Prod data in dev

Using production data in pre-production or development environments is a common practice in data projects. This approach helps simulate real conditions and improve the quality of testing and development. Teams can assess application performance, making it easier to adjust and optimize before deployment. The main advantage is that it enhances development reliability by quickly identifying bugs and enabling data-driven decisions.

However, this technique raises serious security and isolation concerns. If non-production environments are not properly isolated from the production network, there is a risk of compromising production data. Additionally, direct access to sensitive data without proper protection violates regulations such as GDPR, and standards like ISO 27001 or SOC2. To ensure compliance, it is crucial to protect data confidentiality through anonymization techniques.

These practices are particularly complex and require balancing risks with an appropriate level of security, depending on the project’s specific needs. A common alternative is using synthetic data, which eliminates security and isolation concerns by avoiding access to sensitive production data. However, this approach has limitations in terms of representing real-world scenarios and can be time-consuming to implement, reducing overall effectiveness.

Theodo’s point of view

At Theodo, we firmly believe that sensitive production data should not be used in development environments. Given the lack of ideal anonymization tools, we are successfully experimenting with synthetic data generation models (GenAI), allowing us to iterate quickly while maintaining a high level of security and data isolation.