Biological AI tools for protein design and structure prediction are advancing rapidly, creating dual-use risks that existing safeguards cannot adequately address. Current model-level restrictions, including keyword filtering, output screening, and content-based access denials, are fundamentally ill-suited to biology, where reliable function prediction remains beyond reach and novel threats evade detection by design. We propose a three-tier Know Your Customer (KYC) framework, inspired by anti-money laundering (AML) practices in the financial sector, that shifts governance from content inspection to user verification and monitoring. Tier I leverages research institutions as trust anchors to vouch for affiliated researchers and assume responsibility for vetting. Tier II applies output screening through sequence homology searches and functional annotation. Tier III monitors behavioral patterns to detect anomalies inconsistent with declared research purposes. This layered approach preserves access for legitimate researchers while raising the cost of misuse through institutional accountability and traceability. The framework can be implemented immediately using existing institutional infrastructure, requiring no new legislation or regulatory mandates.

翻译：用于蛋白质设计与结构预测的生物人工智能工具正在快速发展，由此产生的双重用途风险是现有防护措施无法充分应对的。当前包括关键词过滤、输出筛查和基于内容的访问拒绝在内的模型级限制措施，从根本上不适用于生物学领域——在该领域，可靠的功能预测仍遥不可及，而新型威胁在设计上就能规避检测。受金融领域反洗钱实践的启发，我们提出一个三层“了解你的客户”框架，将治理重心从内容审查转向用户验证与监控。第一层利用研究机构作为信任锚点，为附属研究人员提供担保并承担审查责任。第二层通过序列同源性搜索和功能注释实施输出筛查。第三层监控行为模式，以检测与申报研究目的不符的异常活动。这种分层方法在保障合法研究人员访问权限的同时，通过机构问责与可追溯性提高了滥用成本。该框架可利用现有机构基础设施立即实施，无需新的立法或监管授权。