Skill UI

This skill analyzes AWS CloudTrail logs to detect suspicious API call patterns, such as new event sources, geographic anomalies, or high-frequency usage. It establishes a statistical baseline of normal activity and flags deviations indicative of compromised credentials, privilege escalation, or insider threats, providing a detailed JSON report for security investigation and threat hunting.

This skill analyzes AWS IAM policies using boto3 and Cloudsplaining principles to detect potential privilege escalation paths in AWS accounts. It identifies overly permissive policies, dangerous permission combinations (such as combining role passing and resource creation), and violations of least-privilege principles. It is designed for security incident investigation, threat hunting, and validating overall cloud security posture.

This skill guides the implementation of security chaos engineering experiments. It involves deliberately degrading or disabling security controls (like WAF, firewalls, or log pipelines) in a controlled environment to rigorously verify the detection, alerting, and response capabilities of the Security Operations Center (SOC). Users learn to use tools like boto3 and Python to simulate failure scenarios and assess system resilience and compliance posture.

This tool provides a comprehensive workflow for performing deep forensic investigations on AWS environments. By leveraging CloudTrail logs and programmatic access via boto3/Athena, users can reconstruct attacker activity, identify compromised credentials, analyze suspicious API call patterns, and generate detailed incident response reports.

This skill uses Python and boto3 to analyze AWS CloudTrail logs. It establishes statistical baselines of normal API activity and detects various anomalies, such as unusual event sources, first-time API usage, geographic IP deviations, and high-frequency calls. This is critical for identifying credential compromise, privilege escalation, and unauthorized resource access in a security incident.