--- name: building-detection-rules-with-sigma-e9f69ba92923 description: > Builds vendor-agnostic detection rules using the Sigma rule format for threat detection across SIEM platforms including Splunk, Elastic, and Microsoft Sentinel. Use when creating portable detection logic from threat intelligence, mapping rules to MITRE ATT&CK techniques, or converting community Sigma rules into platform-specific queries using sigmac or pySigma backends. domain: cybersecurity subdomain: soc-operations tags: [soc, sigma, detection-rules, siem, mitre-attack, splunk, elastic, sentinel] version: "1.0" author: mahipal license: Apache-2.0 --- # Building Detection Rules with Sigma ## When to Use Use this skill when: - SOC engineers need to create detection rules portable across multiple SIEM platforms - Threat intelligence reports describe TTPs requiring new detection coverage - Existing vendor-specific rules need standardization into a shareable format - The team adopts Sigma as a detection-as-code standard in CI/CD pipelines **Do not use** for real-time streaming detection (Sigma is for batch/scheduled searches) or when the target SIEM has native detection features that Sigma cannot express (e.g., Splunk RBA risk scoring). ## Prerequisites - Python 3.8+ with `pySigma` and appropriate backend (`pySigma-backend-splunk`, `pySigma-backend-elasticsearch`, `pySigma-backend-microsoft365defender`) - Sigma rule repository cloned: `git clone https://github.com/SigmaHQ/sigma.git` - MITRE ATT&CK framework knowledge for technique mapping - Understanding of target SIEM log source field mappings ## Workflow ### Step 1: Define Detection Logic from Threat Intelligence Start with a threat report or ATT&CK technique. Example: detecting Mimikatz credential dumping (T1003.001 — LSASS Memory): ```yaml title: Mimikatz Credential Dumping via LSASS Access id: 0d894093-71bc-43c3-8d63-bf520e73a7c5 status: stable level: high description: Detects process accessing lsass.exe memory, indicative of credential dumping tools like Mimikatz references: - https://attack.mitre.org/techniques/T1003/001/ - https://github.com/gentilkiwi/mimikatz author: mahipal date: 2024/03/15 modified: 2024/03/15 tags: - attack.credential_access - attack.t1003.001 logsource: category: process_access product: windows detection: selection: TargetImage|endswith: '\lsass.exe' GrantedAccess|contains: - '0x1010' - '0x1038' - '0x1fffff' - '0x40' filter_main_svchost: SourceImage|endswith: '\svchost.exe' filter_main_csrss: SourceImage|endswith: '\csrss.exe' filter_main_wininit: SourceImage|endswith: '\wininit.exe' condition: selection and not 1 of filter_main_* falsepositives: - Legitimate security tools accessing LSASS - Windows Defender scanning - CrowdStrike Falcon sensor ``` ### Step 2: Validate Sigma Rule Syntax Use `sigma check` to validate the rule: ```bash # Install pySigma and validators pip install pySigma pySigma-validators-sigmaHQ # Validate rule sigma check rule.yml ``` Alternatively, validate with Python: ```python from sigma.rule import SigmaRule from sigma.validators.core import SigmaValidator rule = SigmaRule.from_yaml(open("rule.yml").read()) validator = SigmaValidator() issues = validator.validate_rule(rule) for issue in issues: print(f"{issue.severity}: {issue.message}") ``` ### Step 3: Convert to Target SIEM Query **Convert to Splunk SPL:** ```python from sigma.rule import SigmaRule from sigma.backends.splunk import SplunkBackend from sigma.pipelines.splunk import splunk_windows_pipeline pipeline = splunk_windows_pipeline() backend = SplunkBackend(pipeline) rule = SigmaRule.from_yaml(open("rule.yml").read()) splunk_query = backend.convert_rule(rule) print(splunk_query[0]) ``` Output: ```spl TargetImage="*\\lsass.exe" (GrantedAccess="*0x1010*" OR GrantedAccess="*0x1038*" OR GrantedAccess="*0x1fffff*" OR GrantedAccess="*0x40*") NOT (SourceImage="*\\svchost.exe") NOT (SourceImage="*\\csrss.exe") NOT (SourceImage="*\\wininit.exe") ``` **Convert to Elastic Query (Lucene):** ```python from sigma.backends.elasticsearch import LuceneBackend from sigma.pipelines.elasticsearch import ecs_windows_pipeline pipeline = ecs_windows_pipeline() backend = LuceneBackend(pipeline) elastic_query = backend.convert_rule(rule) print(elastic_query[0]) ``` **Convert to Microsoft Sentinel KQL:** ```python from sigma.backends.microsoft365defender import Microsoft365DefenderBackend backend = Microsoft365DefenderBackend() kql_query = backend.convert_rule(rule) print(kql_query[0]) ``` ### Step 4: Map to MITRE ATT&CK and Add Coverage Metadata Tag every rule with ATT&CK technique IDs in the `tags` field: ```yaml tags: - attack.credential_access # Tactic - attack.t1003.001 # Sub-technique - attack.t1003 # Parent technique ``` Track detection coverage using the ATT&CK Navigator: ```python import json # Generate ATT&CK Navigator layer from Sigma rules layer = { "name": "SOC Detection Coverage", "versions": {"attack": "14", "navigator": "4.9", "layer": "4.5"}, "domain": "enterprise-attack", "techniques": [] } # Parse Sigma rules directory for technique tags import os from sigma.rule import SigmaRule for root, dirs, files in os.walk("sigma/rules/windows/"): for f in files: if f.endswith(".yml"): rule = SigmaRule.from_yaml(open(os.path.join(root, f)).read()) for tag in rule.tags: if str(tag).startswith("attack.t"): technique_id = str(tag).replace("attack.", "").upper() layer["techniques"].append({ "techniqueID": technique_id, "color": "#31a354", "score": 1 }) with open("coverage_layer.json", "w") as f: json.dump(layer, f, indent=2) ``` ### Step 5: Test Rule Against Sample Data Create test data and validate the rule catches the expected events: ```bash # Use sigma test framework sigma test rule.yml --target splunk --pipeline splunk_windows # Or manually test in Splunk with sample data # Upload Sysmon process_access log with known Mimikatz signature ``` Validate false positive rate by running against 7 days of production data in a non-alerting saved search. ### Step 6: Deploy to Production SIEM Deploy the converted query as a scheduled search or correlation rule: **Splunk ES Correlation Search:** ```spl | tstats summariesonly=true count from datamodel=Endpoint.Processes where Processes.process_name="*\\lsass.exe" by Processes.src, Processes.user, Processes.process_name, Processes.parent_process_name | `drop_dm_object_name(Processes)` | where count > 0 ``` **Elastic Security Rule (TOML format):** ```toml [rule] name = "LSASS Memory Access - Credential Dumping" description = "Detects suspicious access to LSASS process memory" risk_score = 73 severity = "high" type = "eql" query = ''' process where event.action == "access" and process.name == "lsass.exe" and not process.executable : ("*\\svchost.exe", "*\\csrss.exe") ''' [rule.threat] framework = "MITRE ATT&CK" [[rule.threat.technique]] id = "T1003" name = "OS Credential Dumping" ``` ### Step 7: Version Control and CI/CD Integration Store rules in Git with automated testing: ```yaml # .github/workflows/sigma-ci.yml name: Sigma Rule CI on: [push, pull_request] jobs: validate: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: actions/setup-python@v5 with: python-version: '3.11' - run: pip install pySigma pySigma-validators-sigmaHQ - run: sigma check rules/ - run: sigma convert -t splunk -p splunk_windows rules/ > /dev/null ``` ## Key Concepts | Term | Definition | |------|-----------| | **Sigma** | Vendor-agnostic detection rule format (YAML-based) that compiles to SIEM-specific queries via backends | | **pySigma** | Python library replacing legacy sigmac for rule conversion, validation, and pipeline processing | | **Backend** | pySigma plugin that translates Sigma detection logic into a target platform query language (SPL, KQL, Lucene) | | **Pipeline** | Field mapping configuration that translates generic Sigma field names to SIEM-specific field names | | **Logsource** | Sigma rule section defining the category (process_creation, network_connection) and product (windows, linux) of the target data | | **Detection-as-Code** | Practice of managing detection rules in version control with CI/CD testing and automated deployment | ## Tools & Systems - **SigmaHQ**: Official Sigma rule repository with 3,000+ community-maintained detection rules on GitHub - **pySigma**: Python-based Sigma rule processing framework with modular backends and pipelines - **ATT&CK Navigator**: MITRE tool for visualizing detection coverage mapped to ATT&CK techniques - **Uncoder.IO**: Web-based Sigma rule converter supporting 30+ SIEM platforms for quick translation ## Common Scenarios - **New CVE Detection**: Write Sigma rule for exploitation indicators (e.g., Log4Shell JNDI lookup patterns in web logs) - **Hunting Rule Promotion**: Convert ad-hoc Splunk hunting query into Sigma rule for ongoing automated detection - **Multi-SIEM Migration**: Converting 500+ Splunk correlation searches to Sigma for migration to Elastic Security - **Purple Team Output**: Convert red team findings into Sigma rules for immediate defensive coverage - **Threat Intel Operationalization**: Transform IOC-based threat reports into behavioral Sigma rules ## Output Format ``` SIGMA RULE DEPLOYMENT REPORT ━━━━━━━━━━━━━━━━━━━━━━━━━━━ Rule ID: 0d894093-71bc-43c3-8d63-bf520e73a7c5 Title: Mimikatz Credential Dumping via LSASS Access ATT&CK: T1003.001 - LSASS Memory Severity: High Status: Deployed to Production Conversions: Splunk SPL: PASS — Saved search "sigma_lsass_access" created Elastic EQL: PASS — Detection rule ID elastic-0d894093 enabled Sentinel KQL: PASS — Analytics rule deployed via ARM template Testing: True Positives: 4/4 test cases matched False Positives: 2 in 7-day backtest (svchost edge case — filter added) Performance: Avg execution 3.2s on 50M events/day ``` --- Source attribution: luokai0 Original project: https://github.com/luokai0/ai-agent-skills-by-luo-kai/blob/4d2fb89cdfb75f3f6fd3c6c5ec9ad90ea129eb3c/ai-agent-skills/21-external-registries/08-pinkpixel-collection/building-detection-rules-with-sigma/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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See the License for the specific language governing permissions and limitations under the License. # Mega AI Hub resource metadata Creator / publisher: luokai0 Original project: https://github.com/luokai0/ai-agent-skills-by-luo-kai/blob/4d2fb89cdfb75f3f6fd3c6c5ec9ad90ea129eb3c/ai-agent-skills/21-external-registries/08-pinkpixel-collection/building-detection-rules-with-sigma/SKILL.md Attribution basis: Source repository record Fields of work: Development & Engineering Roles: Security Engineer Specialties: API Tools: Pysigma, Sigma Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 30ca1815d167658728577fb91d9d216b28a0239fba4163890f17dc7172b1d0c5