---
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."
license: "Apache-2.0"
metadata:
  decimalai-skill-id: "c37f98df-a610-418d-a096-609f5fbb7a45"
  decimalai-version-id: "0af6d79f-eeb7-4ddb-8bf6-d1f2cd07abf3"
  decimalai-version: "1"
  decimalai-body-sha256: "dc1d612b896c200dba3fd18348946dd3c2d22f1add640275e53de57a00eb0ee1"
  decimalai-name: "analyzing-cloud-storage-access-patterns"
  decimalai-format: "1"
  decimalai-invocation: "model"
  decimalai-category: "security"
  decimalai-source: "https://app.decimal.ai/s/analyzing-cloud-storage-access-patterns@1/SKILL.md"
---
# 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"}}
```