v1.0 to v1.0
18 added, 7 removed. Audit A to A.
---
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.
+ 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
+ - 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"}}
```