Why Traditional Secure Networking Can’t Protect AI Workloads

AI is rapidly transforming enterprise infrastructure, but the network layer lags behind, still relying on outdated technologies like VPNs. Traditional secure networking struggles with AI's unique characteristics, such as high-volume, bursty traffic. This creates risks, especially through predictable traffic patterns that adversaries can exploit. AI workloads are distributed, sensitive to latency, and often operate in challenging network conditions. Existing security measures aren't designed to combat adversarial AI threats like side-channel attacks and model exfiltration. This jeopardizes the confidentiality, observability and operational integrity of AI systems. A new transport architecture is crucial to defend against these vulnerabilities, particularly those that eliminate predictable paths. This new architecture necessitates multipath transport, dynamic route shifting, and Zero Trust principles. Stealth and robust performance in degraded network conditions are also critical requirements. The current secure networking landscape falls short, widening the gap between AI's needs and its protection. A modern, stealthy, multipath, and Zero Trust approach to transport is essential.