Detect abnormal access in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics for after-hours bulk downloads, new-IP access, and API-call spikes (e.g. GetObject) via statistical baselines and time-series anomaly detection. Use when inv

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. Examples CloudTrail S3 Data Event

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