Open-Source Alternatives to Splunk Enterprise

Wazuh is a free, open-source security platform that provides unified XDR (Extended Detection and Response) and SIEM (Security Information and Event Management) capabilities. It consists of an agent deployed on endpoints and a central server that collects, analyzes, and correlates security data. Wazuh performs real-time log analysis, file integrity monitoring, rootkit detection, vulnerability assessment, configuration compliance checking (CIS, PCI DSS, HIPAA, NIST), and active response. It detects threats using rules that correlate events from multiple sources, including endpoint logs, cloud services (AWS, Azure, GCP), containers, and network devices. Wazuh integrates with Elasticsearch and OpenSearch for log storage and visualization, and includes a custom dashboard for security operations. Its open-source nature and comprehensive feature set make it a popular alternative to commercial SIEM solutions.

Sigma is a generic and open signature format for SIEM systems, analogous to what YARA is for files and Snort is for network traffic. Sigma rules describe log events in a YAML-based format that is independent of any specific SIEM product. Using the sigma-cli converter (pySigma), rules can be translated into native query languages for Splunk (SPL), Elasticsearch (Lucene/KQL), Microsoft Sentinel, QRadar, CrowdStrike, Carbon Black, Grep, and over 30 other backends. The Sigma rule repository contains thousands of community-contributed detection rules covering MITRE ATT&CK techniques, common malware behaviors, lateral movement, persistence mechanisms, and suspicious system activity. Security teams use Sigma to write detection logic once and deploy it across their entire detection infrastructure, regardless of which SIEM products they use. The format is maintained by the SigmaHQ project and has become the de facto standard for shareable detection rules.

osquery, developed at Facebook, exposes an operating system as a high-performance relational database. This allows you to write SQL queries to explore operating system data - running processes, loaded kernel modules, open network connections, browser plugins, hardware events, file hashes, and hundreds of other system attributes are all available as SQL tables. For security teams, this means you can query your fleet in real-time: 'SELECT * FROM processes WHERE name LIKE \'%miner%\'' finds cryptominers, 'SELECT * FROM listening_ports WHERE port = 4444' finds suspicious listeners. osquery supports scheduled queries that log differential changes over time, making it powerful for continuous security monitoring and compliance verification. It runs on Linux, macOS, Windows, and FreeBSD, and integrates with fleet management tools like Fleet (formerly Kolide) for centralized querying and alerting across thousands of endpoints.

Timesketch is Google's open-source collaborative forensic timeline analysis platform designed for security incident investigations. It ingests events from multiple forensic sources — Plaso supertimelines, CSV files, JSONL logs, and direct uploads — and presents them in a searchable, annotatable web interface where multiple analysts can work simultaneously. Investigators can create named views with saved queries, tag events with labels and comments, build investigation timelines, and share findings with teammates. Timesketch includes built-in analyzers that automatically detect suspicious patterns like lateral movement, credential access, and data staging. The Sigma integration allows analysts to run detection rules directly against timeline data. The sketch-based workflow means each investigation is self-contained with its own data, annotations, and analysis — making it easy to hand off cases between analysts or revisit investigations months later.

Hayabusa is a forensic tool designed to generate timelines from Windows event logs and facilitate threat hunting. Written in Rust, it integrates Sigma rule support to automatically detect suspicious activities, making it a powerful utility for digital forensics and incident response (DFIR) operations. Its fast processing capability allows security professionals to efficiently analyze large volumes of log data for potential security threats.

Plaso (log2timeline) is a super timeline creation engine that extracts timestamps from multiple forensic artifact sources into a single timeline. It supports a wide range of log and artifact formats, facilitating comprehensive forensic analysis. Written in Python, Plaso is widely used in digital forensics for its ability to correlate events from diverse data sources, providing a unified view of system activities over time.