adatvesztésmegelzési

Adatvesztésmegelőzés, often abbreviated as DLP, refers to a set of strategies, processes, and technologies used by organizations to ensure that sensitive data is not lost, misused, or accessed by unauthorized users. The primary goal of DLP is to protect data wherever it resides, whether it is in use, in motion, or at rest. This involves identifying, monitoring, and protecting data in accordance with business and security policies.

DLP solutions typically operate by classifying data based on its sensitivity and then applying policies to govern its handling. This classification can be achieved through various methods, including content analysis, keyword matching, regular expressions, and data fingerprinting. Once data is classified, DLP systems can enforce rules to prevent sensitive information from leaving the organization's network, being copied to removable media, or being transmitted through unapproved channels.

Key features of DLP systems often include data discovery and classification, endpoint DLP, network DLP, and cloud DLP. Endpoint DLP focuses on protecting data on user devices like laptops and desktops, while network DLP monitors data as it travels across the organization's network. Cloud DLP extends these protections to data stored and processed in cloud environments.

Implementing a DLP strategy requires a thorough understanding of an organization's data assets, regulatory compliance requirements, and potential threats. It is a crucial component of a comprehensive cybersecurity framework, helping organizations mitigate risks associated with data breaches, intellectual property theft, and non-compliance.