Do I need a new vendor to pass audit?

Usually no. Start with the platform-native controls you already pay for: Snowflake masking policies and tags, BigQuery policy tags, Lake Formation/Ranger for lakes. Add OPA for policy-as-code and OpenLineage for evidence. Vendors like Immuta/Privacera help at scale, but you can ship an MVP without them.

Will masking and ABAC hurt performance?

In practice, native policies add <3% overhead on typical analytics workloads. The cost of not enforcing privacy—unlimited copies, accidental exposure, audit delays—hits both cloud spend and delivery speed harder.

How do we handle the right to be forgotten (DSR) in derived tables?

Use idempotent deletion pipelines that replay deletes through lineage. For warehouses, rebuild or backfill affected materializations (dbt `--full-refresh` for impacted models). For lakes, propagate tombstones and compact. Capture artifacts proving which rows and datasets were touched.

What about LLMs and AI features?

Redact or tokenize PII before calling LLM APIs. Keep reversible tokens in a separate vault with strict purpose-based access. Log prompts and outputs, and disallow raw PII in prompts by policy. Treat AI integrations as another sink in your lineage graph.

Who owns privacy policies: security, data, or legal?

Security and legal define the rules; the data platform enforces them. Put the rules in code (OPA/Rego) and require Git-based approvals from DPO/security for exceptions. That keeps accountability clear and audits simple.

Data-engineering · Oct 11, 2025 · 9 minute read

Privacy That Ships: Data Controls Regulators Sign Off On (And Your Pipelines Don’t Hate)

Q: How do we handle the right to be forgotten (DSR) in derived tables?

Use idempotent deletion pipelines that replay deletes through lineage. For warehouses, rebuild or backfill affected materializations (dbt `--full-refresh` for impacted models). For lakes, propagate tombstones and compact. Capture artifacts proving which rows and datasets were touched.

Q: What about LLMs and AI features?

Redact or tokenize PII before calling LLM APIs. Keep reversible tokens in a separate vault with strict purpose-based access. Log prompts and outputs, and disallow raw PII in prompts by policy. Treat AI integrations as another sink in your lineage graph.

Q: Who owns privacy policies: security, data, or legal?

Security and legal define the rules; the data platform enforces them. Put the rules in code (OPA/Rego) and require Git-based approvals from DPO/security for exceptions. That keeps accountability clear and audits simple.

Build privacy-by-design into your data platform so audits pass, engineers move fast, and the business actually gets value.

Alex Mercer

Principal Data & Platform Engineer, GitPlumbers

20 years building and stabilizing data platforms at scale (AWS, GCP, Snowflake, Kafka, dbt). Ex-Stripe, ex-Target. I fix the scary parts so teams can ship safely.

Privacy that works looks like SRE: declarative, enforced by the platform, and leaving an evidence trail you don’t have to scramble to rebuild.

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Privacy That Ships: Data Controls Regulators Sign Off On (And Your Pipelines Don’t Hate)

Key takeaways

Implementation checklist