Local AI workloads are AI tasks that run on the device itself instead of being sent to a remote cloud service. The model, prompts, outputs, and sometimes training data stay on the endpoint, using local CPU, GPU, or dedicated AI hardware. This can improve privacy, reduce latency, and keep sensitive data inside an organization’s trust boundary.
In cyber security, the shift matters because the endpoint becomes a higher-value target. If an attacker compromises a machine running local AI, they may steal model files, prompts, cached outputs, or credentials used to manage the workload. They can also tamper with results or abuse orchestration tools in distributed setups. Defenses include full-disk encryption, least-privilege access, patching, signed software, endpoint monitoring, and network separation for AI traffic. Local AI reduces cloud exposure, but it does not remove the need for strong workstation and infrastructure controls.