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mukul975
39 lines
1.4 KiB
Markdown
39 lines
1.4 KiB
Markdown
---
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name: analyzing-cloud-storage-access-patterns
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description: >-
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Detect abnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail
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Data Events, GCS audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads,
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access from new IP addresses, unusual API calls (GetObject spikes), and potential data exfiltration
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using statistical baselines and time-series anomaly detection.
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domain: cybersecurity
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subdomain: cloud-security
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tags: [analyzing, cloud, storage, access]
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version: "1.0"
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author: mahipal
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license: MIT
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---
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## Instructions
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1. Install dependencies: `pip install boto3 requests`
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2. Query CloudTrail for S3 Data Events using AWS CLI or boto3.
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3. Build access baselines: hourly request volume, per-user object counts, source IP history.
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4. Detect anomalies:
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- After-hours access (outside 8am-6pm local time)
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- Bulk downloads: >100 GetObject calls from single principal in 1 hour
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- New source IPs not seen in the prior 30 days
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- ListBucket enumeration spikes (reconnaissance indicator)
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5. Generate prioritized findings report.
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```bash
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python scripts/agent.py --bucket my-sensitive-data --hours-back 24 --output s3_access_report.json
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```
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## Examples
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### CloudTrail S3 Data Event
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```json
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{"eventName": "GetObject", "requestParameters": {"bucketName": "sensitive-data", "key": "financials/q4.xlsx"},
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"sourceIPAddress": "203.0.113.50", "userIdentity": {"arn": "arn:aws:iam::123456789012:user/analyst"}}
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```
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