Performing Cloud Forensics Investigation

---
name: performing-cloud-forensics-investigation
description: Use when conduct forensic investigations in cloud environments by collecting
  and analyzing logs, snapshots, and metadata from AWS, Azure, and GCP services. Use
  when conducting forensic investigations in cloud environments by collecting and
  analyzing.
domain: cybersecurity
tags:
- forensics
- cloud-forensics
- aws
- azure
- gcp
- incident-response
- log-analysis
subdomain: digital-forensics
version: '1.0'
author: oyi77
license: Apache-2.0
nist_csf:
- RS.AN-01
- RS.AN-03
- DE.AE-02
- RS.MA-01
category: cybersecurity
---

# Performing Cloud Forensics Investigation

## Overview

Cybersecurity skill for performing cloud forensics investigation. Follows industry best practices and security standards.

## When to Use
**Trigger phrases:**
- "performing cloud forensics investigation"
- "Conduct forensic investigations in cloud environments by collecting and analyzin"

- When investigating a security breach in AWS, Azure, or GCP cloud environments
- For collecting volatile and non-volatile evidence from cloud infrastructure
- When tracing unauthorized access through cloud service API logs
- During incident response requiring preservation of cloud-based evidence
- For analyzing compromised virtual machines, containers, or serverless functions


## When NOT to Use

- When you lack proper authorization for testing
- For production systems without change management
- When the task requires legal or compliance expertise beyond technical scope


## Prerequisites
- Administrative access to the cloud account under investigation
- AWS CLI, Azure CLI, or gcloud CLI configured with appropriate permissions
- Understanding of cloud-native logging (CloudTrail, Activity Log, Audit Log)
- Forensic workstation with cloud SDKs installed
- Knowledge of IAM, networking, and compute services in target cloud
- Evidence preservation procedures for cloud environments

## Workflow

```python
# Example: IOC detection
import re

IOC_PATTERNS = {
    "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
    "domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
    "hash_md5": r"\b[a-f0-9]{32}\b",
    "hash_sha256": r"\b[a-f0-9]{64}\b",
}

def extract_iocs(text: str) -> dict:
    return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}
```

1. **Plan Operations** — Define objectives, scope, and success criteria for cloud forensics investigation operations.
2. **Prepare Environment** — Set up tools, access, and data sources required for cloud forensics investigation.
3. **Execute Core Workflow** — Perform the cloud forensics investigation operations following established procedures.
4. **Validate Results** — Verify that results meet quality standards and objectives.
5. **Report Findings** — Document results, observations, and recommendations.
6. **Follow Up** — Track remediation actions and verify fixes where applicable.

## Tools

- **Analysis Platform** — Data processing and visualization
- **Collaboration Tools** — Team coordination and knowledge sharing


## Process

1. **Reconnaissance** — Gather target information, identify attack surface, enumerate services
1. **Analysis/Exploitation** — Execute the technique, analyze results, document findings
1. **Reporting** — Document IOCs, write findings, provide remediation recommendations

## Verification

- [ ] All cloud forensics investigation procedures executed completely and documented
- [ ] Findings validated against multiple data sources
- [ ] False positives identified and filtered
- [ ] Results documented with evidence and timestamps
- [ ] Recommendations provided with risk-based prioritization

## Anti-Rationalization Table

| Rationalization | Reality |
|---|---|
| "We are too small to be targeted" | Automated attacks target everyone. Size does not matter. |
| "Security slows us down" | A breach slows you down 100x more. Build security in from the start. |
| "We will fix it after launch" | Vulnerabilities in production are exploited within hours. Fix before deploy. |