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What Is Network Data Loss Prevention vs Endpoint DLP?

by Chris Brook on Tuesday February 14, 2023

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Data loss prevention software protects and secures your data from going where it shouldn’t go.

What Are the 3 Types of Data Loss Prevention?

Data Loss Prevention emerged to address the proliferation of data and is used to help organizations protect sensitive data, such as intellectual property and other business-critical data, from loss, damage, theft, and malicious abuse.

The three types of data loss prevention are:

  • Network DLP: This consists of security software and practices that monitor, track, and analyze activity across a network. Through network security, it tries to detect and prevent critical, confidential, or sensitive data from being exfiltrated through network traffic. In addition to inspecting network protocols, network DLP can discover sensitive information across various local and remote repositories (ex. Network share and MS Sharepoint Online), including databases.
  • Endpoint DLP: Endpoint DLP extends monitoring for data loss to endpoints such as mobile devices, IoT devices, laptops, desktops, and servers. Endpoint DLP is predominantly concerned with protecting data in use and data at rest.
  • Cloud DLP: Because the cloud is a storage location, cloud DLP is used to protect data at rest. In addition to the public cloud, this DLP can also protect data inside a private cloud run on a virtual server. 

How Does DLP Work?

DLP has to engage with many attack vectors and access points in its task of data loss prevention. In addition to leveraging encryption and user access control, here are the processes involved in making DLP work.

  1. Classifying data: Data classification is an important prerequisite for DLP. This includes labeling data in an organization’s possession into, say, public, sensitive, or internal classification levels.

    Because classification can be labor intensive, most DLP solutions provide automated data discovery and classification services. This technology can scan your data repositories to classify new data entering the organization’s infrastructure. 

  2. Establishing confidentiality levels: While DLP solutions provide classification features by default, you shouldn’t totally outsource this function. IT departments should assign data classification labels that make sense within the context of their data security.

    Typical classification examples could range from credit confidential, card information, sensitive, top secret, private, and internal.  

  3. Linking protection to the right context: Because data in the enterprise can occupy several states (at rest, in motion, or in use), DLP has to account for the susceptibility of data loss at each stage and with each loss vector. 
  4. Developing DLP policy: Configuring a DLP system’s behavior by creating data rules and policies. These encompass how the DLP system should react to data events. These may include revoking user access when someone is in violation of policy, issuing alert notifications when confidentiality markers are triggered, and so on. 
  5. Monitoring and investigation: With the visibility provided by DLP, security experts can easily detect data leak security incidents through anomalous behavior, and subsequently reduce the chance of data loss and reputational harm. 

In summary, DLP requires discovering sensitive data, accurately classifying it, and taking remediation actions such as denying access or removing duplicates and inaccuracies.

General DLP Use Cases

When a data breach occurs, it exposes organizations to significant reputational, financial, and regulatory risks.

A data breach or incident can manifest in several forms, such as insider threat, data leakage, data exfiltration, or data loss.

  • Data loss generally encompasses any action or event that renders data usable through destruction, damage, or corruption.
  • Insider threats are caused by authorized users who either maliciously or unintentionally cause data loss or abuse.
  • Data leakage occurs due to the unauthorized but unintentional transfer of confidential or sensitive data.
  • Data exfiltration is, on the other hand, the unauthorized and intentional transfer of confidential or sensitive data.

Fortunately, DLP is designed to address these concerns in a concerted manner.

Preventing Data-Related Incidences

DLP solutions are primarily tasked with preventing data loss and data leakage. These are the ways DLP helps to achieve this objective.

Providing Data Visibility

Data visibility is a prerequisite for data security. You can’t monitor your data without knowing where it resides, its movement flow, and its chain of custody. Comprehensive DLP solutions typically provide insight into data at the three stages of the data lifecycle.

Protecting IP and Competitive Advantage 

Business battles are increasingly waged on eCommerce front stores through digital products and the power of digital brand awareness. Data is increasingly the bedrock of building intellectual property, trade secrets, and product designs that drive corporate profits.

Without DLP standing as a bulwark, the proprietary information, and the data that comprise it can be easily lost through theft and corporate espionage.

Ensuring Regulatory Compliance Is Maintained

The sensitivity of data and the risk it poses to people’s privacy have compelled governments around the world to enact legislation to protect personally identifiable information (PII), including health and financial records.

Some popular ones include GDPR, HIPAA, PCI DSS, SOX, and CCPA.

Complying with all these cybersecurity laws can be challenging for businesses. DLP software provides a mechanism to monitor data and ensure the right policies and data frameworks are applied.

What are the similarities and differences between endpoint vs. network vs. cloud DLP?

Although they each have different objectives, both network, cloud, and endpoint DLP are necessary to fortify an organization’s data security posture. Together, they ensure that all the bases are covered with regard to data protection, namely, monitoring movement and activity surrounding critical data, protecting in all phases of the data lifecycle, and controlling who and how it is accessed.

Network DLP protects data traveling across the network. As a subset of network DLP, cloud DLP extends protection to cloud repositories for organizations that leverage cloud computing resources. In addition to protecting data in motion, endpoint and cloud DLP prevents data loss when it is being processed and in general use.

Also, the common denominator across these three DLP processes is encryption. It is used to protect data, whether it is at rest, in motion, or in use.

Network DLP vs. Endpoint DLP

 As its name implies, network DLP secures data transmitted across a network. It also protects data on web apps like email and other file transfer processes from being exfiltrated. These operate at the network periphery and act as agents of network transmission.

Unlike network DLP, which is equipped to protect data in motion and data at rest, endpoint DLP protects data in all three data cycle phases: data in use, in motion, and at rest. Endpoint DLP mainly achieves this through the installation of agents.

The prominent use case of endpoint DLP is protecting intellectual property and ensuring compliance to data policies are adhered to.

Learn How Digital Guardian Secure Collaboration Helps to Extend Security in DLP Products (like Digital Guardian) to Safeguard Your Data 

Digital Guardian Secure Collaboration's ability to extend security in DLP solutions allows you to combine the best of breed data protection to include network and endpoint DLP along with digital rights management (DRM), and information rights management (IRM). These measures ensure your data is protected regardless of where, how, or who accesses it.

To learn more about data loss prevention and how to bulletproof your endpoints, read about how we work with DLP solutions here.

Tags:  Secure Collaboration

Chris Brook

Chris Brook

Chris Brook is the editor of Digital Guardian’s Data Insider blog. He is a cybersecurity writer with nearly 15 years of experience reporting and writing about information security, attending infosec conferences like Black Hat and RSA, and interviewing hackers and security researchers. Prior to joining Digital Guardian–acquired by Fortra in 2021–he helped launch Threatpost, an independent news site that was a leading source of information about IT and business security for hundreds of thousands of professionals worldwide.

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