Securing confidential business data is vital for organizations across all industries. While open-source DLP software presents viable solutions for data protection, larger enterprises often turn to closed-source DLP software solutions for enhanced functionality.
Here, see the top open-source DLP software, examining their features and capabilities:
Top open-source DLP software
Inclusion criteria: All software offering open-source DLP or configurable DLP functionality with active development (updates within the last 6 months) and significant community adoption.
*Tools ranked by GitHub stars to reflect community validation and adoption.
** Other: Since the open-source DLP software landscape is limited, we included additional open-source software that can be configured to perform DLP tasks.
Deep dives into open-source DLP solutions
1. TruffleHog
TruffleHog is the most powerful open-source secret scanner, with over 23,500 GitHub stars and 14 million downloads. It discovers, classifies, and verifies leaked credentials across Git repositories, files, directories, and multiple platforms.
Standout capabilities:
- Classifies 800+ secret types (AWS keys, database passwords, API tokens)
- Verifies if discovered secrets are still active
- Scans Git history, including deleted commits and private forks
- Advanced analysis reveals secret permissions and accessible resources
Best for: DevSecOps teams, security engineers scanning code repositories, and organizations with complex multi-cloud environments.
Limitations: Primarily focused on code and version control; requires integration for broader enterprise DLP needs.
2. Gitleaks
Gitleaks is a fast, lightweight secret scanner with 19,700 GitHub stars. Purpose-built for detecting hardcoded secrets in Git repos, it integrates seamlessly into CI/CD pipelines.
Standout capabilities:
- Pre-commit hooks prevent secret commits before they happen
- Composite rules with proximity matching for complex patterns
- Archive extraction scans zip files and tarballs
- Custom reporting with multiple output formats (JSON, SARIF, CSV)
Best for: Development teams implementing shift-left security, automated CI/CD pipelines, and organizations prioritizing prevention over detection.
Limitations: Git-focused with limited coverage beyond source code repositories.
3. Wazuh
Wazuh is a comprehensive open-source security platform with 14,100 GitHub stars. While not a traditional DLP tool, it provides robust data protection through unified XDR and SIEM capabilities.
Standout capabilities:
- File integrity monitoring detects unauthorized data changes
- Endpoint security across on-premises, cloud, and containerized environments
- Vulnerability detection and security configuration assessment
- Log analysis and compliance management (PCI DSS, HIPAA, GDPR)
Best for: Enterprises needing comprehensive security monitoring, SOC teams managing incident response, and organizations with compliance requirements.
Limitations: Requires significant configuration for DLP-specific use cases; steeper learning curve than purpose-built DLP tools.
4. Security Onion
Security Onion is a Linux distribution for network security monitoring with 4,200 GitHub stars. It includes integrated tools for threat hunting, intrusion detection, and log management.
Standout capabilities:
- Unified platform with Suricata, Zeek, osquery, and Elasticsearch
- Real-time network traffic analysis and PCAP capture
- Case management and alert investigation workflows
- Pre-built dashboards for security operations
Best for: Network security teams, threat hunters, and organizations building SOC capabilities with limited budgets.
Limitations: Not explicitly designed for DLP; primarily detects data exfiltration attempts rather than preventing them. Requires dedicated hardware or VMs.
5. Snort
Snort is an open-source intrusion prevention system with 2,700 GitHub stars. It performs real-time traffic analysis and can be configured for DLP tasks through custom rules.
Standout capabilities:
- Real-time traffic analysis and packet logging
- Customizable rule-based detection engine
- Protocol analysis and content matching
- Integration with security automation platforms
Best for: Network administrators, security analysts creating custom detection rules, and organizations with in-house security expertise.
Limitations: Requires manual rule creation for DLP functionality; lacks automated data classification and policy management.
What is open-source DLP software?
Open-source data loss prevention software is a type of solution designed to protect sensitive information from data leaks, unauthorized access, and breaches. This software provides tools for scanning sensitive data, monitoring data transfers, and preventing data loss across various platforms, including cloud services, mobile devices, and external devices.
Why are they valued?
Open-source DLP tools are particularly valued for their flexibility and adaptability, allowing IT administrators and security teams to modify source code to meet specific data security requirements and compliance standards.
They offer a cost-effective option for businesses of all sizes to safeguard customer, financial, and personally identifiable information, ensuring continuous protection against data exfiltration, insider threats, and data breaches.
Quick selection guide
Essential features of open-source DLP software
Data classification and governance
Detection engines are crucial to a DLP solution’s ability to identify, classify, and manage sensitive data. A good DLP solution enables the automatic classification and application of sensitivity labels to files across the entire environment. Customizable configuration of classification policies and protective measures is essential.
Access control and user activity monitoring
Role-based access control is an essential component of DLP. Tracking user identities and roles against granular policies enables a proactive approach to preventing threat actors from accessing sensitive digital assets. Granular access controls help prevent insider threats, such as noncompliant file transfers.
Exfiltration prevention and inline scanning
Exfiltration prevention is a critical DLP function that mitigates the risks of data theft and unintentional leaks. Inline scanning is required for this function, as the action must be blocked before it occurs. Preventing data theft and leaks helps reduce the number of potential attack vectors.
Secret Detection and Verification
Modern DLP tools detect hardcoded secrets, API keys, and credentials in code repositories. Advanced solutions verify if discovered secrets are active, enabling teams to prioritize remediation efforts effectively.
Open source vs. closed source DLP
Here, we compare open-source and closed-source software from three aspects.
1. Flexibility and customization
Open-source DLP: Open-source DLP tools, such as those used for scanning sensitive data, offer extensive customization options. These solutions enable security teams to modify the source code, tailoring the DLP tool to effectively protect sensitive information, including financial data and personally identifiable information.
This level of customization supports continuous monitoring and policy settings adjustments for businesses handling the most sensitive data.
Closed-source DLP: On the other hand, closed-source DLP software typically offers less flexibility but comes with user-friendly, pre-configured settings ideal for immediate deployment. These tools, often used by large enterprises, are designed to efficiently meet general data protection requirements, ensuring compliance with data security standards and reducing the risk of data breaches with minimal configuration.
2. Cost and accessibility
Open-source DLP: Open-source DLP solutions typically have no initial cost, making them an attractive option for small and medium-sized businesses. However, they require significant IT expertise to customize and maintain, potentially increasing the total cost of ownership, including ongoing management and updates to safeguard against data theft and leaks.
Closed-source DLP: Conversely, closed-source DLP solutions involve upfront and ongoing licensing fees, but they also include vendor support for incident management, updates, and troubleshooting. This can provide a more predictable expense and less administrative overhead for IT administrators, especially in environments with extensive data transfers or where sensitive data is stored across cloud services and external devices.
3. Security and support
Open-source DLP: The security of open-source DLP software relies heavily on the community and on users’ active involvement. While flexible, this approach requires a proactive stance on security updates and may not provide the same level of immediate support as closed-source alternatives.
It’s well-suited for organizations with capable technical teams dedicated to protecting data at rest and in transit, managing data access, and preventing data loss through continuous adjustments and monitoring.
Closed-source DLP: Closed-source DLP solutions often offer more comprehensive security features out of the box, designed for robust protection against insider threats, unauthorized file transfers, and data exfiltration.
With dedicated vendor support, these solutions help streamline compliance requirements and provide a centralized dashboard for monitoring suspicious behavior and managing data breach incidents effectively.
Open-source DLP tools offer affordability and flexibility for smaller businesses and organizations that have the necessary technical expertise. However, their limitations in scalability and support often make closed-source solutions the preferred choice for enterprises requiring strong protection.
Future of Open-Source DLP Software
AI and machine learning enhance DLP solutions by improving detection accuracy, reducing false positives, and providing real-time threat intelligence. The evolving DLP landscape includes:
- Cloud Access Security Brokers (CASB) – Protecting data in cloud applications
- Email and Gateway DLP – Monitoring data in transit
- Insider Risk Management – Behavioral analytics and user monitoring
- Data Security Posture Management – Continuous data discovery and classification
- App Native DLP – Protection built into applications
Open-source tools increasingly incorporate these capabilities, making enterprise-grade data protection accessible to organizations of all sizes.
Other open-source software for data protection
1. ModSecurity
- Purpose: Open-source web application firewall that can be configured for DLP purposes by writing custom rules to detect and block specific sensitive data patterns in HTTP traffic.
- Features: Real-time traffic analysis and custom rule support.
- GitHub Stars: ~6.8 K.
2. OSSEC
- Purpose: Another open-source security tool that functions as a host-based intrusion detection system (HIDS) and can monitor changes in files or detect sensitive data leaks when configured with custom rules.
- Features: File integrity monitoring and alerting.
- GitHub Stars: ~4.3 K.
3. Pi-hole
- Purpose: Although primarily a DNS-level ad and tracker blocker, it can be adapted to filter or block domains involved in data exfiltration.
- Features: DNS-based monitoring and filtering.
- GitHub Stars: ~43 K.
4. ELK Stack (Elasticsearch, Logstash, Kibana)
- Purpose: While it’s a logging and data visualization tool, it can be tailored for DLP tasks through custom dashboards, queries, and anomaly detection in data flows.
- Features: Log ingestion, analysis, and customizable alerting.
- GitHub Stars: Elasticsearch ~64K, Logstash ~13K, Kibana ~18 K.
These tools can be configured or extended to perform specific DLP-related tasks; however, they may require significant customization and expertise to achieve the same level of effectiveness as purpose-built DLP software.
FAQs for open-source DLP software
Further reading
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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