Introduction
The strategy of modern software development focuses on delivering high-quality products as quickly and efficiently as possible. Achieving this goal requires a well-structured development process supported by high-quality data. Often, data must be shared with third-party vendors, such as outsourcing companies. Many organizations maintain separate staging environments for testing, development, and pre-production. However, the closer these environments mirror production, the greater the risk of data breaches due to the increased sensitivity of the data involved.
To generate high-quality data for testing and development while minimizing the risk of data breaches, organizations often use anonymized data or synthetic data generation. Anonymized data is a transformed version of the original data that retains its usability for testing, development, and analysis. Synthetic data, on the other hand, is generated independently of the original records and is often used for AI training and other purposes.
A real-world examples
Let’s explore some real-world examples where anonymized and synthetic data prove to be both useful and beneficial:
Outsourcing service
One of the critical challenges in the software development industry is having an ability to deliver high-quality products on time. This is especially true for companies that outsource their software development projects. When outsourcing, companies often face the challenge of sharing sensitive data with third-party vendors. To mitigate the risk of data breaches the companies often deploy numerous barriers to control the actions of outsourcers. Jump hosts are one of the most common barriers used to control access to sensitive data. However, this approach can be cumbersome and time-consuming, leading to delays in project delivery.
To address this challenge, companies trying to optimize their development approach often by organizing a staging environment that fits all regulatory requirements. Having a staging environment that closely resembles the production environment allows the development team to work with anonymized data, reducing the risk of data breaches while maintaining operational efficiency. This approach enables companies to streamline their development process, improve project delivery times, and enhance the overall quality of their products.
The benefits of using prepared staging environment are:
- Optimized Task Allocation: Minimize the dependency on client-authorized personnel for specific tasks, enabling a more flexible and efficient team structure.
- Lower Resource Reservation: Reduce the need to reserve authorized personnel by allowing non-authorized team members to handle appropriate tasks.
- Reduced Rework: Decrease the likelihood of rework by enabling testing on data that closely resembles real-world scenarios.
- Efficient Scaling: Unlock resources through tools like Greenmask, supporting project scaling without requiring additional financial investments.
This approach can be beneficial for both outsourcing companies and the organizations that use their services. For outsourcing providers, it enables smoother collaboration with clients and reduces delays caused by restricted access. For companies leveraging outsourcing services, it minimizes risks, ensures secure data handling, and enhances the efficiency of outsourced projects.
How can we organize a staging environment that closely resembles production while minimizing the risk of data breaches? The answer lies in anonymizing sensitive data or synthetic data generation.
Fintech company
FinTech companies often face a unique challenge when addressing fraud detection using production or production-like data. For instance, analysts may need to identify patterns within the data but are restricted from accessing Personally Identifiable Information (PII) while still fulfilling their responsibilities.
When working with real, non-anonymized, and uncontrolled data, the time required for approvals and access increases significantly. Direct access to such data almost always requires frequent approvals and carries the risk of data breaches.
Another applicable case is when insufficient or incomplete test data during the development and debugging stages fails to reveal bugs, which can potentially lead to misuse. In such situations, the organization of a staging environment becomes essential.
In such scenarios, anonymized data becomes a viable solution. By applying transformations to the data, it can be securely shared with employees to complete their tasks without compromising sensitive information. A tool that automates and facilitates this transformation process can significantly enhance efficiency and security, and Greenmask effectively addresses these challenges.
Conclusion
There are numerous examples where anonymized and synthetic data prove invaluable for organizations. Establishing a well-organized staging environment, coupled with the right tools to support the entire software development lifecycle, is critical for safeguarding sensitive data while maintaining development efficiency. Virtually every aspect of the software development industry can benefit from properly structured staging environments and improved data accessibility.