What are the 3 types of data loss prevention?

Data loss prevention (DLP) refers to a comprehensive approach taken by organizations to detect and prevent the unauthorized transmission of sensitive or confidential information outside of the corporate network. DLP encompasses people, processes, and technology focused on identifying, monitoring, and protecting data in use, data in motion, and data at rest through deep content inspection and context-aware controls.

There are three main types of data loss prevention solutions:

Network DLP

Network DLP monitors network traffic to prevent data from leaving the corporate network. Network DLP solutions inspect traffic on the network in real-time, analyzing data packets against a series of rules designed to detect sensitive information. This allows organizations to stop sensitive information before it leaves the protected network perimeter.

Network DLP is placed at network egress points, where data is leaving the internal network, to monitor outbound traffic. Using predefined policies and content inspection techniques like optical character recognition, network DLP can identify sensitive data or patterns such as credit card numbers, social security numbers, or health records being transmitted out through email, web uploads, FTP transfers, or instant messaging services. If a policy match is detected, the solution can prevent or block the action, log the incident, or trigger additional activities like encrypting the data.

Key capabilities of network DLP solutions include:

  • Real-time inspection of outbound network traffic
  • Identification and prevention of sensitive data transmission
  • Extensive set of predefined data definitions and policies
  • Fingerprinting techniques to detect structured and unstructured data
  • Integrations with secure web gateways and firewalls
  • Support for numerous protocols like HTTP, SMTP, FTP, and IM

Network DLP is extremely useful for stopping data loss at the exit points of the network, preventing breaches before they occur. However, network DLP only protects data in motion over the network. It cannot see sensitive data at rest on endpoints or being transferred offline on USB devices.

Endpoint DLP

Endpoint DLP focuses on monitoring and securing endpoints like laptops, desktops, and servers where data is often stored or used. Endpoint DLP software is centrally managed and installed locally on end-user devices to monitor activity, applications, and data access in real-time.

Endpoint DLP agents can detect when sensitive files are being accessed or transmitted through removable media like USB drives. The software analyzes data at rest on the endpoint to identify sensitive information and applies context-aware controls to limit unauthorized transmissions. For example, endpoint DLP could detect when an employee connects a USB drive and tries to copy sensitive financial reports. The software can block the copy, log the event, notify the employee, and alert IT teams.

In addition to controlling removable media like USB drives, endpoint DLP can also regulate printing, screen capture, CD/DVD burning, and uploads to web applications. Key capabilities include:

  • Content-aware monitoring of sensitive data on endpoints
  • Device and port control over USB drives, printers, and upload channels
  • Predefined and custom data definitions, policies, and regulations
  • Automated classification and labeling of sensitive documents
  • Encryption of sensitive data at rest on endpoints
  • Activity monitoring, alerts, and detailed incident reporting

Endpoint DLP provides protections that network DLP lacks by securing data directly on user devices. However, endpoint agents only protect the devices on which they are installed. Data can still be leaked by users who are not covered by endpoint DLP controls.

Cloud DLP

Cloud DLP secures sensitive data stored in cloud apps and services like Microsoft Office 365, G Suite, Box, Dropbox, Salesforce, and more. As organizations increasingly rely on cloud services to enable collaboration and remote workforces, there is a growing need to monitor how data is handled within cloud environments.

Cloud DLP enables organizations to discover, monitor, and protect sensitive information across their cloud applications in a centralized manner. Policies and controls are integrated at the API level with cloud apps to analyze data at rest, data in motion, and data in use within cloud services. For example, cloud DLP could identify sensitive data stored in OneDrive and prevent an unauthorized user from downloading or sharing the file externally.

Key capabilities of cloud DLP include:

  • API-level integration and data monitoring across cloud apps
  • Discovery and classification of sensitive data stored in the cloud
  • Policies to detect high-risk activities like external sharing of sensitive data
  • Automated scanning and tagging of sensitive cloud content
  • Encryption of sensitive information within cloud apps
  • Activity monitoring, alerts, and reporting on cloud events and risks

Cloud DLP extends data loss prevention to cloud environments, enabling centralized protection of data across cloud apps and services. However, cloud DLP relies on API-level hooks into specific cloud applications, so coverage across services may be inconsistent.

Comparing the Pros and Cons

Each type of DLP has distinct strengths and weaknesses based on the location of data they monitor and protect. Here is a comparison of the pros and cons:

DLP Type Pros Cons
Network DLP
  • Real-time detection of outbound sensitive data across the network
  • Prevention of data loss at the source
  • Broad coverage of network protocols and channels
  • Limited visibility into endpoint and cloud activities
  • Only monitors data in motion, not data at rest
Endpoint DLP
  • Secures data directly on end-user devices
  • Controls removable media like USB drives
  • Full visibility into user behaviors and data actions on devices
  • Must be installed on every endpoint
  • Only protects devices with agents installed
Cloud DLP
  • Centralized visibility and control across cloud apps
  • Allows secure collaboration while protecting data in the cloud
  • Discovery of sensitive data stored in cloud services
  • Relies on API integration with cloud providers
  • Limited network and endpoint visibility

Key Considerations for Implementation

There are several important factors to consider when implementing data loss prevention in an organization:

  • Know your data – Discover and classify sensitive information across networks, endpoints and the cloud to understand what data needs protection.
  • Align to regulations – Ensure DLP policies adhere to regulatory and compliance mandates around data security and privacy.
  • Understand workflows – Map out user workflows for handling sensitive data to avoid business disruption when implementing controls.
  • Involve stakeholders – Engage with legal, HR, IT and other groups to gain buy-in for DLP and refine policies.
  • Start small – Roll out DLP controls in phases, starting with most critical data and lower-impact policies.
  • Tune polices continuously – Adjust rules and policies over time to improve accuracy and align controls with changing needs.
  • Layer defenses – Utilize a mix of network, endpoint and cloud DLP for comprehensive coverage across environments.
  • Educate end users – Train employees about DLP and data security best practices to improve compliance.

Taking a strategic, user-focused approach to deploying the right mix of DLP controls enables organizations to reduce their risk of data loss while enabling workflows and collaboration.

Conclusion

Data loss can have catastrophic consequences for organizations, exposing sensitive information, enabling intellectual property theft, and damaging reputations. That’s why implementing a comprehensive data loss prevention program is critical for information security.

DLP provides multilayered protection against data loss across three key vectors:

  • Network DLP secures data in motion
  • Endpoint DLP secures data at rest
  • Cloud DLP secures data in cloud apps

While each DLP method has pros and cons, used together they provide broad visibility and control across the data landscape. A successful DLP implementation requires understanding your data, users, and workflows to balance security and productivity.

With the right DLP controls in place, organizations can enable collaboration, support remote work, and adopt cloud services while preventing the risk of data loss and maintaining compliance. As threats evolve and regulations expand, DLP will continue to be a foundational data protection domain for reducing business risk.