- Course
- Security
Behavioral Analysis and Anomaly Detection
This course will provide an overview about the application of AI within behavioral analysis and anomaly detection, which can be used to detect undetected threats within a business environment.
What you'll learn
Identifying security threats in real-time is increasingly complex as environments grow more dynamic and data-driven. In this course, Behavioral Analysis and Anomaly Detection, you’ll gain the ability to apply AI to detect suspicious behavior and anomalies that signal potential threats. First, you’ll explore the fundamentals of distinguishing normal behavior from activity that indicates risk. Next, you’ll discover how AI models—such as time-series analysis, clustering, and autoencoders—are used to identify anomalies. Then, you’ll learn how these methods help detect threats like privileged account abuse. Finally, you’ll see how AI-enhanced detection works alongside traditional SIEM solutions like Splunk and Elasticsearch. When you’re finished with this course, you’ll have the skills and knowledge to leverage AI-driven behavioral analysis and anomaly detection as part of a modern cybersecurity strategy.
Table of contents
About the author
Ryan Smith is a cybersecurity engineer and the founder of QFunction, which is a consultancy that aims to help businesses with their cybersecurity needs.