation dashboard showing risk score, status (Open/In progress/Validated/Closed), KPIs and burden (effort/cost).
- Weekly triage meetings with owners; monthly executive report showing top 5 risks, trend, residual risk.
- Validation: run penetration tests and quarterly audits; close tickets only after validation and evidence (logs, scans).
Communicating residual risk:
- For each risk provide: current residual likelihood/impact, mitigations implemented, controls evidence, and business impact statement (quantified where possible: data records at risk, potential regulatory penalties).
- Use RACI to show accountability. For executives, present risk heatmap + mitigation roadmap and ask for explicit acceptance when residual risk remains (include compensating controls and monitoring cadence).
This approach balances engineering fixes, automated controls, governance, and stakeholder transparency so PII is protected while enabling analytics.
