Analyzing Cloud Storage Access PatternsSAFE

817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 29 security domains ·

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
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"}}
```

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

What the source does

Filesystem

none-observed

Network

declared (1 observation(s))

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (1)

"indicator": "Bulk download (potential exfiltration)",

Gates applied: no_behavioural_pass.