The adoption of private AI and LLM (Large Language Model) infrastructures by enterprises introduces a new class of risks. Unlike traditional IT workloads, AI data centers manage sensitive training data, powerful GPU clusters, distributed inference services, and high-throughput pipelines that can easily become attack vectors.
Another segment building their own AI factories are Neocloud providers, who deliver GPU-as-a-Service, building hyperscale AI factories powered by NVIDIA and other leading GPU platforms to give enterprises on-demand, high-performance compute for training and inference.
Organizations face threats to data, intellectual property, AI models, and end-users. Building AI capabilities without embedding security increases exposure to poisoning, data leakage, and governance failures.
To ensure resilience, AI data centers must be secured end-to-end — from the fabric and GPU clusters to Kubernetes workloads, and API-driven inference workloads and services.