Should we mask PII in raw ingestion tables?

Usually no. Lock raw down hard (platform + ETL only), then expose masked/pseudonymized fields via curated models/views. Masking raw tends to break reprocessing, backfills, and forensic work—while still failing audits if access control is sloppy.

Is tokenization required for GDPR?

GDPR doesn’t mandate a specific technique; it requires appropriate technical measures. In analytics, tokenization/pseudonymization is often the best tradeoff: it reduces exposure while keeping joins stable and metrics reliable.

What’s the fastest path to measurable compliance improvements?

Pick one domain (customers), tag sensitive columns, lock down raw, ship masked curated views, centralize audit logs, and add drift detection in CI. You’ll get immediate reduction in PII exposure and a defensible story for auditors.

How do we keep privacy controls from slowing down analysts?

Standard roles + governed curated views. Analysts shouldn’t file tickets to do normal analysis. Reserve elevated access for a small set of break-glass workflows with time-bound approvals and full auditing.

Data-engineering · Jan 15, 2026 · 9 minute read

Your Data Lake Isn’t “Private” — It’s Just Uninspected: Privacy Controls That Survive GDPR Audits and Monday Mornings

Real-world privacy controls for analytics platforms: classification, least-privilege access, masking, retention, and auditability—implemented in ways that improve data reliability and business delivery instead of slowing teams down.

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Your Data Lake Isn’t “Private” — It’s Just Uninspected: Privacy Controls That Survive GDPR Audits and Monday Mornings

Key takeaways

Implementation checklist