Data loss prevention software

Data loss prevention (DLP) is a set of strategies and technologies that prevent the unauthorized transmission or disclosure of sensitive data in an information system, including data in motion (across networks), at rest (in storage), or in use (on endpoints). This concept is part of information privacy, data security and data governance. DLP systems have traditionally relied upon a variety of classification and enforcement mechanisms to reduce the risk of data loss but increasingly incorporate machine learning and behavioral analytics to enhance detection accuracy. DLP is used in on-premises systems, cloud applications, and hybrid environments.

Data loss incidents (unauthorized disclosure or deletion of sensitive data) may turn into data leak incidents (data breaches) when media containing sensitive information are lost and then acquired by an unauthorized party, including via data theft. However, a data leak is possible without losing the data on the originating side. There are limitations to the effectiveness of DLP systems in reducing risk of data loss.

Other terms associated with data leakage prevention include information leak detection and prevention (ILDP), information leak prevention (ILP), content monitoring and filtering (CMF), information protection and control (IPC), and extrusion prevention system (EPS), as opposed to an intrusion prevention system.