A Biomimetic Framework for Collective Sensing and Immune-Inspired Verification in Complex Risk Analysis

Generative AI, retrieval-augmented architectures, and multi-source automated analytical tools are now being deployed in increasingly exacting risk-analytic environments. Yet faster processing has not yielded commensurate reductions in false alarms, missed alarms, hallucinated outputs, or failures of responsibility attribution. Against that background, this study develops a biomimetic framework that integrates collective sensing with immune-inspired verification for analyzing complex risk information. Using an openly documented two-layer data architecture that combines authentic public-source samples with rule-generated, synthetic derivative samples, the study links biological-to-engineering mechanism translation, multi-objective optimization, National Institute of Standards and Technology (NIST)-aligned evaluation, and a governance-compatibility index within a single auditable design chain. The present evidence indicates that risk level continues to show a stable positive association with threat scores. At the same time, fabricated relations, despite their smaller aggregate volume, are more likely to accumulate in high-risk intervals. These patterns suggest that structural perturbations are more consequential than mere high-frequency noise in distorting judgment. More importantly, the study establishes the empirical and methodological conditions for a formal comparison of recognition quality, system resilience, and governance compatibility. Taken together, the paper offers a testable biomimetic mechanism model and a reproducible evaluative blueprint for auditable optimization in complex risk-information analysis.