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anxious-phyllo879/anthropic-cybersecurity-skills/analyzing-cloud-storage-access-patterns
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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)
  1. 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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