AI Red Teaming Mastery: LLM Security, Defense & Pentesting

In this lesson, we introduce AI red teaming from a practical security perspective. You’ll learn how AI systems differ from traditional software, why probabilistic behavior creates new attack surfaces, and how attackers can exploit trust, prompts, and model behavior instead of code alone. We also look at how AI security fits into real-world threat testing and why traditional pentesting methods are no longer enough on their own. By the end, you’ll have a clear picture of what AI red teaming means and why it matters.

This lesson gives you the technical foundation you need before testing AI systems. You’ll learn what AI actually is, how machine learning works under the hood, and why data, training, and evaluation create new security concerns. We’ll break down the learning process in simple terms and show how model behavior can be influenced, manipulated, or misunderstood. This is the groundwork for every later section in the course, so you can think like a security professional instead of treating AI like a black box.

This lesson explains machine learning in a simple, intuitive way. You’ll see how models learn from examples, how they can generalize to new data, and how generative AI creates new outputs from learned patterns. We also explore common machine learning types, including supervised and unsupervised learning, and show how these ideas show up in everyday tools like keyboards, recommendations, and spam filters. By the end, you’ll understand the core logic behind modern AI systems and why that matters for security work.

In this lesson, you’ll learn how to recognize the four major machine learning problem types: classification, regression, clustering, and anomaly detection. We’ll walk through each one with clear examples so you can understand when and why to use them. You’ll also learn the difference between labeled and unlabeled data, as well as supervised, unsupervised, and reinforcement learning. This section helps you think more clearly about AI systems and prepares you for the hands-on models later in the course.

This lesson takes you from setup to your first working model. You’ll install the core Python libraries used in machine learning, learn what each one does, and use Jupyter Notebook to write and run code step by step. We’ll build a basic regression model, make predictions, and visualize the results so you can understand the full workflow. By the end, you won’t just know the tools - you’ll have used them to create a real machine learning project from scratch.

This is your first real AI security project. In this lesson, you’ll build a spam filter that classifies email messages as spam or legitimate. You’ll learn how to turn text into numerical features, train classic machine learning models, and compare their performance on real data. We’ll also explore how perceptron, SVM, logistic regression, decision trees, and Naive Bayes approach the same problem differently. By the end, you’ll have built a working spam detection system and gained a practical understanding of text classification in cybersecurity.

In this lesson, you’ll go deeper into spam detection by comparing several classic machine learning algorithms on the same dataset. You’ll see how each model behaves, where it performs well, and why some approaches are better suited for text classification than others. This lesson also reinforces the importance of preprocessing, splitting data correctly, and evaluating real model performance. By the end, you’ll understand not just how to build a spam detector, but how to think critically about model choice in AI security.