Analyzing cloud storage access patterns

Skill autohandai/community-skills/analyzing-cloud-storage-access-patterns

A collection of curated, useful, and safe skills for Autohand Code CLI Agent

Install

npx -y skills add autohandai/community-skills --skill analyzing-cloud-storage-access-patterns

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What its author says it does

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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.

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SKILL.md

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Instructions

Install dependencies: pip install boto3 requests
Query CloudTrail for S3 Data Events using AWS CLI or boto3.
Build access baselines: hourly request volume, per-user object counts, source IP history.
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)
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