Data Leak Prevention

Data loss prevention software detects potential data breaches/data ex-filtration transmissions and prevents them by monitoring, detecting and blocking sensitive data while in-use (endpoint actions), in-motion (network traffic), and at-rest (data storage). In data leakage incidents, sensitive data is disclosed to unauthorized parties by either malicious intent or an inadvertent mistake. Sensitive data includes private or company information, intellectual property (IP), financial or patient information, credit-card data and other information.

The terms "data loss" and "data leak" are related and are often used interchangeably. Data loss incidents turn into data leak incidents in cases where media containing sensitive information is lost and subsequently acquired by an unauthorized party. However, a data leak is possible without losing the data on the originating side. Other terms associated with data leakage prevention are 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 intrusion prevention system.

The technological means employed for dealing with data leakage incidents can be divided into categories: standard security measures, advanced/intelligent security measures, access control and encryption and designated DLP systems.

Standard security measures, such as firewalls, intrusion detection systems (IDSs) and antivirus software, are commonly available products that guard computers against outsider and insider attacks. The use of a firewall, for example, prevents the access of outsiders to the internal network and an intrusion detection system detects intrusion attempts by outsiders. Inside attacks can be averted through antivirus scans that detect Trojan horses that send confidential information, and by the use of thin clients that operate in a client-server architecture with no personal or sensitive data stored on a client device.

Advanced security measures employ machine learning and temporal reasoning algorithms for detecting abnormal access to data (e.g., databases or information retrieval systems) or abnormal email exchange, honeypots for detecting authorized personnel with malicious intentions and activity-based verification (e.g., recognition of keystroke dynamics) and user activity monitoring for detecting abnormal data access.

...
Wikipedia