Detecting Compromised Cloud Credentials

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

# 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
2. Analysis/Exploitation — Execute the technique, analyze results, document findings
3. 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