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mukul975
915ea611e5
Skills added: - implementing-privileged-access-workstation (IAM, PAW hardening) - detecting-suspicious-oauth-application-consent (cloud security, Graph API) - performing-hardware-security-module-integration (cryptography, PKCS#11) - analyzing-android-malware-with-apktool (malware analysis, androguard) - hunting-for-unusual-service-installations (threat hunting, T1543.003) - detecting-shadow-it-cloud-usage (cloud security, proxy/DNS log analysis) - performing-active-directory-forest-trust-attack (red team, impacket) - implementing-deception-based-detection-with-canarytoken (deception, Canary API) - analyzing-office365-audit-logs-for-compromise (cloud security, BEC detection) - hunting-for-startup-folder-persistence (threat hunting, T1547.001) Each skill includes SKILL.md, LICENSE, scripts/agent.py, references/api-reference.md
1.8 KiB
1.8 KiB
name, description, domain, subdomain, tags, version, author, license
| name | description | domain | subdomain | tags | version | author | license | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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. | cybersecurity | cloud-security |
|
1.0 | mahipal | Apache-2.0 |
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.
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