Mr. Vaibhav Vudayagiri

AI-Driven Distributed Cyber Defense: Transforming Web Application Security at Scale

Abstract:

As web applications increasingly operate within distributed and cloud-native architectures, the traditional paradigms of cybersecurity are proving insufficient to counteract the dynamic and sophisticated threat landscape. This talk delves into the transformative potential of Artificial Intelligence (AI) in redefining cyber defense strategies for distributed systems, merging cutting-edge research with pragmatic industry applications. AI-driven cybersecurity introduces unprecedented capabilities in real-time anomaly detection, autonomous threat mitigation, and predictive threat intelligence. Federated learning enables decentralized models that secure edge devices without compromising data privacy, while reinforcement learning provides adaptive, policy-driven responses to evolving attack vectors. Additionally, advanced techniques in unsupervised learning and behavioral analytics empower systems to detect zero-day exploits, safeguard microservices, and fortify API communication channels at scale. The session will also highlight the integration of AI within CI/CD pipelines for proactive vulnerability management and the challenges of deploying scalable, low-latency security solutions in distributed environments. Real-world case studies, including securing high-throughput e-commerce platforms and AI-driven Zero Trust frameworks, will underscore the practical implications and operational efficiencies of these approaches. Finally, we will address emerging challenges such as adversarial attacks on AI models, the limitations of current datasets, and the need for explainable AI (XAI) to build trust in automated security systems. By the end of this session, participants will gain a comprehensive understanding of how AI is not just augmenting but fundamentally transforming the cybersecurity landscape for distributed web applications, paving the way for a secure, resilient digital future.