How do I convert Sigma rules to Splunk queries?

Use the Sigma CLI or pySigma library to convert YAML rules to Splunk's SPL. The README also mentions sigconverter.io for a GUI option and TA-Sigma-Searches as a Splunk app for integration.

What's the difference between Sigma and Snort?

Sigma is for log event detections in SIEM systems, while Snort is for network traffic analysis. Both are open standards, but Sigma focuses on log analytics, as analogized in the README.

Can Sigma rules run directly in Elasticsearch?

No, Sigma rules need conversion to Elasticsearch Query DSL using tools like Sigma CLI or pySigma. The conversion step is required for deployment in any SIEM.

How to write a Sigma rule for Windows event logs?

Follow the Sigma specification and high-level guide to define log conditions in YAML, referencing existing rules in the repository for patterns and using conversion tools to test outputs.

Is Sigma better than native QRadar rule language?

Sigma offers portability and community sharing, but native QRadar rules might be more optimized for that platform. It's a trade-off between flexibility and direct integration, as seen in IBM's recent Sigma support.

How to report a false positive in a Sigma rule?

Create an issue on the GitHub repository using the provided templates, as stated in the 'Reporting False Positives' section, allowing the community to review and improve rules.

Sigma Rules

NOASSERTIONPythonr2026-01-01

A generic and open signature format for describing log event detections, shareable across SIEM systems.

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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.

Target Audience

Detection engineers, threat hunters, SOC analysts, and defensive security practitioners who need to create, share, and implement detection rules across multiple SIEM platforms.

Value Proposition

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

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Community-curated · Updated weekly · 100% open source

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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

Vendor-Agnostic Design

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.

Extensive Community Repository

Over 3000 peer-reviewed rules cover detection, hunting, compliance, and emerging threats, providing a rich, cost-free starting point for security teams.

Easy Rule Sharing

YAML-based format makes rules portable and shareable across reports and communities, fostering collaboration as emphasized in the project philosophy.

Flexible Rule Types

Supports multiple use cases including generic detections, threat hunting, and compliance checks, allowing for diverse security operations needs.

Cons

Conversion Overhead

Rules must be converted to SIEM-specific queries using tools like pySigma or CLI, which can introduce errors, require maintenance, and add deployment complexity.

Steep Learning Curve

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.

Community Rule Variability

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:

M

Microsoft Sentinel custom rules

I

IBM QRadar custom rules

IBM QRadar custom rules are user-defined rules within QRadar that extend its detection capabilities for specific security scenarios.

Splunk ES

Splunk Enterprise Security (ES) is Splunk's security information and event management (SIEM) solution for threat detection, investigation, and response.

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Elastic Detection Rules

Detection 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.

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