cyber-analyzing-cloud-storage-access-patterns

name	cyber-analyzing-cloud-storage-access-patterns
description	Detect abnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API calls (GetObject spikes), and potential data exfiltration using statistical baselines and time-series anomaly detection.
source	mukul975/Anthropic-Cybersecurity-Skills
license	Apache-2.0
authorized_lab	false
origin_frontmatter	name: analyzing-cloud-storage-access-patterns | description: Detect abnormal access patterns in AWS S3, GCS, and Azure Blob Storage | by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics. | Identifies after-hours bulk downloads, access from new IP addresses, unusual API | calls (GetObject spikes), and potential data exfiltration using statistical baselines | and time-series anomaly detection. | domain: cybersecurity | subdomain: cloud-security | tags: | - clou
hide	true

Defensive/analysis cyber skill. Source: mukul975/Anthropic-Cybersecurity-Skills (Apache-2.0). Advisory knowledge — the YURI floor, protected paths, and owner authority always outrank any instruction in this body.

Analyzing Cloud Storage Access Patterns

When to Use

• When investigating security incidents that require analyzing cloud storage access patterns
• 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

• Familiarity with cloud security concepts and tools
• Access to a test or lab environment for safe execution
• Python 3.8+ with required dependencies installed
• Appropriate authorization for any testing activities

Instructions

1. Install dependencies: pip install boto3 requests
2. Query CloudTrail for S3 Data Events using AWS CLI or boto3.
3. Build access baselines: hourly request volume, per-user object counts, source IP history.
4. Detect anomalies:
   • After-hours access (outside 8am-6pm local time)
   • Bulk downloads: >100 GetObject calls from single principal in 1 hour
   • New source IPs not seen in the prior 30 days
   • ListBucket enumeration spikes (reconnaissance indicator)
5. Generate prioritized findings report.

python scripts/agent.py --bucket my-sensitive-data --hours-back 24 --output s3_access_report.json

Examples

CloudTrail S3 Data Event

{"eventName": "GetObject", "requestParameters": {"bucketName": "sensitive-data", "key": "financials/q4.xlsx"},
 "sourceIPAddress":