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
70 lines
1.8 KiB
Markdown
70 lines
1.8 KiB
Markdown
# API Reference: Implementing Anti-Phishing Training Program
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## KnowBe4 API
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```python
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import requests
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headers = {"Authorization": "Bearer <API_KEY>"}
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base = "https://us.api.knowbe4.com/v1"
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# List users
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users = requests.get(f"{base}/users", headers=headers).json()
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# Get phishing campaign results
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campaigns = requests.get(f"{base}/phishing/campaigns", headers=headers).json()
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# Get training enrollments
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enrollments = requests.get(f"{base}/training/enrollments", headers=headers).json()
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```
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## Key Metrics
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| Metric | Target | Calculation |
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|--------|--------|-------------|
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| Click Rate | < 15% | Clicked / Total Recipients |
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| Submit Rate | < 5% | Submitted Creds / Total |
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| Report Rate | > 70% | Reported / Total Recipients |
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| Completion Rate | > 90% | Completed / Enrolled |
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## pandas Simulation Analysis
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```python
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import pandas as pd
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df = pd.read_csv("simulation_results.csv", parse_dates=["timestamp"])
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# Department click rates
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dept = df.groupby("department").agg(
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click_rate=("clicked", "mean"),
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report_rate=("reported", "mean"),
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)
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# Monthly trend
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monthly = df.set_index("timestamp").resample("M")["clicked"].mean()
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```
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## SANS Maturity Model Levels
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| Level | Name | Description |
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|-------|------|-------------|
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| 1 | Non-existent | No program |
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| 2 | Compliance | Annual checkbox |
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| 3 | Awareness | Engaging, regular |
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| 4 | Sustainment | Culture change |
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| 5 | Metrics | Risk-based optimization |
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## GoPhish (Open-Source Alternative)
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```bash
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# Launch campaign
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curl -X POST https://gophish:3333/api/campaigns \
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-H "Authorization: <API_KEY>" \
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-d '{"name":"Q1-2025","template":{"name":"IT Alert"},"groups":[{"name":"All Staff"}]}'
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```
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### References
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- KnowBe4 API: https://developer.knowbe4.com/
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- GoPhish: https://getgophish.com/
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- SANS Security Awareness: https://www.sans.org/security-awareness-training/
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