Data Classification and Sensitivity Handling Questions

Classifying data by sensitivity and applying controls proportionate to that classification: identifying personal, sensitive, and special-category data and tagging it through its lifecycle. Covers classification schemes, labeling, and how classification drives access, encryption, and retention decisions. Includes assessing the impact of a given data type on privacy and security risk.

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Describe a data governance policy to minimize PII exposure for ML teams. Explain how to implement least-privilege access controls, masking or tokenization, and an audit trail for dataset access while maintaining analyst productivity.

Sample Answer

Situation: At my last role we needed ML features that used customer data while reducing PII exposure and meeting compliance.

Policy overview: Define a data governance policy that classifies data (PII, sensitive, non-sensitive), enforces purpose-based access, and mandates deidentification/tokens and full auditing before any dataset leaves the secure environment.

Implementation — least-privilege access:- Role-based + attribute-based access: create coarse roles (researcher, engineer, analyst) and ABAC rules (project, dataset sensitivity, time-limited access).- Use short-lived credentials (IAM sessions) and require just-in-time approvals for sensitive datasets.- Example tech: AWS IAM + Lake Formation or GCP IAM + Data Catalog to grant table-/column-level permissions.

Masking / tokenization:- Use field-level deterministic tokenization for keys (preserves joins) and irreversible hashing/salting for true PII where joins aren’t needed.- Apply format-preserving encryption when downstream systems require realistic formats.- Implement transformation pipelines in a secure ETL (e.g., Glue, Dataproc) that produce de-identified feature tables labeled with lineage metadata.

Audit trail & monitoring:- Log dataset access at row/column level, record user, purpose, query, and time using centralized audit logs (CloudTrail + Lake Formation/GCP Access Transparency).- Automate alerts for unusual access patterns and require quarterly access reviews.

Maintaining analyst productivity:- Provide sanitized feature stores and synthetic datasets for exploration; offer self-service deid pipelines and a tokenization library so analysts can work without hitting approvals for every experiment.- Document approved data patterns and provide templates, notebooks, and a sandbox with pre-masked samples.

Result: This approach enforces least privilege, reduces PII exposure via tokenization, and preserves productivity by giving analysts safe, self-service data while retaining full auditability and compliance.

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