Mr. Edidiong Akpan

Machine Learning(ML) in Zero Trust Security: A Paradigm Shift in Cyber Defense

As cyber threats evolve, traditional perimeter-based security models are proving inadequate against sophisticated attacks such as ransomware, insider threats, and supply chain compromises. The rise of Zero Trust Architecture (ZTA) presents a fundamental shift in cybersecurity, emphasizing "never trust, always verify" principles. However, implementing Zero Trust at scale presents challenges, including real-time authentication, dynamic access control, and behavioral risk assessment—all areas where Machine Learning (ML) is driving transformative solutions. This talk explores the intersection of ML and Zero Trust Security, highlighting how AI-powered algorithms enhance authentication, threat detection, and anomaly response. We will discuss how ML-driven behavioral analytics can provide continuous verification, moving beyond static security policies to a dynamic, adaptive security model. Topics covered include: ML for Continuous Authentication: How AI-driven identity verification mitigates credential theft and session hijacking. Behavioral Analytics & Anomaly Detection: Identifying deviations in user and device behavior to prevent insider threats. AI-Powered Access Control: Automating security policy enforcement with ML-based risk assessment. Threat Intelligence Augmentation: Leveraging ML to detect zero-day threats and prevent lateral movement within networks. Despite its promise, integrating ML into Zero Trust frameworks also introduces challenges, including false positives, adversarial AI risks, and scalability issues. We will analyze real-world case studies from financial services, healthcare, and government sectors, showcasing how organizations implement AI-driven Zero Trust strategies to secure their ecosystems.