Hybrid AI Failover and Secure Field-Data Capture System with Shadow-Mode Validation and Training-to-Inference Reassignment

A system comprising:

• a computing system configured to operate in a safety-critical environment;
• a primary inference compute unit executing a primary artificial intelligence (AI) model to generate inference outputs;
• a secondary compute unit executing a candidate AI model in parallel with the primary AI model,
• wherein the candidate AI model processes inputs functionally equivalent to those processed by the primary AI model and is prevented from producing authoritative output during parallel execution;
• a divergence analysis module configured to compare outputs of the primary AI model and the candidate AI model over a validation interval;
• an inference-authority gating module configured to determine eligibility for transfer of inference authority based on whether output divergence remains within a defined threshold to satisfy alignment conditions;
• and
• a failover controller configured to govern inference-authority assignment, including revoking inference authority from the primary AI model upon detection of failure, drift, or anomaly, and assigning inference authority to the candidate AI model during a defined transition window,
• wherein assignment of inference authority during the transition window is conditioned on satisfaction of divergence-based alignment criteria evaluated by the divergence analysis module and is performed in a controlled manner to reduce output discontinuity or system instability.

A computer-implemented method for controlling inference authority in a safety-critical system, comprising:

• executing a primary artificial intelligence (AI) model on a first compute unit to generate inference outputs;
• executing a candidate AI model in parallel on a second compute unit using inputs that are functionally equivalent to those used by the primary AI model following deterministic preprocessing or augmentation steps, while suppressing authoritative output from the candidate AI model;
• comparing outputs of the primary AI model and the candidate AI model over a validation interval;
• evaluating output divergence relative to a defined threshold to determine whether alignment conditions are satisfied;
• governing inference authority by maintaining the candidate AI model in a non-authoritative state unless the divergence threshold is satisfied;
• and
• upon satisfaction of the divergence threshold or upon detection of failure, drift, or anomaly, assigning inference authority to the candidate AI model during a transition window, including revoking inference authority from the primary AI model,
• wherein inference authority is assigned in a controlled manner during the transition window that enforces output alignment to reduce output discontinuity or system instability.