Understanding Generative AI Technologies

This chapter establishes the technical foundations of generative AI, essential for understanding its security implications. Beginning with machine learning fundamentals, supervised, unsupervised, and reinforcement learning (RL) , it progresses through deep learning architectures including artificial neural networks (ANNs) , convolutional neural networks (CNNs), and recurrent neural networks (RNNs) . The chapter explores primary generative models (GANs , VAEs , and Transformers) with practical cybersecurity applications for each, demonstrating how these technologies serve as both defensive tools and potential attack vectors. Advanced topics include natural language processing (NLP) capabilities that enable sophisticated text generation and computer vision systems that create and analyze visual content. Through concrete examples, from phishing email detection to network traffic analysis, the chapter bridges theoretical concepts with real-world security scenarios. This technical grounding is vital for security professionals to effectively implement AI-powered tools, identify potential vulnerabilities, and develop appropriate defensive strategies against increasingly sophisticated AI-enabled threats.