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name: detecting-shadow-it-cloud-usage description: 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. domain: cybersecurity subdomain: cloud-security tags: [shadow-IT, SaaS-discovery, proxy-logs, DNS-analysis, netflow, cloud-security, pandas] version: "1.0" author: mahipal license: 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

1. Parse proxy access logs and extract destination domains with traffic volumes
2. Parse DNS query logs to identify resolved cloud service domains
3. Aggregate traffic by domain using pandas — total bytes, request counts, unique users
4. Classify domains against known SaaS categories (storage, email, dev tools, AI)
5. Flag unauthorized services not on the approved application list
6. Calculate risk scores based on data volume, user count, and service category
7. 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