Which method offers an additional layer of security by allowing alternate presentation of data while keeping the original values intact?

Data masking effectively adds an extra layer of security by transforming sensitive data into a format that is not easily recognizable while preserving its original structure. The primary goal of data masking is to protect sensitive information during processes such as testing and development, where real data should not be exposed.

In this method, the actual data values are altered, yet the modified data can still be used in situations that require a similar format or structure. For example, if a database requires names and social security numbers for testing purposes, data masking can substitute fake names and numbers in place of actual sensitive data, ensuring that the original data remains untouched and secure. This allows organizations to maintain privacy and comply with regulations while still being able to perform necessary operations that involve data.

Other methods, while useful in their own contexts—such as encryption, which secures data by converting it into a coded format requiring a key to access, and tokenization, which replaces sensitive data elements with non-sensitive equivalents—do not keep the original value intact in the same manner as data masking does. Anonymization also removes the ability to identify the original data entirely, which differs from the approach taken by data masking, where the data remains identifiable in a controlled way.