User and Entity Behavior Analytics Foundations for Modern Security

Most security tools watch for known bad activity, but a growing share of incidents involve insider abuse or stolen credentials where the activity looks legitimate. User and Entity Behavior Analytics, often called UEBA, addresses that gap by learning what normal looks like for each user and entity and surfacing deviations. This course gives you a calm, structured introduction. You will learn what UEBA actually monitors, where it fits in the broader security stack, and how machine learning supports the detection of subtle anomalies. The course stays grounded in widely used concepts and points to modern advances reshaping the field. What you'll learn: - Understand what UEBA monitors including users, service accounts, devices, and other entities - Recognize the difference between rule-based detection and behavior-based anomaly detection - Explore the data sources used by UEBA including authentication, access, and activity logs - Read the typical workflow from event collection to baseline modeling to anomaly scoring - Identify the common use cases including insider threats, account compromise, and data exfiltration - Understand the integration points between UEBA and SIEM, IAM, and data loss prevention systems The course begins with the gap UEBA addresses, moves through its data sources, modeling, and use cases, and closes with how it integrates into a broader security program. Written exercises help you connect each concept to a realistic organizational context. This course is designed for absolute beginners with no UEBA or machine learning background, including security analysts, IT administrators, and computer science students. No prerequisites are needed. The course is informational, explains every concept as it appears, and respects the realities of running detection systems in production environments.