Data Masking: Testing with Near-real Data

Organizations worldwide collect data about customers, users, products, and services. Striving to get the most out of collected data, they use it to fuel many day-to-day processes including software testing, development, and personnel training. The majority of this collected data is sensitive and falls under specific government regulations or industry standards that define policies for privacy and generally limit or prohibit using the data for these secondary purposes. Data masking solves this problem. It replaces sensitive information with data that looks real and is structurally similar to the actual information but is useless to anyone trying to obtain the real data. Learn about the process, pros and cons of static and dynamic data masking architectures, subsetting, randomization, generalization, shuffling, and other basic techniques used to set up data masking. Discover how to start data masking and learn about common challenges on data masking projects.