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 complex risk-information analysis. Using an openly documented two-layer data architecture that combines authentic public-source samples with rule-generated derivative synthetic samples, the study links biological-to-engineering mechanism translation, multi-objective optimisation, NIST-aligned evaluation, and a governance compatibility index within one auditable design chain. The present evidence indicates that risk level retains a stable positive association with threat scores, while 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 for judgment distortion. More importantly, the study establishes the empirical and methodological conditions required for formal comparison across recognition quality, system resilience, and governance compatibility. Taken together, the paper offers a testable biomimetic mechanism model and a reproducible evaluative blueprint for auditable optimisation in complex risk-information analysis.