This skill is designed for security professionals and SOC analysts who need to analyze access patterns in cloud storage environments such as AWS S3, Google Cloud Storage (GCS), and Azure Blob Storage. It leverages data from CloudTrail, GCS audit logs, and Azure Storage Analytics to identify abnormal access behaviors that could indicate security incidents, such as data exfiltration attempts. By applying statistical baselines and time-series anomaly detection, users can effectively monitor and investigate suspicious activities.

The skill provides structured procedures to help users build access baselines based on hourly request volumes, user-specific object access counts, and historical source IP data. It identifies key anomalies like after-hours access, bulk downloads exceeding predefined thresholds, and new IP addresses that haven't been seen in the last 30 days. Additionally, it can flag spikes in ListBucket API calls, which may suggest reconnaissance activities.

To use this skill, users must be familiar with cloud security concepts and have the necessary permissions to access and analyze data in their cloud environments. The skill requires Python 3.8 or higher and specific dependencies to be installed. Once set up, users can execute the provided Python script to generate a prioritized report of findings, making it easier to respond to potential threats effectively.

This skill is particularly useful for organizations that require robust monitoring of their cloud storage access patterns to ensure data security and compliance. It aids in building detection rules and enhances threat-hunting capabilities in cloud environments.