Data Loss Prevention Software Open Source

TL;DR

• Open source data loss prevention (DLP) software provides customizable tools for organizations to discover, classify, and protect sensitive information across networks, endpoints, and the cloud.
• While lacking some features of commercial solutions, open source DLP offers advanced data security capabilities that can be tailored to meet specific compliance and data protection needs at low or no cost.

This guide explores the key capabilities and top open source DLP solutions to consider for securing intellectual property, customer data, financial information, and other critical digital assets.

What is Open Source Data Loss Prevention Software?

Open source DLP software consists of freely available tools and applications that organizations can leverage to prevent unauthorized access, use, and transfers of sensitive data. It scans content and metadata to identify confidential information, then applies policies to control or block data flows containing sensitive data across networks, endpoints, email, web channels, and cloud apps.

Open source DLP provides capabilities to:

• Discover and classify sensitive data at rest and in motion using content scanning and data fingerprinting.
• Monitor data usage, transfers, and policy violations through extensive logging and auditing.
• Protect sensitive data by blocking, quarantining, or encrypting content that violates DLP policies.
• Generate alerts and reports for security teams to investigate and remediate data leakage incidents.
• Customize data classifications, policies, rules, and workflows by modifying open source code.

Key Benefits of Open Source DLP

• Cost savings compared to commercial DLP solutions.
• Source code access enables customization to address unique data security needs.
• Options for both agent-based and agentless deployments.
• Helps meet compliance requirements for data protection regulations.
• Extends DLP capabilities for organizations using open source security tools.
• Allows scaling on low-cost hardware without vendor limitations.
• Integrates with existing open source software and security infrastructure.

Limitations of Open Source DLP Tools

• Limited pre-built content classifiers and policies compared to commercial DLP.
• Typically less user-friendly and requires more IT expertise.
• Lower performing data discovery and analytics capabilities.
• Minimal vendor support and responsiveness to issues.
• Higher administrative overhead for ongoing tuning and maintenance.
• Not ideal for complex, enterprise-wide DLP deployments.

Top 4 Open Source Data Loss Prevention Software Solutions

MyDLP Community Edition

MyDLP Community Edition is an open source DLP solution built for monitoring and preventing sensitive data leakage. It offers network traffic inspection, endpoint monitoring, and cloud-native deployment options. The free community edition provides basic DLP capabilities, while the enterprise version adds advanced features.

Key Features:

• Inspects web, email, instant messaging, and file transfer channels.
• Predefined data types like credit cards and national IDs.
• Regex pattern matching and keyword lists for custom data types.
• Endpoint monitoring for printing, external storage, and screenshots.
• Notifications and basic reporting.
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OpenDLP

OpenDLP is an open source, centralized DLP tool focused on data discovery through content scanning. It can identify sensitive data at rest across Windows and UNIX systems, as well as MySQL and Microsoft SQL databases.

Key Features:

• Agent-based and agentless Windows file system scanning.
• Agentless database scanning for MySQL and Microsoft SQL Server.
• Web-based management console.
• Regex pattern matching to detect sensitive data.
• Customizable scan profiles and blacklists.
• Scheduled scans with reporting.
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Security Onion

Security Onion is an open source Linux distro for network security monitoring, intrusion detection, and log management. It can be leveraged for data loss prevention by monitoring networks for signs of data exfiltration.

Key Features:

• Network traffic analysis and packet capture.
• Log aggregation and correlation.
• Intrusion detection with Snort and Suricata.
• Visualization and reporting for incident response.
• Behavioral analytics for detecting anomalies.
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Snort

Snort is an open source intrusion detection and prevention system that can be configured to perform DLP functions like scanning traffic for sensitive data patterns. Custom Snort rules can be created to identify PII and other critical data.

Key Features:

• Real-time traffic analysis and logging.
• Customizable rules and signatures.
• Data and protocol inspection capabilities.
• Generating alerts when matches detected.
• Blocking suspicious traffic and connections.

Key Capabilities to Look for in Open Source Data Loss Prevention Software

While open source DLP solutions vary in features, key capabilities to look for include the following:

• Data discovery - Scan local systems and networks to find where sensitive data resides.
• Content inspection - Deep analysis of data at rest and in motion to classify and match patterns.
• Endpoint monitoring - Agent-based scanning and auditing of endpoints.
• Network traffic analysis - Identify data leaving the network perimeter via web, email, FTP.
• Policy enforcement - Block, encrypt, or quarantine data flows and actions based on defined policies.
• Notifications and alerts - Inform security teams of detected incidents and policy violations.
• Reporting - Summarize DLP monitoring activity and data discovery results.
• Customizable rules - Flexible regex, keywords, and other classifiers to detect sensitive data.
• Data fingerprinting - Recognize data based on patterns without needing exact file matches.
• Forensics capabilities - Support auditing and investigation of data leakage incidents.
• Workflow automation - Orchestrating data remediation actions when threats are detected.
• Cloud integration - Discover and monitor sensitive data in cloud apps and services.

Choosing the Right Open Source Data Loss Prevention Software Tool

Consider the following when selecting an open source DLP solution:

• Deployment needs - Agent vs. agentless, on-prem vs cloud architecture.
• IT resources and skill level - Opt for less complex tools if you have limited expertise.
• Specific data types and compliance needs - Pick tools with relevant classifiers and policies.
• Integration requirements - Choose a solution that works with your tech stack.
• Performance and scalability - Assess scanning and data processing capabilities.
• Support availability - Documentation quality and community forums.
• Customization needs - Ability to tailor policies, rules, and workflows.
• Overall feature set - Weigh all capabilities against business requirements.

For enhanced data security, open source DLP can be used alongside commercial solutions like endpoint protection platforms. With the right solution, open source DLP enables organizations to cost-effectively discover, monitor, and protect sensitive information.

Implementing Open Source Data Loss Prevention Software Best Practices

Follow these best practices when deploying open source DLP for maximum effectiveness:

• Start with non-intrusive monitoring to establish data usage baselines.
• Build custom classifiers tailored to your specific sensitive data types.
• Leverage endpoint agents to extend monitoring and protection to devices.
• Tune policies and rules to reduce false positives and negatives.
• Phase in enforcement actions like blocking and encryption.
• Set alerts for immediate notification of policy violations.
• Document all configuration changes and customizations.
• Schedule scans during off-peak hours to minimize disruption.
• Regularly test and audit DLP controls to identify gaps.
• Educate employees on DLP policies and ethical data handling.
• Centralize logging and reporting for easy access and analysis.
• Have a plan to respond if a data leakage incident occurs.
• Continuously expand and update DLP coverage as data flows evolve.

How Strac Can Help:

While open source data loss prevention software offers customizable options, organizations seeking enterprise-grade protection may benefit from Strac's comprehensive DLP solution. Strac provides a SaaS/Cloud DLP and Endpoint DLP solution with modern features that complement and extend open source capabilities.

Strac's built-in and custom detectors support all sensitive data elements for PCI, HIPAA, GDPR, and any confidential data. Uniquely, Strac offers detection and redaction capabilities for images and deep content inspection for various document formats. Explore Strac's full catalog of sensitive data elements to see the breadth of protection available.

For organizations concerned about compliance, Strac DLP helps achieve standards for PCI, SOC 2, HIPAA, ISO-27001, CCPA, GDPR, and NIST frameworks. With easy integration, customers can implement Strac and see live scanning and redaction on their SaaS apps in under 10 minutes.

Strac's machine learning models ensure accurate detection and redaction of sensitive PII, PHI, PCI, and confidential data, minimizing false positives and negatives. The solution offers extensive SaaS integrations, including AI integration with LLM APIs and AI websites like ChatGPT, Google Bard, and Microsoft Copilot.

For comprehensive protection, Strac provides Endpoint DLP that works across SaaS, Cloud, and Endpoint environments. Developers can leverage Strac's API support for custom implementations, while inline redaction capabilities ensure sensitive text is masked or blurred within attachments.

Strac's customizable configurations and out-of-the-box compliance templates allow for flexible, tailored data protection measures that can complement or enhance open source DLP implementations.

The Bottom Line

Ready to explore a robust alternative to open source data loss prevention software? Book a demo with Strac to see how our comprehensive DLP solution can provide enterprise-grade protection for your sensitive data. Join our satisfied customers who trust Strac for their most critical data security needs.