Extract DPAPI-protected secrets such as credentials and browser data offline and online.
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andycungkrinx91/konoha92026년 10월 19일 업데이트
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detecting-shadow-it-cloud-usage
Detect unauthorized SaaS and cloud service usage (shadow IT) by analyzing proxy logs, DNS query logs, and netflow data using Python pandas for traffic pattern analysis and domain classification.
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설치 방법 보기포함된 파일(3)
- SKILL.md2.3 KB
- references/api-reference.md2.3 KB
- scripts/agent.py10.8 KB
SKILL.md(원문)
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Detecting Shadow IT Cloud Usage
Overview
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.
When to Use
- When investigating security incidents that require detecting shadow it cloud usage
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques
Prerequisites
- Python 3.9+ with
pandas,tldextract - Proxy logs (Squid, Zscaler, or Palo Alto format) or DNS query logs
- SaaS application catalog/blocklist for classification
- Network firewall logs with FQDN resolution (optional)
Steps
- Parse proxy access logs and extract destination domains with traffic volumes
- Parse DNS query logs to identify resolved cloud service domains
- Aggregate traffic by domain using pandas — total bytes, request counts, unique users
- Classify domains against known SaaS categories (storage, email, dev tools, AI)
- Flag unauthorized services not on the approved application list
- Calculate risk scores based on data volume, user count, and service category
- Generate shadow IT discovery report with remediation recommendations
Expected Output
- JSON report listing discovered cloud services with traffic volumes, user counts, risk scores, and approval status
- Top unauthorized services ranked by data exfiltration risk
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