/detection-sigma
Generic detection rule creation and management using Sigma, the universal SIEM rule format. Sigma provides vendor-agnostic detection logic for log analysis across multiple SIEM platforms. Use when: (1) Creating detection rules for security monitoring, (2) Converting rules
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Generic detection rule creation and management using Sigma, the universal SIEM rule format. Sigma provides vendor-agnostic detection logic for log analysis across multiple SIEM platforms. Use when: (1) Creating detection rules for security monitoring, (2) Converting rules
SKILL.md
detection-sigma.SKILL.mdname: detection-sigma
description: >
Generic detection rule creation and management using Sigma, the universal SIEM rule format.
Sigma provides vendor-agnostic detection logic for log analysis across multiple SIEM platforms.
Use when: (1) Creating detection rules for security monitoring, (2) Converting rules between
SIEM platforms (Splunk, Elastic, QRadar, Sentinel), (3) Threat hunting with standardized
detection patterns, (4) Building detection-as-code pipelines, (5) Mapping detections to
MITRE ATT&CK tactics, (6) Implementing compliance-based monitoring rules.
version: 0.1.0
maintainer: SirAppSec
category: incident-response
tags: [sigma, detection, siem, threat-hunting, mitre-attack, detection-engineering, log-analysis]
frameworks: [MITRE-ATT&CK, NIST, ISO27001]
dependencies:
python: ">=3.8"
packages: [pysigma, pysigma-backend-splunk, pysigma-backend-elasticsearch, pyyaml]
references:
- https://github.com/SigmaHQ/sigma
- https://github.com/SigmaHQ/pySigma
- https://sigmahq.io/
Sigma Detection Engineering
Overview
Sigma is to log detection what Snort is to network traffic and YARA is to files - a universal signature format for describing security-relevant log events. This skill helps create, validate, and convert Sigma rules for deployment across multiple SIEM platforms, enabling detection-as-code workflows.
**Core capabilities**:
- Create detection rules using Sigma format
- Convert rules to 25+ SIEM/EDR backends (Splunk, Elastic, QRadar, Sentinel, etc.)
- Validate rule syntax and logic
- Map detections to MITRE ATT&CK framework
- Build threat hunting queries
- Implement compliance-based monitoring
Quick Start
Install Dependencies
pip install pysigma pysigma-backend-splunk pysigma-backend-elasticsearch pyyaml
Create a Basic Sigma Rule
title: Suspicious PowerShell Execution
id: 7d6d30b8-5b91-4b90-a71e-4f5a3f5a3c3f
status: experimental
description: Detects suspicious PowerShell execution with encoded commands
references:
- https://attack.mitre.org/techniques/T1059/001/
author: Your Name
date: YYYY/MM/DD
modified: YYYY/MM/DD
tags:
- attack.execution
- attack.t1059.001
logsource:
category: process_creation
product: windows
detection:
selection:
Image|endswith: '\powershell.exe'
CommandLine|contains:
- '-enc'
- '-EncodedCommand'
- 'FromBase64String'
condition: selection
falsepositives:
- Legitimate administrative scripts
level: mediumConvert Rule to Target SIEM
# Convert to Splunk
python scripts/sigma_convert.py rule.yml --backend splunk
# Convert to Elasticsearch
python scripts/sigma_convert.py rule.yml --backend elasticsearch
# Convert to Microsoft Sentinel
python scripts/sigma_convert.py rule.yml --backend sentinel
Core Workflows
Workflow 1: Detection Rule Development
Progress: [ ] 1. Identify detection requirement from threat intelligence or compliance [ ] 2. Research log sources and field mappings for target environment [ ] 3. Create Sigma rule using standard template [ ] 4. Validate rule syntax: `python scripts/sigma_validate.py rule.yml` [ ] 5. Test rule against sample logs or historical data [ ] 6. Convert to target SIEM format [ ] 7. Deploy and tune based on false positive rate [ ] 8. Document rule metadata and MITRE ATT&CK mapping
Work through each step systematically. Check off completed items.
Workflow 2: Threat Hunting Rule Creation
For proactive threat hunting based on TTPs:
1. **Select MITRE ATT&CK Technique**
- Review threat intelligence for relevant TTPs
- Identify technique ID (e.g., T1059.001 - PowerShell)
- See [references/mitre-attack-mapping.md](references/mitre-attack-mapping.md) for common techniques
2. **Identify Log Sources**
- Determine which logs capture the technique
- Map log source categories (process_creation, network_connection, file_event)
- Verify log source availability in your environment
3. **Define Detection Logic**
- Create selection criteria matching suspicious patterns
- Add filters to reduce false positives
- Use field modifiers for robust matching (endswith, contains, re)
4. **Validate and Test**
- Run validation: `python scripts/sigma_validate.py hunting-rule.yml`
- Test against known-good and known-bad samples
- Tune detection logic based on results
5. **Document and Deploy**
- Add references to threat reports
- Document false positive scenarios
- Convert and deploy to production SIEM
Workflow 3: Bulk Rule Conversion
When migrating between SIEM platforms:
# Validate all rules first
python scripts/sigma_validate.py --directory rules/ --report validation-report.json
# Convert entire rule set
python scripts/sigma_convert.py --directory rules/ --backend splunk --output converted/
# Generate deployment report
python scripts/sigma_convert.py --directory rules/ --backend splunk --report conversion-report.md
Review conversion report for:
- Successfully converted rules
- Rules requiring manual adjustment
- Unsupported field mappings
- Backend-specific limitations
Workflow 4: Compliance-Based Detection
For implementing compliance monitoring (PCI-DSS, NIST, ISO 27001):
1. **Map Requirements to Detections**
- Identify compliance control requirements
- Determine required log monitoring
- See [references/compliance-mappings.md](references/compliance-mappings.md)
2. **Create Detection Rules**
- Use compliance rule templates from `assets/compliance-rules/`
- Tag rules with compliance framework (e.g., tags: [pci-dss.10.2.5])
- Set appropriate severity levels
3. **Validate Coverage**
- Run: `python scripts/compliance_coverage.py --framework pci-dss`
- Review coverage gaps
- Create additional rules as needed
4. **Generate Compliance Report**
- Document detection coverage by control
- Include sample queries and expected alerts
- Maintain audit trail fo
Read more
name: detection-sigma description: > Generic detection rule creation and management using Sigma, the universal SIEM rule format. Sigma provides vendor-agnostic detection logic for log analysis across multiple SIEM platforms. Use when: (1) Creating detection rules for security monitoring, (2) Converting rules between SIEM platforms (Splunk, Elastic, QRadar, Sentinel), (3) Threat hunting with standardized detection patterns, (4) Building detection-as-code pipelines, (5) Mapping detections to MITRE ATT&CK tactics, (6) Implementing compliance-based monitoring rules. version: 0.1.0 maintainer: SirAppSec category: incident-response tags: [sigma, detection, siem, threat-hunting, mitre-attack, detection-engineering, log-analysis] frameworks: [MITRE-ATT&CK, NIST, ISO27001] dependencies: python: ">=3.8" packages: [pysigma, pysigma-backend-splunk, pysigma-backend-elasticsearch, pyyaml] references: - https://github.com/SigmaHQ/sigma - https://github.com/SigmaHQ/pySigma - https://sigmahq.io/
Sigma Detection Engineering
Overview
Sigma is to log detection what Snort is to network traffic and YARA is to files - a universal signature format for describing security-relevant log events. This skill helps create, validate, and convert Sigma rules for deployment across multiple SIEM platforms, enabling detection-as-code workflows.
**Core capabilities**:
- Create detection rules using Sigma format
- Convert rules to 25+ SIEM/EDR backends (Splunk, Elastic, QRadar, Sentinel, etc.)
- Validate rule syntax and logic
- Map detections to MITRE ATT&CK framework
- Build threat hunting queries
- Implement compliance-based monitoring
Quick Start
Install Dependencies
pip install pysigma pysigma-backend-splunk pysigma-backend-elasticsearch pyyaml
Create a Basic Sigma Rule
title: Suspicious PowerShell Execution
id: 7d6d30b8-5b91-4b90-a71e-4f5a3f5a3c3f
status: experimental
description: Detects suspicious PowerShell execution with encoded commands
references:
- https://attack.mitre.org/techniques/T1059/001/
author: Your Name
date: YYYY/MM/DD
modified: YYYY/MM/DD
tags:
- attack.execution
- attack.t1059.001
logsource:
category: process_creation
product: windows
detection:
selection:
Image|endswith: '\powershell.exe'
CommandLine|contains:
- '-enc'
- '-EncodedCommand'
- 'FromBase64String'
condition: selection
falsepositives:
- Legitimate administrative scripts
level: mediumConvert Rule to Target SIEM
# Convert to Splunk python scripts/sigma_convert.py rule.yml --backend splunk # Convert to Elasticsearch python scripts/sigma_convert.py rule.yml --backend elasticsearch # Convert to Microsoft Sentinel python scripts/sigma_convert.py rule.yml --backend sentinel
Core Workflows
Workflow 1: Detection Rule Development
Progress: [ ] 1. Identify detection requirement from threat intelligence or compliance [ ] 2. Research log sources and field mappings for target environment [ ] 3. Create Sigma rule using standard template [ ] 4. Validate rule syntax: `python scripts/sigma_validate.py rule.yml` [ ] 5. Test rule against sample logs or historical data [ ] 6. Convert to target SIEM format [ ] 7. Deploy and tune based on false positive rate [ ] 8. Document rule metadata and MITRE ATT&CK mapping
Work through each step systematically. Check off completed items.
Workflow 2: Threat Hunting Rule Creation
For proactive threat hunting based on TTPs:
1. **Select MITRE ATT&CK Technique**
- Review threat intelligence for relevant TTPs
- Identify technique ID (e.g., T1059.001 - PowerShell)
- See [references/mitre-attack-mapping.md](references/mitre-attack-mapping.md) for common techniques
2. **Identify Log Sources**
- Determine which logs capture the technique
- Map log source categories (process_creation, network_connection, file_event)
- Verify log source availability in your environment
3. **Define Detection Logic**
- Create selection criteria matching suspicious patterns
- Add filters to reduce false positives
- Use field modifiers for robust matching (endswith, contains, re)
4. **Validate and Test**
- Run validation: `python scripts/sigma_validate.py hunting-rule.yml`
- Test against known-good and known-bad samples
- Tune detection logic based on results
5. **Document and Deploy**
- Add references to threat reports
- Document false positive scenarios
- Convert and deploy to production SIEM
Workflow 3: Bulk Rule Conversion
When migrating between SIEM platforms:
# Validate all rules first python scripts/sigma_validate.py --directory rules/ --report validation-report.json # Convert entire rule set python scripts/sigma_convert.py --directory rules/ --backend splunk --output converted/ # Generate deployment report python scripts/sigma_convert.py --directory rules/ --backend splunk --report conversion-report.md
Review conversion report for:
- Successfully converted rules
- Rules requiring manual adjustment
- Unsupported field mappings
- Backend-specific limitations
Workflow 4: Compliance-Based Detection
For implementing compliance monitoring (PCI-DSS, NIST, ISO 27001):
1. **Map Requirements to Detections**
- Identify compliance control requirements
- Determine required log monitoring
- See [references/compliance-mappings.md](references/compliance-mappings.md)
2. **Create Detection Rules**
- Use compliance rule templates from `assets/compliance-rules/`
- Tag rules with compliance framework (e.g., tags: [pci-dss.10.2.5])
- Set appropriate severity levels
3. **Validate Coverage**
- Run: `python scripts/compliance_coverage.py --framework pci-dss`
- Review coverage gaps
- Create additional rules as needed
4. **Generate Compliance Report**
- Document detection coverage by control
- Include sample queries and expected alerts
- Maintain audit trail fo
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