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