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

Area of Science:

• Artificial Intelligence
• Biomimetics
• Risk Analysis

Background:

• Current generative AI and automated tools in risk analysis face challenges with accuracy and accountability.
• Faster processing has not reduced issues like false alarms or hallucinated outputs.

Purpose of the Study:

• To develop a biomimetic framework integrating collective sensing and immune-inspired verification for complex risk analysis.
• To create an auditable design chain linking biological mechanisms to engineering solutions for risk assessment.

Main Methods:

• Utilized a two-layer data architecture with authentic and synthetic samples.
• Employed biological-to-engineering mechanism translation and multi-objective optimization.
• Incorporated National Institute of Standards and Technology (NIST)-aligned evaluation and a governance-compatibility index.

Main Results:

• Risk level consistently correlates positively with threat scores.
• Fabricated data, though less voluminous, disproportionately accumulates in high-risk intervals.
• Structural perturbations are more impactful on judgment than high-frequency noise.

Conclusions:

• The study provides a testable biomimetic model for complex risk information analysis.
• Establishes conditions for comparing recognition quality, system resilience, and governance compatibility.
• Offers a reproducible blueprint for auditable optimization in risk analysis.