Repost: Easily Anonymize Production Data in Postgres

A Repost is Sharing a high-quality content, written by people I appreciate.

In Neon.tech blog I read a blog post Easily Anonymize Production Data in Postgres.

Use Neon's Postgres database branching and PostgreSQL Anonymizer to develop in production-like environments with masked PII.

TLDR

• Neon's database branching allows you to create copies of your production database.

• The PostgreSQL Anonymizer extension helps you mask data in your Postgres database.

• You can choose between static masking (permanently replaces data, a destructive step) and dynamic masking (masks data on the fly).

• Neon's branching capabilities with PostgreSQL Anonymizer allows you to create anonymized development and preview environments.

My Take

Testing with production-like data sounds fantastic, right? It's a goldmine for uncovering performance issues hiding in development environments, like sluggish queries on massive tables or time-consuming index creation. But the implementation is not that simple.

While Neon's approach to anonymize data for development and testing is a great step in the right direction, implementing it for real-world PII data gets tricky. Regulations make it difficult to export PII using dynamic masking. If security labels are removed, the sensitive data becomes exposed again

Granting developers access to a copy of the production database in the production environment creates a security risk. Even with anonymization techniques, a copy of the production database might still contain sensitive information depending on the level of anonymization used.

Tools like Tonic.ai or neosync.dev generate secure and scalable synthetic data that mimics the structure and characteristics of your real production data. This allows developers to test with realistic datasets without compromising any sensitive information.