Data Loss Prevention
A set of strategies, tools, and processes designed to detect and prevent the unauthorized transmission, exfiltration, or leakage of sensitive data outside an organization's controlled environment.
Data Loss Prevention (DLP) addresses one of the most significant risks organizations face: the unintended or malicious exposure of sensitive information. DLP solutions work by identifying sensitive data (through content inspection, contextual analysis, and classification labels), monitoring how that data moves across the organization, and enforcing policies that prevent unauthorized sharing or transmission. DLP operates at three key points: data in motion (network traffic, email, web uploads), data at rest (file servers, databases, cloud storage), and data in use (clipboard operations, screen captures, printing).
DLP is directly relevant to multiple compliance requirements. GDPR mandates appropriate technical and organizational measures to ensure data security, and DLP is a primary mechanism for preventing unauthorized disclosure of personal data. ISO 27001 Annex A controls on information transfer (A.5.14) and information classification (A.5.12-A.5.13) align closely with DLP capabilities. SOC 2's Confidentiality criteria require that confidential information is protected from unauthorized disclosure throughout its lifecycle. NIS2 requires essential entities to implement policies for handling and protecting sensitive data, which DLP directly supports.
Implementing DLP effectively requires a phased approach. Organizations should begin by classifying their data to understand what is sensitive and where it resides. Policy definition comes next — determining what constitutes an unauthorized transfer and what actions to take (block, quarantine, alert, encrypt). Starting in monitor-only mode helps organizations tune policies and reduce false positives before enabling enforcement. Modern DLP solutions increasingly leverage machine learning to improve accuracy in identifying sensitive content and distinguishing legitimate business activities from actual data loss scenarios. Cloud-native DLP capabilities from major cloud providers and SaaS platforms can extend protection to cloud storage, collaboration tools, and email without requiring separate infrastructure.
Related frameworks
Related terms
Access Control
The set of policies, procedures, and technical mechanisms that govern who can access which information assets, systems, and resources. Access control ensures that only authorized individuals can view, modify, or interact with sensitive data and systems.
Data Minimization
The principle that organizations should collect, process, and retain only the minimum amount of personal data necessary to fulfill a specific, stated purpose. Data minimization limits exposure risk by ensuring unnecessary data is never gathered in the first place.
Encryption
The process of converting data into an encoded format that can only be read by authorized parties who possess the correct decryption key. Encryption protects data confidentiality both at rest (stored data) and in transit (data being transmitted over networks).
Endpoint Protection
Security solutions and practices designed to protect end-user devices such as laptops, desktops, mobile phones, and servers from cyber threats including malware, ransomware, and unauthorized access.
Privacy by Design
An approach to system engineering and business practices that embeds privacy protections into the design and architecture of IT systems, processes, and products from the outset, rather than adding them as an afterthought.
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