mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
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24f816b6a3
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):
- brain/ LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
dispatch, A51 channels, and the OSINT cluster
- knowledge/ LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
MITRE ATT&CK data
- reference/ defensive threat-reference (C3, shhbruh doc) + AdaYaml parser
License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.
Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.
https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
59 lines
2.3 KiB
Markdown
59 lines
2.3 KiB
Markdown
---
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name: implementing-siem-use-case-tuning
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description: Tune SIEM detection rules to reduce false positives by analyzing alert volumes, creating whitelists, adjusting
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thresholds, and measuring detection efficacy metrics in Splunk and Elastic
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domain: cybersecurity
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subdomain: security-operations
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tags:
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- siem
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- detection-engineering
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- false-positive-reduction
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- splunk
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- elastic
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- alert-tuning
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- soc
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version: '1.0'
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author: mahipal
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license: Apache-2.0
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nist_csf:
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- DE.CM-01
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- RS.MA-01
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- GV.OV-01
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- DE.AE-02
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---
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# Implementing SIEM Use Case Tuning
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## Overview
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SIEM 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/recall 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.
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## When to Use
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- When deploying or configuring implementing siem use case tuning capabilities in your environment
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- When establishing security controls aligned to compliance requirements
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- When building or improving security architecture for this domain
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- When conducting security assessments that require this implementation
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## Prerequisites
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- Splunk Enterprise/Cloud with ES or Elastic SIEM with detection rules enabled
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- Historical alert data (minimum 30 days) for baseline analysis
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- Python 3.8+ with `requests` library
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- SIEM admin credentials or API tokens
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## Steps
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1. Export current alert volumes per detection rule from SIEM
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2. Calculate false positive rate per rule using analyst disposition data
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3. Identify top noise-generating rules by volume and FP rate
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4. Build environmental baselines for thresholds (e.g., login counts, process spawns)
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5. Create whitelist entries for known-good entities (service accounts, scanners)
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6. Adjust rule thresholds using statistical analysis (mean + N standard deviations)
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7. Measure tuning impact via before/after precision and alert-to-incident ratio
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## Expected Output
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JSON report with per-rule tuning recommendations including current FP rate, suggested threshold adjustments, whitelist entries, and projected alert reduction percentages.
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