Advancing GenAI for Real-Time Cybersecurity: Applications in Threat Detection and Adaptive Response

Abstract

The rapid evolution of generative artificial intelligence (GenAI) has opened new avenues for enhancing real-time cybersecurity, particularly in threat detection and adaptive response mechanisms. This paper explores the application of GenAI technologies in identifying and mitigating cyber threats, emphasizing their capacity to analyze vast datasets and recognize patterns that traditional methods might overlook. By leveraging advanced machine learning techniques, GenAI systems can provide real-time insights into potential vulnerabilities and attack vectors, allowing organizations to respond proactively to emerging threats. Furthermore, adaptive response strategies powered by GenAI enable automated incident management, facilitating quicker remediation of security incidents while minimizing human intervention. Through case studies and practical examples, this research demonstrates the efficacy of GenAI in enhancing cybersecurity resilience across various sectors, including finance, healthcare, and critical infrastructure. The findings indicate that integrating GenAI into cybersecurity frameworks not only improves threat detection rates but also fosters a more dynamic and responsive security posture, essential for navigating the complexities of today’s cyber landscape.