Organizations deploying GPU infrastructure for AI training and inference are facing a new set of networking challenges. Unlike traditional enterprise applications, AI workloads generate unique traffic patterns, including large-scale data ingestion before training, high-bandwidth model checkpoint transfers to distributed storage, continuous inference requests from thousands of users, and ongoing management traffic that crosses cluster boundaries.

Deploying hardware security keys like YubiKeys is one of the most effective ways to prevent phishing and credential-based attacks. Yet for many IT teams, the initial promise of stronger, phishing-resistant security is often offset by the operational burden that comes with managing these devices at scale.