A Mixture of Experts (MoE) model splits a system into multiple specialized sub-networks, or “experts,” and activates only the ones needed for a given input. Instead of running every parameter on every request, a routing mechanism chooses a small subset, which reduces compute and memory use while keeping the model large in overall capacity.
In cyber security, MoE matters because it changes how AI workloads are deployed and defended. Security teams may use MoE models to fit stronger inference systems onto limited hardware, especially when local processing is preferred over cloud services. Attackers can also exploit the efficiency of selective activation to run bigger models on smaller machines. For defenders, MoE creates operational challenges: monitoring, profiling, and sandboxing may need to account for different experts being loaded or triggered only under specific prompts, which can hide resource usage patterns and complicate analysis.