--- name: implementing-siem-use-cases-for-detection-02be5a584da3 description: 'Implements SIEM detection use cases by designing correlation rules, threshold alerts, and behavioral analytics mapped to MITRE ATT&CK techniques across Splunk, Elastic, and Sentinel. Use when SOC teams need to expand detection coverage, formalize use case lifecycle management, or build a detection library aligned to organizational threat profile. ' domain: cybersecurity subdomain: soc-operations tags: - soc - siem - use-cases - detection-engineering - mitre-attack - splunk - elastic - sentinel version: '1.0' author: mahipal license: Apache-2.0 nist_ai_rmf: - MEASURE-2.7 - MAP-5.1 - MANAGE-2.4 atlas_techniques: - AML.T0070 - AML.T0066 - AML.T0082 d3fend_techniques: - Token Binding - Restore Access - Password Authentication - Reissue Credential - Strong Password Policy nist_csf: - DE.CM-01 - DE.AE-02 - RS.MA-01 - DE.AE-06 mitre_attack: - T1078 - T1685.002 - T1685.005 - T1566 - T0816 --- # Implementing SIEM Use Cases for Detection ## When to Use Use this skill when: - SOC teams need to build or expand their SIEM detection library from scratch - Threat assessments identify ATT&CK technique gaps requiring new detection rules - Detection engineers need a structured process for use case design, testing, and deployment - Compliance requirements mandate specific detection capabilities (PCI DSS, HIPAA, SOX) **Do not use** for ad-hoc hunting queries — use cases are formalized, tested, and maintained detection rules, not exploratory searches. ## Prerequisites - SIEM platform (Splunk ES, Elastic Security, or Microsoft Sentinel) with production data - ATT&CK Navigator for coverage gap analysis - Log sources normalized to CIM/ECS field standards - Use case documentation framework (wiki, Git repo, or detection engineering platform) - Testing environment with attack simulation tools (Atomic Red Team, MITRE Caldera) ## Workflow ### Step 1: Assess Detection Coverage Gaps Map current detection rules to ATT&CK and identify gaps: ```python import json # Load current detection rules mapped to ATT&CK current_rules = [ {"name": "Brute Force Detection", "techniques": ["T1110.001", "T1110.003"]}, {"name": "Malware Hash Match", "techniques": ["T1204.002"]}, {"name": "Suspicious PowerShell", "techniques": ["T1059.001"]}, ] # Load ATT&CK Enterprise techniques with open("enterprise-attack.json") as f: attack = json.load(f) all_techniques = set() for obj in attack["objects"]: if obj["type"] == "attack-pattern": ext = obj.get("external_references", []) for ref in ext: if ref.get("source_name") == "mitre-attack": all_techniques.add(ref["external_id"]) covered = set() for rule in current_rules: covered.update(rule["techniques"]) gaps = all_techniques - covered print(f"Total techniques: {len(all_techniques)}") print(f"Covered: {len(covered)} ({len(covered)/len(all_techniques)*100:.1f}%)") print(f"Gaps: {len(gaps)}") # Prioritize gaps by threat relevance priority_techniques = [ "T1003", "T1021", "T1053", "T1547", "T1078", "T1055", "T1071", "T1105", "T1036", "T1070" ] priority_gaps = [t for t in priority_techniques if t in gaps] print(f"Priority gaps: {priority_gaps}") ``` ### Step 2: Design Use Case Specification Document each use case with a standardized template: ```yaml use_case_id: UC-2024-015 name: Credential Dumping via LSASS Access description: Detects tools accessing LSASS process memory for credential extraction mitre_attack: tactic: Credential Access (TA0006) technique: T1003.001 - LSASS Memory data_sources: - Process: OS API Execution (Sysmon EventCode 10) - Process: Process Access (Windows Security 4663) log_sources: - index: sysmon, sourcetype: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational - index: wineventlog, sourcetype: WinEventLog:Security severity: High confidence: Medium-High false_positive_sources: - Antivirus products scanning LSASS - CrowdStrike Falcon sensor - Windows Defender ATP - SCCM client tuning_notes: > Maintain exclusion list for known security tools that legitimately access LSASS. Review exclusions quarterly for newly deployed security products. sla: Alert within 5 minutes of detection owner: detection_engineering_team status: Production created: 2024-03-15 last_tested: 2024-03-15 ``` ### Step 3: Implement Detection Logic Across Platforms **Splunk ES Correlation Search:** ```spl | tstats summariesonly=true count from datamodel=Endpoint.Processes where Processes.process_name="lsass.exe" by Processes.dest, Processes.user, Processes.process_name, Processes.parent_process_name, Processes.parent_process | `drop_dm_object_name(Processes)` | lookup lsass_access_whitelist parent_process AS parent_process OUTPUT is_whitelisted | where isnull(is_whitelisted) OR is_whitelisted!="true" | `credential_dumping_lsass_filter` ``` Or using raw Sysmon data: ```spl index=sysmon EventCode=10 TargetImage="*\\lsass.exe" GrantedAccess IN ("0x1010", "0x1038", "0x1fffff", "0x40") NOT [| inputlookup lsass_whitelist.csv | fields SourceImage] | stats count, values(GrantedAccess) AS access_flags by Computer, SourceImage, SourceUser | where count > 0 ``` **Elastic Security EQL Rule:** ```eql process where event.type == "access" and process.name == "lsass.exe" and not process.executable : ( "?:\\Windows\\System32\\svchost.exe", "?:\\Windows\\System32\\csrss.exe", "?:\\Program Files\\CrowdStrike\\*", "?:\\ProgramData\\Microsoft\\Windows Defender\\*" ) ``` **Microsoft Sentinel KQL Rule:** ```kql DeviceProcessEvents | where Timestamp > ago(1h) | where FileName == "lsass.exe" | where ActionType == "ProcessAccessed" | where InitiatingProcessFileName !in ("svchost.exe", "csrss.exe", "MsMpEng.exe") | project Timestamp, DeviceName, InitiatingProcessFileName, InitiatingProcessCommandLine, AccountName ``` ### Step 4: Test with Attack Simulation Validate detection rules using Atomic Red Team: ```bash # Install Atomic Red Team IEX (IWR 'https://raw.githubusercontent.com/redcanaryco/invoke-atomicredteam/master/install-atomicredteam.ps1' -UseBasicParsing) Install-AtomicRedTeam -getAtomics # Execute T1003.001 - Credential Dumping Invoke-AtomicTest T1003.001 -TestNumbers 1,2,3 # Execute T1053.005 - Scheduled Task Invoke-AtomicTest T1053.005 -TestNumbers 1 # Execute T1547.001 - Registry Run Key Invoke-AtomicTest T1547.001 -TestNumbers 1,2 ``` Verify detection in SIEM: ```spl index=sysmon EventCode=10 TargetImage="*\\lsass.exe" earliest=-1h | stats count by Computer, SourceImage, GrantedAccess | where count > 0 ``` Document test results: ``` TEST RESULTS — UC-2024-015 Atomic Test T1003.001-1 (Mimikatz): DETECTED (alert fired in 47s) Atomic Test T1003.001-2 (ProcDump): DETECTED (alert fired in 32s) Atomic Test T1003.001-3 (Task Manager): FALSE NEGATIVE (excluded by whitelist — expected) False Positive Rate (7-day backtest): 2 events (CrowdStrike scan — added to whitelist) ``` ### Step 5: Deploy and Monitor Use Case Health Track detection rule effectiveness: ```spl -- Use case firing frequency index=notable | stats count AS fires, dc(src) AS unique_sources, dc(dest) AS unique_dests by rule_name, status_label | eval true_positive_rate = round( sum(eval(if(status_label="Resolved - True Positive", 1, 0))) / count * 100, 1) | sort - fires | table rule_name, fires, unique_sources, unique_dests, true_positive_rate -- Detection latency monitoring index=notable | eval detection_latency = _time - orig_time | stats avg(detection_latency) AS avg_latency_sec, perc95(detection_latency) AS p95_latency_sec by rule_name | eval avg_latency_min = round(avg_latency_sec / 60, 1) | sort - avg_latency_sec ``` ### Step 6: Maintain Use Case Library Establish lifecycle management for all detection use cases: ``` USE CASE LIFECYCLE ━━━━━━━━━━━━━━━━━━ 1. PROPOSED → New detection need identified (threat intel, gap analysis, incident finding) 2. DEVELOPMENT → Query written, false positive analysis, tuning 3. TESTING → Atomic Red Team validation, 7-day backtest 4. STAGING → Deployed in alert-only mode (no incident creation) for 14 days 5. PRODUCTION → Full production with incident creation and SOAR integration 6. REVIEW → Quarterly review of effectiveness, false positive rate, relevance 7. DEPRECATED → Technique no longer relevant or replaced by better detection ``` ## Key Concepts | Term | Definition | |------|-----------| | **Use Case** | Formalized detection rule with documented logic, testing, tuning, and lifecycle management | | **Detection Engineering** | Practice of designing, testing, and maintaining SIEM detection rules as a software development discipline | | **Correlation Search** | SIEM query that combines events from multiple sources to identify attack patterns | | **False Positive Rate** | Percentage of alerts that are benign activity — target <20% for production use cases | | **Detection Latency** | Time between event occurrence and alert generation — target <5 minutes for critical detections | | **ATT&CK Coverage** | Percentage of relevant ATT&CK techniques with at least one production detection rule | ## Tools & Systems - **Splunk ES**: Enterprise SIEM with correlation searches, risk-based alerting, and Incident Review - **Elastic Security**: SIEM with detection rules, EQL sequences, and ML-based anomaly detection - **Microsoft Sentinel**: Cloud SIEM with KQL analytics rules, Fusion ML engine, and Lighthouse multi-tenant - **Atomic Red Team**: Open-source attack simulation framework for testing detection rules against ATT&CK techniques - **ATT&CK Navigator**: MITRE visualization tool for mapping and tracking detection coverage across techniques ## Common Scenarios - **Post-Incident Use Case**: After a ransomware incident, build detection for the initial access vector discovered during investigation - **Compliance-Driven**: PCI DSS requires detection of admin account misuse — build use cases for 4672/4720/4732 events - **Threat-Intel Driven**: New APT group targets your sector — build use cases for their documented TTPs - **Red Team Findings**: Purple team exercise identifies blind spots — convert findings into production detection rules - **SIEM Migration**: Migrating from QRadar to Splunk — convert and validate all existing use cases on new platform ## Output Format ``` USE CASE DEPLOYMENT REPORT ━━━━━━━━━━━━━━━━━━━━━━━━━ Quarter: Q1 2024 Total Use Cases: 147 (Production: 128, Staging: 12, Development: 7) New Deployments This Quarter: UC-2024-012 Kerberoasting Detection (T1558.003) — Production UC-2024-013 DLL Side-Loading (T1574.002) — Production UC-2024-014 Scheduled Task Persistence (T1053.005) — Production UC-2024-015 LSASS Memory Access (T1003.001) — Staging ATT&CK Coverage: Overall: 67% of relevant techniques (up from 61%) Initial Access: 78% Execution: 82% Persistence: 71% Credential Access: 65% Lateral Movement: 58% (priority gap area) Health Metrics: Avg True Positive Rate: 74% (target: >70%) Avg Detection Latency: 2.3 min (target: <5 min) Use Cases Deprecated: 3 (replaced by improved versions) ``` --- Source attribution: costrict-plugins-repo Original project: https://github.com/costrict-plugins-repo/mukul975-anthropic-cybersecurity-skills-cybersecurity-skills/blob/1b344d155f00bae5cf27d8eba9508e8f5050d904/skills/implementing-siem-use-cases-for-detection/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Dave AI. ## 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. # Dave AI resource metadata Creator / publisher: costrict-plugins-repo Original project: https://github.com/costrict-plugins-repo/mukul975-anthropic-cybersecurity-skills-cybersecurity-skills/blob/1b344d155f00bae5cf27d8eba9508e8f5050d904/skills/implementing-siem-use-cases-for-detection/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering, Operations Roles: Business Process Analyst, Data Analyst, ML / AI Engineer, Operations Manager, Security Engineer Specialties: API Tools: Tool not specified Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 0946d04d7899560c4e6a2164175a2956dbd5541fe01a55712042ea97333cdea7