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Data Loss Prevention

Data Loss Prevention (DLP) is the practice of detecting and preventing data breaches, exfiltration, or unwanted destruction of sensitive data. Organizations use DLP to protect and secure their data and comply with regulations.

The DLP term refers to defending organizations against both data loss and data leakage prevention. Data loss refers to an event in which important data is lost to the enterprise, such as in a ransomware attack. Data loss prevention focuses on preventing illicit transfer of data outside organizational boundaries.

Organizations typically use DLP to:

  • Protect Personally Identifiable Information (PII) and comply with relevant regulations
  • Protect Intellectual Property critical for the organization
  • Achieve data visibility in large organizations
  • Secure mobile workforce and enforce security in Bring Your Own Device (BYOD) environments
  • Secure data on remote cloud systems

Causes of Data Leaks

Three common causes of data leaks are:

  • Insider threats — a malicious insider, or an attacker who has compromised a privileged user account, abuses their permissions and attempts to move data outside the organization.
  • Extrusion by attackers — many cyber attacks have sensitive data as their target. Attackers penetrate the security perimeter using techniques like phishing, malware or code injection, and gain access to sensitive data.
  • Unintentional or negligent data exposure — many data leaks occur as a result of employees who lose sensitive data in public, provide open Internet access to data, or fail to restrict access per organizational policies.

Components of a Data Loss Solution

  • Securing data in motion — technology installed at the network edge can analyze traffic to detect sensitive data sent in violation of security policies.
  • Securing endpoints — endpoint-based agents can control information transfer between users, groups of users, and external parties. Some endpoint-based systems can block attempted communications in real time and provide user feedback.
  • Securing data at rest — access control, encryption and data retention policies can protect archived organizational data.
  • Securing data in use — some DLP systems can monitor and flag unauthorized activities that users may intentionally or unintentionally perform in their interactions with data.
  • Data identification — it is crucial to determine if data needs to be protected or not. Data can be defined as sensitive either done manually by applying rules and metadata, or automatically via techniques like machine learning.
  • Data leak detection — DLP solutions and other security systems like IDS, IPS, and SIEM, identify data transfers that are anomalous or suspicious. These solutions also alert security staff of a possible data leak.
  • DLP Solutions safeguard data at rest and data in use, and detect leaks of file-based data.

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