For decades, businesses have been protecting data privacy to build customer trust. Fast forward to today, it is no more a choice but a regulatory mandate that can attract stringent action in case of no compliance. With regulations like GDPR and CCPA, enterprises must take extra measures to protect data privacy in their test environments.
As more companies rely on technology to power their businesses, the importance of test data management has become increasingly crucial.
In this post, I share best practices and critical techniques to ensure privacy in test data management, including best practices for data masking, data subsetting, and data scrambling. By implementing these techniques, companies can minimize the risk of data breaches and maintain customer trust.
Test Data Management (TDM) manages the data used for testing software applications. This includes identifying, creating, and managing test data sets that are representative of the production environment.
However, as test data management involves working with often sensitive or confidential data, ensuring privacy in TDM is critical to protect the information. This has to be achieved while still providing developers with the necessary data to test their applications.
To ensure relevant application testing covering various use cases, many companies use real production data, which typically includes sensitive personal information.
Data privacy laws such as GDPR, CCPA, and KVKK are there to protect citizen data, even when used internally, and prohibit its misuse. However, outsourcing testing, Quality Assurance, and training processes to third parties in different countries may expose citizen data to unauthorized users, violating data privacy laws.
Businesses must identify, classify, and protect every data element according to user population and policies to enable data privacy. All environments must be considered when handling data.
A data breach can affect a customer’s data in any environment, whether a production database, an archive, or a test database. Such incidents can result in non-compliance fines and penalties for the business.
It’s essential to have the ability to demonstrate that data is non-reversible and to respond to privacy auditors’ inquiries by verifying that test and analytics data values are genuinely anonymized and irreversible.
