Gartner defines data loss prevention (DLP) as a technical control designed to prevent data loss in order to comply with personal data regulations, prevent unintended disclosure, minimize insider risk and ensure that sensitive data is not overly accessible. DLP controls are typically applied to reduce the data risk for two states of unstructured data: data at rest and data in motion. Depending on the state of the data, DLP applies detective, preventive or corrective controls, including alerting, quarantining, blocking, redaction or access restriction.
Data masking is based on the premise that sensitive data can be transformed into less sensitive but still useful data. This is necessary to satisfy application testing use cases that require representative and coherent data, as well as analytics that involve the use of aggregate data for scoring, model building and statistical reporting. The market for data protection, DM included, continues to evolve with technologies designed to redact, anonymize, pseudonymize, or in some way deidentify data in order to protect it against confidentiality or privacy risk.