mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
synced 2026-08-01 08:30:20 +00:00
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
54 lines
1.4 KiB
Markdown
54 lines
1.4 KiB
Markdown
# Workflows: Detecting BEC with AI
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## Workflow 1: AI-Powered BEC Detection Pipeline
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```
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Inbound email arrives
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v
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[Feature extraction]
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+-- Sender metadata (domain, IP, authentication)
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+-- Email content (subject, body, NLP features)
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+-- Behavioral context (communication history, timing)
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+-- Relationship graph (sender-recipient pattern)
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v
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[Multi-model analysis (parallel)]
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+-- Impostor classifier: Display name/domain impersonation
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+-- NLP model: Writing style vs. sender baseline
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+-- Behavioral model: Request anomaly detection
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+-- Intent classifier: Payment/credential/data request
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[Confidence scoring]
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+-- Aggregate model outputs
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+-- Weight by model confidence and context
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+-- Generate overall BEC probability score
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[Action]
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+-- Score >= 90%: Auto-quarantine + SOC alert
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+-- Score 70-89%: Warning banner + analyst queue
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+-- Score 50-69%: Warning banner only
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+-- Score < 50%: Deliver normally
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```
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## Workflow 2: Model Feedback Loop
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```
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BEC verdict generated
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[User/analyst feedback]
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+-- User reports false positive (legitimate email flagged)
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+-- Analyst confirms true positive (BEC caught)
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+-- User reports missed BEC (false negative)
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v
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[Feedback integration]
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+-- Update sender trust score
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+-- Retrain model with corrected labels
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+-- Adjust confidence thresholds
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+-- Update behavioral baselines
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```
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