/analyzing-cloud-storage-access-patterns

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: 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

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.

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

Examples

CloudTrail S3 Data Event

{"eventName": "GetObject", "requestParameters": {"bucketName": "sensitive-data", "key": "financials/q4.xlsx"},
 "sourceIPAddress": "203.0.113.50", "userIdentity": {"arn": "arn:aws:iam::123456789012:user/analyst"}}