Analyzing Cloud Storage Access Patterns

---
name: analyzing-cloud-storage-access-patterns
description: tespit etme
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 Tespit.
tags:
- storage
- analyzing
- access
- fetih
- cloud-security
- cybersecurity
- siber-güvenlik
- cloud
triggers:
- AWS
- Azure
- GCP
- access
- analyzing
- bulut güvenliği
- cloud
- cloud security
- incident
- patterns
- storage
- threat
category: cloud-security
source_subdomain: cloud-security
nist_csf:
- PR.IR-01
- ID.AM-08
- GV.SC-06
- DE.CM-01
adapted_for: fetih
---

# Analyzing Cloud Storage Access Patterns


## Ne Zaman Kullanılır

- investigating yaparken security incidents that require analyzing cloud storage access patterns
- building yaparken Tespit rules or threat hunting queries for this domain
- SOC yaparken: analysts need structured procedures for this analysis type
- validating yaparken security monitoring coverage for related attack techniques

## Ön Gereksinimler

- Familiarity with cloud security concepts and tools
- Erişim: 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. tespit etme
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 Bul:ings report.

```bash
python scripts/agent.py --bucket my-sensitive-data --hours-back 24 --output s3_access_report.json
```

## Örnekler

### 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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