Accelerating Autonomous Biosecurity Auditing via Multi-Agent Systems Integrating Large Language Model Reasoning and Privacy-Preserving Computational Biology Pipelines
Abstract
The rapid convergence of synthetic biology and generative artificial intelligence has drastically lowered the barriers to sophisticated pathogen engineering, necessitating a paradigm shift in global biosecurity auditing. Current screening protocols are predominantly centralized, reactive, and constrained by the inherent tension between comprehensive genomic surveillance and the protection of intellectual property or national security data. This paper investigates the development of an autonomous, decentralized biosecurity framework that leverages Multi-Agent Systems (MAS) to orchestrate Large Language Model (LLM) reasoning alongside privacy-preserving computational biology pipelines. By utilizing Secure Multi-Party Computation (SMPC) and Homomorphic Encryption (HE), the proposed system enables the auditing of genomic sequences and laboratory activities without exposing the underlying sensitive biological data to a central authority. The research provides a systemic analysis of the structural trade-offs between computational latency and biological detection accuracy, the socio-technical infrastructure required for global deployment, and the governance frameworks essential for maintaining scientific openness while mitigating catastrophic risks. We further examine the policy implications of autonomous auditing agents, focusing on the sustainability of decentralized oversight and the fairness of automated compliance mechanisms. By synthesizing perspectives from computational biology, systems engineering, and international policy, this study offers a comprehensive roadmap for a robust, privacy-respecting, and scalable biosecurity infrastructure designed for the age of distributed biotechnological production.
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