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
64 lines
2.5 KiB
Markdown
64 lines
2.5 KiB
Markdown
# API Reference: SOC Tabletop Exercise Agent
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## Overview
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Manages SOC tabletop exercise lifecycle: scenario generation from templates, participant tracking, inject delivery, response scoring, and after-action report generation.
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## Dependencies
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| Package | Version | Purpose |
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|---------|---------|---------|
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| json | stdlib | Report serialization |
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| datetime | stdlib | Exercise scheduling and IDs |
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## Core Functions
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### `create_exercise(scenario_type, participants, duration_hours=3)`
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Creates a structured tabletop exercise from a scenario template.
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- **Parameters**: `scenario_type` (str) - one of `ransomware`, `data_breach`, `supply_chain`; `participants` (list[dict]) - role/count pairs
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- **Returns**: `dict` - full exercise object with phases and objectives
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### `score_response(category, score)`
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Scores participant response in a specific evaluation category.
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- **Parameters**: `category` (str) - one of `detection_and_triage`, `containment_decision`, `communication`, `business_continuity`; `score` (int) - 0-100
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- **Returns**: `dict` - category, score, rating, weight
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### `calculate_overall_score(scores)`
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Computes weighted average across all scored categories.
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- **Parameters**: `scores` (list[dict]) - output from `score_response`
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- **Returns**: `float` - overall score
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### `generate_after_action_report(exercise, scores, gaps, strengths)`
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Produces the formal after-action report document.
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- **Parameters**: `exercise` (dict), `scores` (list), `gaps` (list[dict]), `strengths` (list[str])
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- **Returns**: `dict` - AAR with scores, findings, and next exercise date
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## Scenario Templates
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| Template | Phases | Focus Areas |
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|----------|--------|-------------|
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| `ransomware` | 6 injects | Detection, containment, ransom decision, recovery |
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| `data_breach` | 4 injects | DLP, insider threat, PII notification |
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| `supply_chain` | 4 injects | Vendor compromise, lateral movement, credential reset |
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## Scoring Criteria
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| Category | Weight | Rating Thresholds |
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|----------|--------|-------------------|
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| detection_and_triage | 25% | >=85 Excellent, >=70 Good, >=55 Adequate |
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| containment_decision | 25% | >=85 Excellent, >=70 Good, >=55 Adequate |
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| communication | 25% | >=85 Excellent, >=70 Good, >=55 Adequate |
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| business_continuity | 25% | >=85 Excellent, >=70 Good, >=55 Adequate |
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## Output Schema
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```json
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{
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"exercise_id": "TTX-2026-Q1",
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"overall_score": "72/100 (Adequate)",
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"scores": {"detection_and_triage": "85/100 (Excellent)"},
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"gaps": [{"finding": "...", "risk": "High", "owner": "SOC Manager"}],
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"strengths": ["Ransomware indicators correctly identified"]
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}
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```
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