Data Loss Prevention

Data loss prevention (DLP) refers to tools and processes that seek to protect sensitive data from being lost, used for wrong purposes, or compromised by unauthorized users. DLP tools classify data to detect possible violations of policies defined by companies and organizations in the context of security information. These policies and regulations are commonly defined by regulators or Information Security Officers who, in most cases, seek to comply with data protection regulations. Once any possible violation of data protection policies is uncovered, DLP software alerts users about the situation and offers viable ways to remediate them. Among the possible solutions is data encryption, anonymization, or any other protective actions that the users might undertake to remediate these situations. These defensive actions might prevent users from inadvertently sharing data that could be exploited for malicious activities.
The factors that have driven the widespread adoption of DLP software are several. Among them are the increased usage of cloud infrastructure for data storing, the increased complexity of data systems that interact with each other, and an overall increase in the number of factors that might lead to security breaches that lead to data being exposed. Therefore, the importance of being able to alert and remediate these situations has increased in recent years.
DLP tools often offer monitoring and control of data endpoints activities, provide filters from streams of data in private networks, and offer real-time data monitoring of cloud infrastructure. These systems seek to provide compliance reporting and monitoring that ultimately leads to identifying points of failure and propose strategies on how to remediate them, and offer data forensics to address any possible incidents.

DLP Main Focus Areas

DLP software mainly focuses on solving three significant aspects that are commonly the weak points for companies and organizations from the point of data security:

  • Personal Information Protection: Companies and organizations handle large amounts of information that can lead to the identification of individuals. This information can be Personally Identifiable Information (PII), Personal Health Information (PHI), or credit card information (PCI). Storing this information requires tight control and compliance with data protection regulations to prevent this data from mishandling. DLP software can search, classify and offer means to remediate possible situations that might put this information in the wrong hands. It might also provide ways to monitor and alert about the capabilities to comply with specific regulations.
  • Intellectual Property Protection: Data about confidential processes or intellectual property is also subjected to be misused of being the target of malicious attacks. DLP software can detect information that might be sensible from the IP standpoint by using tools that rely on context-based classification. These tools can flag data that might be confidential information, regardless of whether it’s stored in a structured or unstructured way.
  • Data Visibility: DLP tools can also be used to visualize and analyze the entire set of data endpoints, streams of data, private networks, cloud infrastructure, or any other integrations that might be susceptible to being a point of failure. These visualizations can be used to monitor users’ activities and alert them of possible harmful situations.

In these three cases, DLP software can offer ways to solve weak points for companies and organizations, including inside threats such as user behavior analysis and other more advanced threats and attacks.

Common Data Leaks Causes

  • Insider Threats: Users who belong to the organization or have penetrated it might comprise data by used accounts with privileges and permissions, attempting to move data outside the organization.
  • External Attackers: Attackers might want to extract data from the organization using phishing, malware, or techniques such as SQL injection to gain access to sensitive data sets.
  • Unintentional Exposure: Poor data management practices might expose data without proper protection, which compromises organizations. The exposure might derive from users losing sensitive data, providing access to private networks, or failing to comply with data security practices.

 

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