Model-agnostic AI is an application architecture that is decoupled from any single foundation model or vendor. Instead of hard-coding calls to one model, the app uses a routing or abstraction layer that can send requests to different approved models based on task, cost, latency, policy, or availability.
This matters in cyber security because it reduces vendor lock-in and makes AI systems easier to govern. If one provider changes its terms, degrades quality, or introduces a security issue, defenders can swap models without rewriting the whole application. The same design also supports controls such as version tracking, logging, canary testing, rollback, and human approval for sensitive workflows. In real attacks, the wider multi-model surface can create new risks: prompt injection, unsafe model substitution, tool misuse, and data leakage may affect every integrated provider unless policy enforcement is centralized. Good model-agnostic design improves resilience, but only when paired with monitoring and strict access controls.