Generative AI-Driven Distributed Cybersecurity Frameworks for AI-Integrated Global Big Data Systems
Rahul Vadisetty, Anand Polamarasetti · 2024
The rapid proliferation of AI into extensive global data systems has brought new challenges in cybersecurity, mainly because such environments have grown inherently complex, large, and distributed. Existing cybersecurity solutions cannot keep pace with emerging threats; therefore, novel approaches are called for. This paper proposes a generative AI-driven distributed cybersecurity framework for enhancing threat detection and response in the context of AI-integrated extensive data systems. These generative models, such as GANs and VAEs, have been considered to advance the framework in simulating cyber-attacks, detecting anomalies, and generating adaptive countermeasures. Based on a decentralized design, the proposed architecture ensures real-time threat monitoring and mitigation over distributed nodes. A few key results are highlighted in the paper, showing the effectiveness of the proposed framework toward improving detection accuracy, reducing false positives, and enhancing resilience against complex attacks. A comparative analysis against existing approaches shows significant improvement in the scalability and adaptability of the efficacy. This study finally represents a new frontier in which generative AI techniques meet the principles of distributed cybersecurity to open a pathway toward more proactive and robust defense mechanisms within large-scale, AI-driven environments.