analyzing-cloud-storage-access-patterns
This Claude Code skill provides procedures and Python automation for detecting abnormal access patterns in AWS S3, Google Cloud Storage, and Azure Blob Storage by analyzing CloudTrail logs and access baselines. Use it when investigating security incidents, building threat detection rules, or validating SOC monitoring coverage for cloud storage threats like unauthorized data exfiltration and reconnaissance activities.
git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills /tmp/analyzing-cloud-storage-access-patterns && cp -r /tmp/analyzing-cloud-storage-access-patterns/skills/analyzing-cloud-storage-access-patterns ~/.claude/skills/analyzing-cloud-storage-access-patternsSKILL.md
# 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.
```bash
python scripts/agent.py --bucket my-sensitive-data --hours-back 24 --output s3_access_report.json
```
## Examples
### CloudTrail S3 Data Event
```json
{"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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