Simple definition
A vendor-neutral rule format used to describe log-based detections that can be converted into platform-specific query languages.
Sigma helps detection engineers express the core analytic idea independently of one SIEM syntax. The final production detection still depends on the target platform's field mappings, query semantics, scheduling, correlation capabilities, and available telemetry.
What does a Sigma rule contain?
Metadata
Title, description, status, author, references, tags, and identifiers describe the analytic.
Log source
Indicates the product, category, service, or event context the rule expects.
Selections
Define field-value patterns or event characteristics that represent suspicious behavior.
Condition
Combines selections and filters into the rule's matching logic.
Filters
Exclude known benign patterns or narrow the analytic.
Tags
Associate rules with ATT&CK techniques, tactics, products, or other classification.
Why Sigma rules matter
- Detection teams can document analytic intent without locking every rule to one vendor query language.
- Sigma repositories provide a common format for sharing and reviewing log-based detection ideas.
- Portable rule descriptions can accelerate SIEM migrations and multi-platform detection programs.
- Rule metadata and ATT&CK tags make detection content easier to organize and govern.
- Conversion still depends on correct field mappings and log-source availability in the target platform.
- Production quality requires target-specific testing, tuning, scheduling, severity, and operational workflow.
How a Sigma rule works
Define the analytic hypothesis
Describe the suspicious behavior and expected telemetry.
Choose the log source
Identify the operating system, application, product, or event category required by the rule.
Write selections and conditions
Express suspicious field values, combinations, keywords, filters, and logical relationships.
Convert for the target
Translate the Sigma rule into the target SIEM or query language using the appropriate field mappings.
Validate against data
Run the converted logic against representative true-positive and benign events.
Operationalize and maintain
Add scheduling, severity, alert context, tuning, ownership, and regression testing.
Sigma rule quality considerations
Data availability
The target environment must collect the log source and fields referenced by the rule.
Field mapping
Generic or source fields need correct translation into the target schema.
Query semantics
Different query languages may interpret wildcards, case, lists, regex, and aggregation differently.
False positives
Portable logic still needs environment-specific exclusions and tuning.
ATT&CK mapping
Tags can connect the analytic to tactics and techniques for coverage analysis.
Validation
Generated queries should be tested rather than accepted simply because conversion succeeded.
Common Sigma rule use cases
Detection sharing
Exchange analytic ideas between teams and security communities.
SIEM migration
Translate detection content when moving between analytics platforms.
Multi-SIEM programs
Maintain a common detection source with platform-specific outputs.
Rule repositories
Store detection content in version control with metadata and review history.
ATT&CK coverage
Tag rules to support technique-level detection inventories.
Detection engineering
Use Sigma as a structured starting point before target-specific tuning.
Sigma Rule vs Detection Rule vs YARA Rule
| Rule type | Primary purpose | Typical data |
|---|---|---|
| Sigma Rule | Describe vendor-neutral log-based detection logic. | Security events, logs, audit records, and normalized event fields. |
| Detection Rule | Platform-specific analytic that generates alerts or findings. | Whatever telemetry and query model the target SIEM/XDR supports. |
| YARA Rule | Describe patterns used to identify files, memory content, malware families, or other byte/text artifacts. | File content, strings, binary structures, memory, and related artifacts. |
Key technical considerations
Map fields deliberately
Conversion is only as accurate as the target field mapping.
Test generated queries
Validate logic on real target-platform events and benign baselines.
Version rule content
Treat Sigma YAML and generated platform outputs as maintained engineering assets.
Document limitations
Record missing fields, unsupported operators, platform deviations, and required enrichments.
Control duplicates
Multiple converted detections should not create unnecessary duplicate alerts.
Add operational metadata
Production rules still need severity, ownership, scheduling, suppression, and response context.
Benefits and common problems
Benefits
- Vendor-neutral detection description.
- Easier rule sharing and review.
- Useful foundation for SIEM migration.
- Version-control friendly detection content.
- Consistent metadata and ATT&CK tagging.
Common problems
- Assuming conversion guarantees identical platform behavior.
- Missing target fields or incompatible schemas.
- Little environment-specific tuning.
- Unsupported aggregation or correlation semantics.
- Generated queries deployed without validation.
How ForshTec helps with Sigma and detection engineering
ForshTec helps security teams turn portable detection ideas into tested production analytics across SIEM and XDR platforms.
- Sigma rule authoring and review
- Platform query conversion and adaptation
- Target-schema field mapping
- ATT&CK tagging and detection coverage
- True-positive and false-positive validation
- SIEM migration and detection-content modernization
Need portable detections converted into production-ready SIEM rules?
ForshTec can take Sigma content through field mapping, platform conversion, testing, tuning, and operational deployment.
Talk to ForshTecFAQs
What is a Sigma rule?
A Sigma rule is a vendor-neutral structured format for describing log-based detection logic that can be translated into platform-specific queries.
What format are Sigma rules written in?
Sigma rules are commonly represented as YAML with metadata, log-source information, detection selections, conditions, and tags.
Does a Sigma rule work directly in every SIEM?
Not usually. The rule is converted or adapted into the target platform's query language and field schema.
Why must converted Sigma rules be tested?
Different platforms have different field names, query semantics, data availability, and operator behavior, so conversion alone does not prove equivalent detection.
How are Sigma rules related to MITRE ATT&CK?
Sigma metadata can include ATT&CK tactic and technique tags to help organize detection content and measure coverage.
How is Sigma different from YARA?
Sigma primarily describes log-event detections, while YARA is commonly used to match patterns in files, memory, malware, and other content artifacts.