detecting-shadow-it-cloud-usage

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
nist_csf	["PR.IR-01","ID.AM-08","GV.SC-06","DE.CM-01"]
mitre_attack	["T1567.002","T1526","T1078.004","T1213"]
source	https://github.com/mukul975/Anthropic-Cybersecurity-Skills
source_commit	04450304b12645cb2b974ab96d28c0664758a88d

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

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