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Mapped every skill to NIST CSF 2.0 subcategory IDs (GV/ID/PR/DE/RS/RC functions) based on subdomain and content analysis. Restores 11 skills corrupted during prior rebase, re-enriching with ATLAS, D3FEND, NIST AI RMF, and CSF 2.0 fields. All 754 skills now carry structured mappings for all 5 security frameworks: - MITRE ATT&CK (in tags) - MITRE ATLAS v5.5 (atlas_techniques) - MITRE D3FEND v1.3 (d3fend_techniques) - NIST AI RMF 1.0 (nist_ai_rmf) - NIST CSF 2.0 (nist_csf)
2.2 KiB
2.2 KiB
name, description, domain, subdomain, tags, version, author, license, nist_csf
| name | description | domain | subdomain | tags | version | author | license | nist_csf | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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.
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