[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6HMCT2THZZM54W1WHK4H8NF":3,"skill-same:01M6HMCT2THZZM54W1WHK4H8NF":100},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":61,"commitSha":62,"skillDirectory":63,"forksCount":64,"repositoryHtmlUrl":65,"repositoryHomepageUrl":6,"ownerHtmlUrl":66,"ownerBlogUrl":6,"ownerDescription":67,"updatedAt":68,"install":69,"resources":82},"01M6HMCT2THZZM54W1WHK4H8NF","implementing-siem-use-case-tuning",null,"Tune SIEM detection rules to reduce false positives by analyzing alert volumes, creating whitelists, adjusting thresholds, and measuring detection efficacy metrics in Splunk and Elastic","HenriqueMC17\u002Fagente-core","HenriqueMC17","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F206460478?v=4","01M6HB63HBBF18AC4H09XTB549","Henrique Monteiro Cardoso","01M6HB63NGSQXWKWCFWW2B20W0",1,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FHenriqueMC17\u002Fagente-core\u002Fblob\u002Fmain\u002Fmodules\u002Fimplementing-siem-use-case-tuning\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"siem","implementing","case","tuning","implementing siem use case tuning","use","reduce","cybersecurity","ov","thresholds","false","cm","alert","Rules","t1059","t1078","detection-engineering","splunk","soc","security-operations","2026-11-10 10:12:30",false,"---\nname: implementing-siem-use-case-tuning\ndescription: Tune SIEM detection rules to reduce false positives by analyzing alert volumes, creating whitelists, adjusting thresholds, and measuring detection efficacy metrics in Splunk and Elastic\ndomain: cybersecurity\nsubdomain: security-operations\ntags:\n- siem\n- detection-engineering\n- false-positive-reduction\n- splunk\n- elastic\n- alert-tuning\n- soc\nversion: '1.0'\nauthor: mahipal\nlicense: Apache-2.0\nnist_csf:\n- DE.CM-01\n- RS.MA-01\n- GV.OV-01\n- DE.AE-02\nmitre_attack:\n- T1078\n- T1190\n- T1059\n- T1685.002\n- T1685.005\ncategory: implementing-siem-use-case-tuning\n---\n# Implementing SIEM Use Case Tuning\n\n## Overview\n\nSIEM use case tuning reduces alert fatigue by systematically analyzing detection rules for false positive rates, adjusting thresholds based on environmental baselines, creating context-aware whitelists, and measuring detection efficacy through precision\u002Frecall metrics. This skill covers tuning workflows for Splunk correlation searches and Elastic detection rules, including statistical baselining, exclusion list management, and alert-to-incident conversion tracking.\n\n\n## When to Use\n\n- When deploying or configuring implementing siem use case tuning capabilities in your environment\n- When establishing security controls aligned to compliance requirements\n- When building or improving security architecture for this domain\n- When conducting security assessments that require this implementation\n\n## Prerequisites\n\n- Splunk Enterprise\u002FCloud with ES or Elastic SIEM with detection rules enabled\n- Historical alert data (minimum 30 days) for baseline analysis\n- Python 3.8+ with `requests` library\n- SIEM admin credentials or API tokens\n\n## Steps\n\n1. Export current alert volumes per detection rule from SIEM\n2. Calculate false positive rate per rule using analyst disposition data\n3. Identify top noise-generating rules by volume and FP rate\n4. Build environmental baselines for thresholds (e.g., login counts, process spawns)\n5. Create whitelist entries for known-good entities (service accounts, scanners)\n6. Adjust rule thresholds using statistical analysis (mean + N standard deviations)\n7. Measure tuning impact via before\u002Fafter precision and alert-to-incident ratio\n\n## Expected Output\n\nJSON report with per-rule tuning recommendations including current FP rate, suggested threshold adjustments, whitelist entries, and projected alert reduction percentages.\n","# Implementing SIEM Use Case Tuning\n\n## Overview\n\nSIEM use case tuning reduces alert fatigue by systematically analyzing detection rules for false positive rates, adjusting thresholds based on environmental baselines, creating context-aware whitelists, and measuring detection efficacy through precision\u002Frecall metrics. This skill covers tuning workflows for Splunk correlation searches and Elastic detection rules, including statistical baselining, exclusion list management, and alert-to-incident conversion tracking.\n\n\n## When to Use\n\n- When deploying or configuring implementing siem use case tuning capabilities in your environment\n- When establishing security controls aligned to compliance requirements\n- When building or improving security architecture for this domain\n- When conducting security assessments that require this implementation\n\n## Prerequisites\n\n- Splunk Enterprise\u002FCloud with ES or Elastic SIEM with detection rules enabled\n- Historical alert data (minimum 30 days) for baseline analysis\n- Python 3.8+ with `requests` library\n- SIEM admin credentials or API tokens\n\n## Steps\n\n1. Export current alert volumes per detection rule from SIEM\n2. Calculate false positive rate per rule using analyst disposition data\n3. 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Use when...","https:\u002F\u002Fgithub.com\u002FHenriqueMC17\u002Fagente-core\u002Fblob\u002Fmain\u002Fmodules\u002Ftriaging-security-alerts-in-splunk\u002FSKILL.md",[],[164,202,36,203,204,205,26,19,206,207,37,208,34,209,210,211,172,30,128,212],"security alerts","triaging security alerts in splunk","alerts","review","dashboard","search","correlation-search","t1566","notable-events","incident-review","de",{"skillNo":214,"name":215,"nameZh":6,"description":216,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":217,"categories":218,"keywords":219,"publishedAt":39},"01M6HPET6XGEPEJPN7V032JGZ0","tracking-threat-actor-infrastructure","Threat actor infrastructure tracking involves monitoring and mapping adversary-controlled assets including command-and-control (C2) servers, phishing domains, exploit kit hosts, bulletproof hosting, a","https:\u002F\u002Fgithub.com\u002FHenriqueMC17\u002Fagente-core\u002Fblob\u002Fmain\u002Fmodules\u002Ftracking-threat-actor-infrastructure\u002FSKILL.md",[],[220,221,222,223,26,224,225,226,227,228,229,209,230,231,30,232,212,233,234,235],"infrastructure","tracking threat actor infrastructure","tracking","threat","monitoring","mitre-attack","threat-actor","threat-intelligence","command-and-control","dns","command","exploit","intelligence","domains","kit","hosts"]