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
50 lines
2.0 KiB
Markdown
50 lines
2.0 KiB
Markdown
# API Reference: Implementing Alert Fatigue Reduction
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## Libraries
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### splunk-sdk (Splunk SDK for Python)
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- **Install**: `pip install splunk-sdk`
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- **Docs**: https://dev.splunk.com/enterprise/docs/devtools/python/sdk-python/
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- `splunklib.client.connect(host, port, username, password)` -- Connect to Splunk
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- `service.jobs.create(query)` -- Execute a search query
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- `job.is_done()` -- Check if search job completed
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- `job.results(output_mode="json")` -- Retrieve results in JSON format
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- `splunklib.results.JSONResultsReader(stream)` -- Parse JSON results
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### Splunk ES Notable Events API
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- **Endpoint**: `/services/notable_update`
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- **Methods**: POST to update notable event status
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- **Fields**: `status`, `urgency`, `owner`, `comment`, `ruleUIDs`
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- **Status values**: `0` (Unassigned), `1` (New), `2` (In Progress), `5` (Resolved)
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## Key SPL Queries
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| Purpose | Key Functions |
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|---------|--------------|
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| Alert volume analysis | `stats count by rule_name`, `eval fp_rate` |
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| Risk-based alerting | `collect index=risk`, `eval risk_score` |
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| Alert consolidation | `dedup src, rule_name span=300` |
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| Capacity calculation | `bin _time span=1d`, `stats avg(daily_alerts)` |
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| Tiered routing | `eval routing = case(urgency, ...)` |
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## Risk-Based Alerting (RBA) Framework
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- Risk contributions replace individual alerts
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- `index=risk` stores cumulative risk scores per entity
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- Threshold alert fires only when `total_risk >= 75`
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- Typical risk score ranges: 5 (low) to 50 (critical)
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## Metrics Targets
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| Metric | Target |
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|--------|--------|
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| False Positive Rate | < 30% per production rule |
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| Alerts/Analyst/Shift | 40-60 (manageable range) |
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| Signal-to-Noise Ratio | > 1.0 |
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| MTTD | Under 15 minutes for critical |
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| MTTR | Under 4 hours for high severity |
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## External References
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- Splunk ES RBA Docs: https://docs.splunk.com/Documentation/ES/latest/Admin/RBA
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- Splunk SDK Python: https://github.com/splunk/splunk-sdk-python
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- MITRE ATT&CK Detection: https://attack.mitre.org/resources/
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