| 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 | ["analyzing","cloud","storage","access"] |
| version | 1.0 |
| author | mahipal |
| license | Apache-2.0 |
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"}}
