An AI workload is any computing task that runs artificial intelligence models for training, fine-tuning, or inference. These workloads often need GPUs or other accelerators, large memory bandwidth, fast storage, and more power and cooling than ordinary business applications. In data centers, they also tend to concentrate many critical services into a small number of high-density systems.
In cybersecurity, AI workloads matter because they expand the attack surface around the model, the data, and the management layer. Attackers may target API endpoints, stolen credentials, orchestration platforms, remote administration tools, or firmware and supply-chain dependencies to steal data, disrupt service, or alter model behavior. Defenders reduce risk with segmentation, strong identity controls, logging, patching, workload isolation, and careful protection of training data, keys, and model artifacts.