How to Build an Open‑Source DLP System with Osquery, Wazuh, Zeek, and ELK

Introduction

Open‑source components can be combined to build a data‑loss‑prevention (DLP) system, providing full control and lower cost compared with commercial suites.

Endpoint data collection ("Neural Endpoints")

Collect OS‑level activity from endpoints.

Osquery ( https://github.com/osquery/osquery.git) exposes the OS as a relational database. Queries can retrieve:

Wazuh ( https://github.com/wazuh/wazuh.git) provides HIDS, log analysis, file‑integrity monitoring (FIM) and vulnerability detection. In a DLP context its agent can:

Network traffic auditing ("Gatekeeper")

Deep inspection of network traffic to detect exfiltration.

Zeek ( https://github.com/zeek/zeek.git) produces structured logs and can:

Suricata ( https://github.com/OISF/suricata.git) is a high‑performance IDS/IPS engine that uses rule‑based detection. Custom rules can match patterns such as ID numbers, credit‑card numbers, or internal project codes and generate alerts or block connections.

Central analysis and response ("Brain") – SIEM/UEBA platform

Aggregate endpoint and network data for correlation and investigation.

ELK Stack (Elastic Stack) :

Security Onion (

https://github.com/Security-Onion-Solutions/securityonion.git

) bundles Zeek, Suricata, Wazuh and a full ELK stack, offering a pre‑configured deployment option.

Data discovery and classification ("Asset Manager")

Open‑source DLP lacks mature automatic classification; custom scripts are required.

Use Python with regex libraries (e.g., Google RE2) and scanners such as truffleHog and gitleaks to scan code repositories, file servers and databases for secrets, then tag or inventory sensitive assets.

Collaborative workflow example

When an employee attempts to email Project‑Titan‑Source‑Code.zip , the endpoint probe (Osquery/Wazuh) logs the file access, Zeek extracts the file from the SMTP traffic, Suricata matches any sensitive patterns, and the SIEM correlates the events to generate an alert or block the transmission.

Key considerations

Integration effort : building an open‑source DLP requires expertise to connect collectors, parsers and the SIEM.

Data classification : custom development is typically needed to achieve reliable discovery and tagging.