Generative AI for Cybersecurity: Fundamentals, Applications, Risks, and Opportunities

Generative AI is changing both sides of the cybersecurity equation: it can support threat detection and analysis, while also enabling new forms of deception and attack. Generative AI for Cybersecurity takes on that tension directly, pairing an introduction to the underlying models with a structured look at their security applications, risks, and open challenges.

Written by Diep N. Nguyen, Ly Vu, Quang Uy Nguyen, and Dinh Thai Hoang, this first edition moves from shared foundations to specific cybersecurity applications. It is suited to readers who want to understand not only where generative AI may help, but also where its limits and risks demand careful attention.

Start with the models—and the threats

The opening material introduces neural networks and generative models, including variational autoencoders, generative adversarial networks, autoregressive models, normalizing flows, energy-based models, and diffusion models. Alongside this technical groundwork, the book surveys cybersecurity threats, their impacts, and preventive, detective, responsive, and recovery measures.

Where generative AI meets security practice

Later chapters examine representation learning, data synthesis, sequence learning, and adversarial learning in cybersecurity settings. Applications discussed include attack and malware detection, network traffic analysis, phishing and spam detection, and analysis of software logs and system calls. The range of examples helps connect model families and learning approaches to recognizable security problems.

Consider both defense and misuse

The book does not treat generative AI as a one-way defensive advantage. It also addresses security risks such as deepfakes, generated malicious code, adversarial denial-of-service attacks, and AI-assisted social engineering. That balance makes the discussion useful for readers assessing both potential applications and the ways those same capabilities could be abused.

Challenges beyond the algorithms

Practical concerns receive attention too, including training-data availability, computational complexity, generalization across scenarios, and AI governance. Hands-on experiments are included, giving readers a practical component alongside the conceptual and research-focused discussion.

For readers working across AI and cybersecurity

Students, researchers, and industry professionals with an interest in either field will find a bridge between generative AI concepts and cybersecurity use cases. Readers should expect a technical, subject-focused treatment rather than a general introduction to cybersecurity alone.