Local AI is an AI system that runs on infrastructure controlled by the operator, such as an on-prem server, workstation, or private cluster, instead of a remote cloud service. The model, data, and inference workload stay closer to the organization, which can improve privacy, reduce dependency on third parties, and give tighter control over logging, storage, and network exposure.
In cybersecurity, local AI changes the trust model rather than removing risk. If the system is exposed through a web interface or API, it can be scanned, abused, or rate-limited like any other service. If it connects to files, tools, or automation, attackers may use prompt injection or malicious inputs to influence outputs and downstream actions. Defenders use local AI to keep sensitive data in-house, but they must still enforce access control, patching, sandboxing, and least privilege.