As artificial intelligence (AI) continues to revolutionize industries, its role in critical applications continues to grow exponentially. With all this innovation comes a growing concern — how do we keep AI systems secure? Unlike traditional applications, AI deals with highly sensitive data, intricate models, and sprawling networks that don’t fit neatly within the walls of traditional security measures. Traditional security models, built on the assumption of trust within a defined network perimeter, are proving inadequate in protecting the highly distributed, dynamic, and sensitive nature of AI workflows. In the context of AI, where sensitive data, complex models, and distributed systems intersect, Zero Trust offers a proactive and holistic approach to security.
This article explores the need for Zero Trust in AI, the fundamental principles that direct its application, and practical methods to safeguard AI systems from the outset.
