Certified Secure Software Lifecycle Professional Practice Exam 2025 - Free Practice Questions and Study Guide

Data masking is the concept that involves creating an inauthentic version of data while preserving its original structure. This technique is often used in environments where sensitive information needs to be protected, such as when data is shared for testing, analytics, or training purposes. The primary goal of data masking is to protect sensitive information while allowing users to work with data that retains the same format and structure as the original.

For instance, if a database contains employee names and social security numbers, data masking could replace the actual names with fictitious ones and alter the social security numbers to random digits. However, the overall format—such as the number of characters or digits—remains unchanged, which is essential for applications that expect data in a certain layout. This way, analysts or developers can perform their tasks without accessing sensitive information.

The other concepts mentioned do not fit this definition. Data encryption involves transforming data into a format that is unreadable to unauthorized users, focusing primarily on securing data rather than maintaining its structural integrity in a usable way. Data compression reduces the size of the data for storage or transmission but doesn't create an inauthentic version. Data normalization organizes data to reduce redundancy and improve data integrity, but it does not involve creating altered versions of data.