Generative Artificial Intelligence (AI) for Cybersecurity

1. Introduction

Generative artificial intelligence (AI), including large language models (LLMs), generative adversarial networks, autoencoders, and related generative learning methods, is increasingly shaping cybersecurity research and practice. These technologies are no longer limited to content generation or general-purpose automation: they are increasingly being explored as tools for threat detection, cyber threat intelligence, security testing, anomaly detection, privacy-preserving monitoring, education, and decision support. At the same time, generative AI also introduces new security challenges, including prompt attacks, AI-assisted malicious activities, adversarial manipulation, model misuse, and new forms of automated cyber threats.

This Special Issue, “Generative Artificial Intelligence (AI) for Cybersecurity”, was organized to capture this important and fast-moving research direction. Its goal was to bring together original studies that examine both the opportunities and risks associated with generative AI in cybersecurity. The papers in this collection demonstrate that generative AI is not a single solution or a narrow technical trend; rather, it is becoming a broad enabling technology across multiple layers of cybersecurity, from data generation and threat simulation to model robustness, intelligence acquisition, human education, and operational defense.

2. Contributions

The contributions in this Special Issue address several important themes. One theme is the use of advanced AI and generative models to improve cyber threat detection and defense. This includes early ransomware detection using deep learning models [1], generative AI-assisted web application firewall protection against SQL injection attacks [2], feature-regularized GANs for addressing class imbalance in network intrusion detection [3], and generative sequence models for privacy-preserving insider threat detection [4]. These studies reflect a shared need in modern cybersecurity: detecting complex and evolving threats under practical constraints such as limited labeled data, imbalanced datasets, privacy requirements, and the need for timely response.

A second theme concerns the role of LLMs in cyber threat intelligence, security analysis, and cybersecurity knowledge processing. LLMs are particularly promising for handling unstructured and semi-structured cybersecurity information, including open-source intelligence, threat reports, vulnerability descriptions, and underground forum discussions. The papers on tool-augmented LLMs for open-source intelligence acquisition [5] and LLM-based classification of hacker forum posts [6] show how language models can support the transformation of large volumes of textual information into actionable cybersecurity insights. These works also highlight an important practical direction: LLMs become more useful for cybersecurity when they are combined with external tools, domain-specific data, fine-tuning, and careful evaluation.

A third theme is the security of AI systems themselves. As LLMs and other AI models become embedded in cybersecurity workflows, the models also become potential targets. The taxonomy of prompt attacks based on the confidentiality, integrity, and availability triad [7] provides a structured way to understand attacks against LLMs and their possible consequences. The study on insider-driven explainable AI-based backdoor attacks and adversarial training [8] further reminds us that explainability and model interpretation, while valuable for trust and transparency, can also be misused by attackers. These contributions emphasize that AI-enabled cybersecurity must be developed together with AI security.

A fourth theme is education and human capacity building. Cybersecurity remains a human-centered discipline, and the effective use of generative AI depends on well-trained analysts, engineers, and decision-makers. The generative AI-enhanced intelligent tutoring system for graduate cybersecurity programs [9] demonstrates how AI can support personalized and scalable cybersecurity education. This direction is particularly important because the rapid evolution of cyber threats requires not only better tools, but also continuous learning and workforce development.

3. Conclusions

Taken together, the nine papers in this Special Issue show both the promise and complexity of applying generative AI to cybersecurity. They demonstrate that generative AI can help improve detection, automate analysis, generate useful security knowledge, support education, and enhance privacy-preserving monitoring; at the same time, they show that cybersecurity applications of generative AI must be designed with caution. Reliability, robustness, explainability, privacy, data quality, computational cost, and responsible use remain central concerns.

Several future research directions emerge from this collection. First, generative AI methods should be further evaluated in realistic cybersecurity environments, including enterprise networks, cloud systems, industrial systems, IoT environments, and critical infrastructure. Second, more work is needed on trustworthy and explainable generative AI for cybersecurity, especially for high-stakes decisions where false positives, false negatives, and hallucinated outputs can have serious consequences. Third, LLM-based cybersecurity tools should be combined with retrieval, verification, human review, and domain-specific constraints to reduce errors and improve operational reliability. Fourth, as attackers may also use generative AI, future defense systems must account for adaptive, automated, and AI-assisted adversaries. Finally, privacy-preserving and resource-aware AI methods will be essential for deploying generative AI in real-world cybersecurity settings.

We hope that this Special Issue will serve as a useful reference for researchers and practitioners working at the intersection of generative AI and cybersecurity. The collected works show that this field is still developing rapidly, with many open challenges and opportunities. Continued collaboration among AI researchers, cybersecurity experts, educators, industry practitioners, and policymakers will be essential to ensure that generative AI strengthens digital security while minimizing new risks.