analyzing-cloud-storage-access-patterns
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 investigating suspected cloud data exfiltration or building related detection rules.
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소스 지침 · 읽기 전용 미리보기- name
- analyzing-cloud-storage-access-patterns
- description
- 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 investigating suspected cloud data exfiltration or building related detection rules.
- domain
- cybersecurity
- subdomain
- cloud-security
- tags
- ["cloud-security","aws-s3","gcs","azure-blob-storage","cloudtrail","data-access-anomaly","exfiltration-detection"]
- version
- 1.0
- author
- mahipal
- license
- Apache-2.0
- atlas_techniques
- ["AML.T0024","AML.T0056"]
- nist_ai_rmf
- ["MEASURE-2.7","MAP-5.1","MANAGE-2.4"]
- nist_csf
- ["PR.IR-01","ID.AM-08","GV.SC-06","DE.CM-01"]
- mitre_attack
- ["T1530","T1567.002","T1619","T1078.004","T1048"]
# 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.
```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"}}
```
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