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mukul975
cb8d79e068
- Add validated mitre_attack frontmatter to all 754 skills (286 distinct techniques), verified against MITRE ATT&CK v19.1 via the official mitreattack-python library: 0 revoked, deprecated, or invalid IDs - Curate precise per-skill technique IDs for forensics, malware-analysis, threat-intel, and red-team skills (e.g. DCSync -> T1003.006, Kerberoasting -> T1558.003, Pass-the-Ticket -> T1550.003) - Reconcile v19.1 tactic restructuring: Defense Evasion split into Stealth (TA0005) and Defense Impairment (TA0112); revoked T1562.* family and T1070.001/.002 remapped to active equivalents (T1685.*) - Normalize word-split tags across 35 skills (remove filename-derived stopword tags, add semantic cybersecurity tags) - Add api-reference.md for 3 skills that were missing it - Update README ATT&CK section with accurate v19.1 tactic distribution
66 lines
2.3 KiB
Markdown
66 lines
2.3 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
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proxy logs, DNS query logs, and netflow data using Python pandas for traffic pattern
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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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mitre_attack:
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- T1567.002
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- T1526
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- T1078.004
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- T1213
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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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