Title: Remember the Importance of Non-Production Data in Perforce Compliance
Diving into the world of software development, we often focus on live production systems, treating them like an assembly line that churns out data-driven products. Yet, let's not overlook the importance of non-production data. This unloved bunch of info includes testing and prototyping datasets, unstructured data from data lakes, and data from sunsetting applications.
From a governance and compliance perspective, these 'forgotten' piles of data still hold value. The challenge lies in managing them effectively, preventing potential data breaches in the process.
David Wells, head of product for data compliance at enterprise DevOps solutions company Perforce, shares some insights on this issue. He warns that non-production data can sprawl across various environments, often with little control or visibility. This can lead to risks such as data breaches.
To address this problem, some businesses turn to synthetic data, which is randomly generated. However, it still maintains the aggregate patterns of the original dataset. This means a malicious attack can still be mounted by successfully reidentifying the source data, especially if it's publicly available.
Developers working with non-production data face similar challenges. They often need to maintain linkages between similar data, but replacing certain details can cause issues in software validation functions. For example, altering a zip code or vehicle identification number can cause unexpected problems.
As data patterns matter to testers, due care and consideration are still required when non-production data becomes languishing. Additionally, the scale of data makes managing it challenging. Test data needs to be transformed or generated consistently to maintain referential integrity. However, when larger apps are involved, it becomes increasingly expensive, complex, and time-consuming.
To tackle this issue, Wells suggests taking a more proactive approach to protecting data in non-production environments. Techniques like static data masking can automatically discover sensitive data and replace it with fictitious, production-like data, maintaining referential integrity across development, testing, and analytics teams.
In summary, non-production data can pose significant challenges when it comes to security and compliance issues. By taking a proactive approach, implementing the right tools, and ensuring data quality, businesses can effectively manage and protect their non-production data, preventing potential data breaches and maintaining data usability for testing and analytics.
Developers working with Perforce's enterprise DevOps solutions might encounter issues while managing non-production data's linkages, as altering specific details can disrupt software validation functions. To mitigate this, Wells recommends employing techniques like static data masking, which automatically discovers and replaces sensitive data with fictitious data, preserving referential integrity across all teams.
Perforce's David Wells emphasizes that non-production data can harbor potential data breaches for developers, given its sprawl across various environments with minimal control or visibility. Implementing a proactive approach, employing the right tools, and maintaining data quality can effectively manage and protect this data, ensuring data breach prevention and preserving usability for testing and analytics.
