Sigma Rules
A generic and open signature format for describing log event detections, shareable across SIEM systems.
What is Sigma Rules?
Sigma is a generic and open signature format for describing log event detections in a standardized, vendor-agnostic way. It allows security practitioners to write detection rules once and convert them to various SIEM query languages, solving the problem of fragmented, non-portable detection logic across different security tools.
Detection engineers, threat hunters, SOC analysts, and defensive security practitioners who need to create, share, and implement detection rules across multiple SIEM platforms.
Developers choose Sigma because it provides a unified, open standard for detection rules, enabling collaboration and reuse across the security community without being locked into a specific vendor's query language.
Overview
Main Sigma Rule Repository
Use Cases
Best For
- Converting detection logic between different SIEM systems like Splunk, Elasticsearch, and QRadar
- Sharing threat detection rules with the security community in a standardized format
- Building a repository of reusable, peer-reviewed detection rules for security operations
- Creating vendor-agnostic detection rules for compliance frameworks like CIS or NIST
- Developing emerging threat detections for specific APT campaigns or zero-day exploits
- Automating the conversion of Sigma rules to SIEM-specific queries using tools like pySigma
Not Ideal For
- Teams using a single, proprietary SIEM with no plans to migrate or share rules externally
- Projects requiring instant, out-of-the-box detections without conversion or tuning steps
- Environments where detection logic must remain closed-source due to compliance or secrecy constraints
Pros & Cons
Pros
Rules are written in standardized YAML and can be converted to queries for SIEMs like Splunk, Elasticsearch, and QRadar, reducing vendor lock-in as highlighted in the README's integrations list.
Over 3000 peer-reviewed rules cover detection, hunting, compliance, and emerging threats, providing a rich, cost-free starting point for security teams.
YAML-based format makes rules portable and shareable across reports and communities, fostering collaboration as emphasized in the project philosophy.
Supports multiple use cases including generic detections, threat hunting, and compliance checks, allowing for diverse security operations needs.
Cons
Rules must be converted to SIEM-specific queries using tools like pySigma or CLI, which can introduce errors, require maintenance, and add deployment complexity.
Writing effective rules demands understanding the Sigma specification and log schemas, which can be challenging for those new to detection engineering, despite the provided guides.
With contributions from many users, some rules may have high false positive rates or need environment-specific adjustments, as acknowledged in the false positive reporting section.
Open Source Alternative To
Sigma Rules is an open-source alternative to the following products:
IBM QRadar custom rules are user-defined rules within QRadar that extend its detection capabilities for specific security scenarios.
Splunk Enterprise Security (ES) is Splunk's security information and event management (SIEM) solution for threat detection, investigation, and response.
Frequently Asked Questions
Related Projects
YARAThe pattern matching swiss knife
Elastic Detection RulesDetection Rules is the official repository for the rules used by Elastic Security's Detection Engine. It provides a comprehensive toolkit for security teams to develop, validate, test, and release detection logic in a structured, code-driven manner. This approach enables version control, automated testing, and streamlined integration with the Elastic SIEM. ## Key Features - **Rule Development & Management** — Python module for parsing, validating, and packaging detection rules from TOML files. - **Detections as Code (DaC)** — Full pipeline and CLI tools to manage rules with version control and automated workflows. - **Unit Testing Framework** — Integrated Python testing suite to validate rule logic and ensure reliability. - **Kibana Integration** — Library for API calls to Kibana and the Detection Engine for seamless rule deployment. - **KQL Validation** — Library for parsing and validating Kibana Query Language used in rule conditions. - **Threat Hunting Packages** — Dedicated directory for storing and managing threat hunting queries and packages. ## Philosophy The project embraces a Detections-as-Code philosophy, treating security rules as software artifacts to improve maintainability, collaboration, and automation in threat detection.
KQL Advanced Hunting Queries & Analytics RulesKQL Queries. Defender For Endpoint and Azure Sentinel Hunting and Detection Queries in KQL. Out of the box KQL queries for: Advanced Hunting, Custom Detection, Analytics Rules & Hunting Rules.
Splunk Security ContentSplunk Security Content
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