Data Security: Scenario-Based Questions

Sensitive data like PII, financial records, or medical info must be handled with care. Leaks or misuse can lead to legal penalties and reputational harm. A robust data protection strategy includes both technical and procedural controls.

🔐 Data Classification & Risk Assessment

• Classify data as public, internal, confidential, or restricted.
• Identify sensitive fields (e.g., SSN, credit card, health info).
• Apply stricter controls to high-risk datasets and flows.

🧊 Encryption at Rest & In Transit

• At Rest: Use AES-256 via KMS/CloudHSM for DBs, object stores, backups.
• In Transit: Enforce TLS 1.2+ across APIs, interservice calls, UIs.
• Key Rotation: Schedule key rotation policies (monthly/yearly).

🫥 Masking & Redaction

• Mask data in logs, dashboards, and staging environments.
• Apply tokenization or format-preserving encryption (FPE) for analytics access.
• Use role-based masking — e.g., partial vs full SSN based on user role.

✅ Auditing & Monitoring

• Track access to sensitive tables or blobs — log and alert on anomalies.
• Use tools like AWS Macie, GCP DLP to scan for sensitive data exposure.
• Maintain audit trails of who accessed or modified sensitive info.

📄 Compliance & Legal Considerations

• Follow frameworks: GDPR, HIPAA, SOC 2, PCI-DSS.
• Allow user-initiated deletions (right to be forgotten).
• Use data retention policies aligned with legal requirements.

🚫 Common Pitfalls

• Logging raw sensitive data (e.g., request payloads, query params).
• Over-permissive access to production DBs or S3 buckets.
• Encrypting data but leaving keys exposed in source code.

📌 Final Insight

Data protection is a shared responsibility — combining application design, infrastructure controls, and policy enforcement. Handling sensitive data well builds customer trust and regulatory resilience.