---
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: MIT
---

## 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"}}
```