mukul975/analyzing-cloud-storage-access-patterns
skills/analyzing-cloud-storage-access-patterns/SKILL.md
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.
$ install --global
skillsauthnpx skillsauth add mukul975/anthropic-cybersecurity-skills analyzing-cloud-storage-access-patternsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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TrivyContainer and dependency vulnerability scanner
95%
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mcp-scan (Snyk)Model Context Protocol security validation
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Snyk (dep)Open source security scanning
50%
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Socket.devSupply chain security analysis
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Last scanned: Jun 2, 2026, 3:46 AM220.4s3 files scanned
SKILL.md
- name:
- analyzing-cloud-storage-access-patterns
- description:
- Detect abnormal access patterns in AWS S3, GCS, and Azure Blob Storage
- domain:
- cybersecurity
- subdomain:
- cloud-security
- version:
- 1.0
- author:
- mahipal
- license:
- Apache-2.0
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
- 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
{"eventName": "GetObject", "requestParameters": {"bucketName": "sensitive-data", "key": "financials/q4.xlsx"},
"sourceIPAddress": "203.0.113.50", "userIdentity": {"arn": "arn:aws:iam::123456789012:user/analyst"}}
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