AI workloads depend heavily on GPUs for training and inference, yet traditional security tools lack the capability to monitor or secure GPU activity, leaving critical vulnerabilities exposed. The multi-node, multi-cluster nature of AI training expands the attack surface, while massive data volumes strain storage and transmission networks, overwhelming conventional security solutions. AI systems also require real-time processing, dynamic model updates, and advanced protection for sensitive data—areas where traditional tools fall short. Emerging threats, such as adversarial attacks, insider tampering, and model theft, further amplify the risks.