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analyzing-cloud-storage-access-patterns

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更新日2026年6月19日 06:44

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.

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kaviyarasu2007

kaviyarasu2007/Master_

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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

Install dependencies: pip install boto3 requests
Query CloudTrail for S3 Data Events using AWS CLI or boto3.
Build access baselines: hourly request volume, per-user object counts, source IP history.
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)
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"}}

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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	["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

Install dependencies: pip install boto3 requests
Query CloudTrail for S3 Data Events using AWS CLI or boto3.
Build access baselines: hourly request volume, per-user object counts, source IP history.
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)
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"}}