Edge deployment means running AI models on local devices or nearby systems such as on-premises servers, gateways, or field hardware instead of sending every request to a remote cloud. This approach is used when low latency, limited bandwidth, privacy, or offline operation matters. In security terms, it changes where trust is placed: data may stay closer to the user, but the model, its inputs, and its updates are handled across many distributed endpoints.
That distribution creates both benefits and risks. Defenders use edge deployment to reduce exposure of sensitive data and to keep critical functions working during network outages. Attackers, however, may target the device itself, tamper with local model files, poison cached data, or intercept insecure update paths. Secure boot, signed updates, access control, and monitoring are essential because an edge system is only as strong as its weakest local node.