The intersection of artificial intelligence and Linux-based infrastructure has created unprecedented opportunities—and equally significant security challenges. As AI workloads increasingly dominate enterprise computing, traditional security models built around network perimeters have proven inadequate for protecting these dynamic, distributed systems. This presentation explores the implementation of Zero Trust Architecture (ZTA) specifically designed for AI workloads running on Linux platforms. We’ll examine how the “never trust, always verify” principle applies to machine learning pipelines, containerized AI services, and distributed training environments. Through real-world case studies and practical demonstrations, attendees will learn how to architect secure AI systems that maintain performance while drastically reducing attack surfaces. Key topics include securing AI model serving with dynamic authentication, implementing microsegmentation for ML pipelines, protecting sensitive training data through fine-grained access controls, and leveraging Linux-native security features like SELinux and cgroups within a Zero Trust framework. We’ll also cover emerging threats specific to AI systems—including model poisoning, adversarial attacks, and data exfiltration—and demonstrate how ZTA principles can mitigate these risks. Attendees will leave with practical knowledge of open-source tools, architectural patterns, and implementation strategies for building more secure AI infrastructure on Linux. Whether you’re deploying machine learning models in production or building the next generation of AI-powered applications, this session will provide actionable insights for strengthening your security posture without sacrificing innovation. Suitable for system administrators, DevOps engineers, security professionals, and developers working with AI/ML systems on Linux platforms.