Cybersecurity Threats And Defenses In The Generative AI Era

Abstract

The rapid proliferation of generative artificial intelligence (GenAI) technologies, including large language models (LLMs) and generative adversarial networks (GANs), has fundamentally transformed the cybersecurity landscape by simultaneously empowering sophisticated attack vectors and enabling advanced defensive mechanisms. This paper presents a comprehensive analysis of emerging cybersecurity threats catalyzed by generative AI, encompassing AI-generated phishing campaigns, deepfake-based social engineering, automated malware generation, and adversarial exploitation techniques. Concurrently, it examines AI-enhanced defense strategies, including transformer-based threat detection, adversarial training for model robustness, and neural network-driven intrusion detection systems. Through a systematic taxonomy of AI-powered threats and a comparative evaluation of traditional versus AI-augmented defense mechanisms, this study quantifies the paradigm shift in attack sophistication and defense efficacy. Findings reveal that AI-enhanced defenses achieve detection rate improvements ranging from 22.4% to 33.4% over traditional methods across phishing, malware, intrusion, and deepfake detection domains. The paper further discusses regulatory frameworks, ethical considerations, and future research directions essential for maintaining cybersecurity resilience in an AI-driven threat environment.