A neuromorphic safety monitor for verifiable runtime assurance in stochastic control loops

The integration of high-performance but non-deterministic AI into safety-critical control loops remains a fundamental challenge for verifiable autonomy. We present a runtime assurance framework in the simplex family, in which a monitored primary controller is paired with a certified fallback safety controller. The monitor is a neuromorphic anomaly detector that we model as a stochastic sensor and characterize directly in this manuscript via end-to-end measurements of its detection latency distribution and its false negative and false positive rates. Using these measured quantities, we derive a probabilistic bound on closed-loop failure risk over a finite horizon that explicitly links monitor reliability and response time to safety. We validate the framework in a high-fidelity cislunar rover simulation under combined adversarial and environmental faults, where timely intervention yields an absolute mission-success gain of 84 percentage points. The result is an auditable methodology for deploying advanced AI in control systems where rapid and reliable fault detection is required.