Detecting Compromised Cloud Credentials

---
name: detecting-compromised-cloud-credentials
description: Use when detecting compromised cloud credentials across AWS, Azure, and
  GCP by analyzing anomalous API activity, impossible travel patterns, unauthorized
  resource provisioning, and credential abuse indicators using GuardDuty, Defender
  for Identity, and SCC Event Threat Detection. . Use when working with detecting
  compromised cloud credentials.
domain: cybersecurity
tags:
- cloud-security
- credential-compromise
- threat-detection
- guardduty
- incident-response
- anomaly-detection
subdomain: cloud-security
version: '1.0'
author: oyi77
license: Apache-2.0
nist_csf:
- PR.IR-01
- ID.AM-08
- GV.SC-06
- DE.CM-01
category: cybersecurity
---

# Detecting Compromised Cloud Credentials

## Overview

Cybersecurity skill for detecting compromised cloud credentials. Follows industry best practices and security standards.

## When to Use
**Trigger phrases:**
- "detecting compromised cloud credentials"
- "Detecting compromised cloud credentials across AWS, Azure, and GCP by analyzing "


- When investigating alerts about unusual cloud API activity from unfamiliar locations
- When building detection rules for credential theft and abuse across cloud environments
- When responding to notifications from cloud providers about exposed credentials
- When monitoring for credential stuffing or brute force attacks against cloud identities
- When assessing the scope of a credential compromise after initial detection

**Do not use** for preventing credential compromise (use MFA, credential rotation, and secrets management), for detecting application-level credential theft (use application security monitoring), or for endpoint credential harvesting detection (use EDR tools).


## 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

- AWS GuardDuty enabled across all accounts and regions
- Azure Defender for Identity and Entra ID Protection configured
- GCP Security Command Center with Event Threat Detection enabled
- CloudTrail, Azure Activity Log, and GCP Audit Log centralized for analysis
- SIEM integration for cross-cloud correlation of credential abuse indicators
- Threat intelligence feeds for known malicious IP ranges

## 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. **Define Detection Scope** — Identify the specific compromised cloud credentials techniques or indicators to hunt. Map to MITRE ATT&CK tactics/techniques where applicable.
2. **Collect Baseline Data** — Gather historical logs and establish normal behavior patterns for compromised cloud credentials.
3. **Build Detection Queries** — Write detection rules, Sigma rules, or SIEM queries targeting compromised cloud credentials indicators.
4. **Execute Hunts** — Run queries against the collected data, starting with broad filters and narrowing down.
5. **Triage Results** — Investigate alerts, filter false positives, and validate findings against known-good behavior.
6. **Document Findings** — Record confirmed detections, IOCs, and affected systems. Update detection rules based on findings.

## Tools

- **SIEM Platform** — Central log aggregation and query execution
- **Sigma Rules** — Vendor-agnostic detection rule format
- **MITRE ATT&CK Navigator** — Technique mapping and coverage analysis


## 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 compromised cloud credentials 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. |