GENERATIVE AI REVOLUTION IN CYBERSECURITY: A COMPREHENSIVE REVIEW OF THREAT INTELLIGENCE AND OPERATIONS

Abstract

The rapid advancement of digital technologies has exposed significant limitations in traditional cybersecurity frameworks, creating an urgent demand for intelligent and adaptive security solutions. This study examines the transformative role of Generative Artificial Intelligence (GAI) in modern cybersecurity frameworks. With the increasing frequency and sophistication of cyber threats, traditional security mechanisms are becoming insufficient, creating a demand for intelligent, adaptive solutions. This review highlights how GAI technologies, including Large Language Models (LLMs) and Generative Adversarial Networks (GANs), enhance threat intelligence by enabling real-time data analysis, anomaly detection, and automated incident response. The study emphasizes the ability of generative models to identify novel threats, simulate cyberattacks, and support proactive defense strategies. Furthermore, GAI contributes to operational efficiency by reducing human workload and improving decision-making processes in security operations centers. However, the paper also critically discusses the emerging risks associated with generative AI, particularly its misuse in developing advanced malware, phishing attacks, and deepfake-based cybercrimes. Challenges such as high computational cost, model inaccuracies, and ethical concerns are also explored. The findings suggest that while GAI significantly strengthens cybersecurity capabilities, its dual-use nature requires balanced implementation, robust governance, and continuous monitoring. Overall, the study provides a comprehensive understanding of how generative AI is reshaping threat intelligence and cybersecurity operations in the digital era.

Keywords : Generative Artificial Intelligence, Cybersecurity Threat Intelligence, Anomaly Detection, Adversarial Attacks, Large Language Models, Incident Response