Asking different people within software organizations or even different people within the security space to define “data masking” often means you will get different – and sometimes complex – answers. There are so many terms, pseudonyms, and technologies related to data masking that we wanted to get back to the basics. In this article, I’ll discuss the fundamental concepts of data masking, explain various masking techniques, delve into the difference between static data masking and dynamic data masking, and talk about Baffle’s Data Masking services.
What is Data Masking
In data security, data masking is generally used as the catch-all term for the process of altering data to protect sensitive information. It can also be known as data obfuscation, data de-identification, data anonymization, and data sanitization. Additionally the General Data Protection Regulation (GDPR) introduced the term “pseudoanonymization” that covers a range of processes to protect sensitive data.
The goal of data masking is to allow sensitive data to be used while ensuring it is kept secure. To meet this goal, sensitive data must be masked using a method that ensures there is no way to reverse engineer the process and gain access to the original data.
Data Masking Techniques
There are many different ways to mask sensitive data, and there are pros and cons to each type of masking. To make the decision on how to mask your data, it’s important to understand the various techniques, how you want to use the data after it’s masked, and who will have access to the data.