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
60 lines
2.2 KiB
Markdown
60 lines
2.2 KiB
Markdown
---
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name: detecting-shadow-it-cloud-usage
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description: Detect unauthorized SaaS and cloud service usage (shadow IT) by analyzing proxy logs, DNS query logs, and netflow
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data using Python pandas for traffic pattern analysis and domain classification.
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domain: cybersecurity
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subdomain: cloud-security
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tags:
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- shadow-IT
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- SaaS-discovery
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- proxy-logs
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- DNS-analysis
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- netflow
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- cloud-security
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- pandas
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version: '1.0'
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author: mahipal
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license: Apache-2.0
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nist_csf:
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- PR.IR-01
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- ID.AM-08
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- GV.SC-06
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- DE.CM-01
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---
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# Detecting Shadow IT Cloud Usage
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## Overview
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Shadow IT refers to unauthorized SaaS applications and cloud services used without IT approval. This skill analyzes proxy logs, DNS query logs, and firewall/netflow data to identify unauthorized cloud service usage, classify discovered domains against known SaaS categories, measure data transfer volumes, and flag high-risk services based on security posture and compliance requirements.
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## When to Use
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- When investigating security incidents that require detecting shadow it cloud usage
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- When building detection rules or threat hunting queries for this domain
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- When SOC analysts need structured procedures for this analysis type
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- When validating security monitoring coverage for related attack techniques
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## Prerequisites
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- Python 3.9+ with `pandas`, `tldextract`
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- Proxy logs (Squid, Zscaler, or Palo Alto format) or DNS query logs
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- SaaS application catalog/blocklist for classification
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- Network firewall logs with FQDN resolution (optional)
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## Steps
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1. Parse proxy access logs and extract destination domains with traffic volumes
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2. Parse DNS query logs to identify resolved cloud service domains
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3. Aggregate traffic by domain using pandas — total bytes, request counts, unique users
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4. Classify domains against known SaaS categories (storage, email, dev tools, AI)
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5. Flag unauthorized services not on the approved application list
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6. Calculate risk scores based on data volume, user count, and service category
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7. Generate shadow IT discovery report with remediation recommendations
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## Expected Output
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- JSON report listing discovered cloud services with traffic volumes, user counts, risk scores, and approval status
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- Top unauthorized services ranked by data exfiltration risk
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