Data anonymization in non-production environments is becoming an increasingly important issue in the context of personal data protection and meeting regulatory requirements. The process not only prevents unauthorized access to sensitive data, but also protects the company’s image and minimizes the risk of breaches. Below, we present an overview of the key elements of data anonymization process to help you prepare your business for this challenge.
When and for what purpose should personal data be anonymised?
Traditional testing based on artificially generated data may not provide sufficiently consistent and reliable information about the tested solution, which is why increasing number of companies are choosing to use a copy of the real-world production environment. In such cases the testers must remember that such data may include also personal data of customers, prone to certain risks. When anonymized, however, the data is no longer at risk and can be used as test data.
Study the test data generation methods on the examples of available tools
One of the key tasks during the test design stage is development of test cases based on the acceptance criteria defined in the documentation. In order to cover all developed cases in a correct way, we often need specific resources, including relevant test data.
Various types and characteristics of non-production environments
In today’s dynamic world of software technologies, both development and maintenance of the high-quality applications and systems is vital for business. To achieve this, it is necessary to provide various types of non-production environments to support the developers’, testers’ and other specialists’ work on the applications in secure and well-controlled conditions.