Data masking, also known as data obfuscation, is the process of modifying sensitive data in such a way that it is of no or little value to unauthorized intruders while still being usable by software or authorized personnel. Data masking is used to protect information that is classified as personally identifiable information or mission-critical data, while still allowing valid test cycles to be undertaken. The main reason for masking data is to ensure that it remains usable for its intended purposes while protecting it from unauthorized access. Data masking can be referred to as anonymization or tokenization, depending on the context.

Data masking techniques include deterministic data masking, which involves mapping two sets of data that have the same type of data in such a way that one value is always replaced by another value. Another technique is statistical data obfuscation, which involves substituting original values in a data set with randomized data using various data shuffling and manipulation techniques.

Data masking is different from encryption, as encrypted data can be decrypted and returned to its original state with the correct encryption key, while masked data has no algorithm to recover the original values. Masking generates a characteristically accurate but fictitious version of a data set that has zero value to hackers. It also cannot be reverse-engineered, and statistical outputs cannot be used to identify individuals.

Data masking is used to protect sensitive data in compliance with data privacy regulations like the General Data Protection Regulation (GDPR) . It is commonly used for data that does not change frequently or remains static over time, and pre-defined rules are consistently applied to the data to ensure consistent masking across multiple environments.

Overall, data masking is an important technique for protecting sensitive data while still allowing it to be used for valid purposes.