AI-Driven Cloud Defense: Fortifying National Infrastructure Against Cyber Threats

About the Session

As cyberattacks increasingly target national infrastructure, traditional perimeter-based defenses fall short. This session introduces a machine learning-powered defense architecture designed for modern, distributed cloud environments. By integrating supervised, unsupervised, and reinforcement learning models, organizations can achieve real-time anomaly detection, automated incident response, and predictive threat mitigation—improving early threat detection by up to 60%. Participants will examine how cloud-native, multi-layered security controls support defense-in-depth strategies, reducing lateral attack movement by up to 85%. Case studies reveal how ML-driven automation slashes containment times from hours to seconds, enabling resilient, adaptive protection. The talk also explores national security implications—from software supply chain risks to state-sponsored threats—and how intelligent behavioral modeling enables over 90% accuracy in identifying advanced persistent threats (APTs). Finally, the session provides actionable guidance for aligning AI-enhanced security practices with Zero Trust Architecture, NIST, and ISO 27001 standards to ensure both proactive defense and regulatory compliance.

Speaker

Natarajan Ravikumar

Technology Executive