# Know Your Scientist: KYC as Biosecurity Infrastructure

arXiv: https://arxiv.org/abs/2602.06172 · HTML: https://mlsanity.com/paper/2602.06172/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 101 references that are in the map.
- First posted on arXiv 5 Feb 2026.

## Builds on (most cited first, 11 of 11)

- [Protein Large Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2502.17504/) (2025)
- [Protecting Society from AI Misuse: When are Restrictions on Capabilities Warranted?](https://mlsanity.com/paper/2303.09377/) (2023)
- [Oversight for Frontier AI through a Know-Your-Customer Scheme for Compute Providers](https://mlsanity.com/paper/2310.13625/) (2023)
- [On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment](https://mlsanity.com/paper/2507.07341/) (2025)
- [Monitoring Misuse for Accountable 'Artificial Intelligence as a Service'](https://mlsanity.com/paper/2001.09723/) (2020)
- [FoldMark: Protecting Protein Generative Models with Watermarking](https://mlsanity.com/paper/2410.20354/) (2024)
- [Towards Responsible Governance of Biological Design Tools](https://mlsanity.com/paper/2311.15936/) (2023)
- [Generative AI for Biosciences: Emerging Threats and Roadmap to Biosecurity](https://mlsanity.com/paper/2510.15975/) (2025)
- [Open-weight genome language model safeguards: Assessing robustness via adversarial fine-tuning](https://mlsanity.com/paper/2511.19299/) (2025)
- [Resilient Biosecurity in the Era of AI-Enabled Bioweapons](https://mlsanity.com/paper/2509.02610/) (2025)
- [Biothreat Benchmark Generation Framework for Evaluating Frontier AI Models I: The Task-Query Architecture](https://mlsanity.com/paper/2512.08130/) (2025)

## Abstract (arXiv)

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.