5 steps to securing AI workloads

This blog provides a five-step framework for securing AI workloads: gain full visibility into models, data flows, and permissions; harden posture with least privilege, credential hygiene, and continuous vulnerability management; enable real-time detection and automated containment across runtime, APIs, and cloud services; integrate prevention and detection to improve resilience and response speed; and maintain continuous security by regularly reassessing inventory, posture, and controls as AI architectures evolve.