Securing Dual-Use Pathogen Data of Concern

Doni Bloomfield, Moritz S. Hanke, Aaron Maiwald, James R. M. Black, Toby Webster, Tina Hernandez-Boussard, Allison Berke, Oliver M Crook, Jassi Pannu

Keywords: Artificial intelligence, biosecurity, biosafety, biological foundation models, genome language models, cybersecurity, data, governance, oversight

TL;DR: We propose a taxonomy of potentially misuse-enabling biological data in order to assist with risk reduction, along with novel technical and legal frameworks to support pathogen data security.

Abstract:

Training data is an essential input into creating competent artificial intelligence (AI)
models. AI models for biology are trained on large volumes of data, including data
related to biological sequences, structures, images, and functions. The type of data
used to train a model is intimately tied to the capabilities it ultimately possesses–
including those of biosecurity concern. For this reason, an international group of
more than 100 researchers at the recent 50th anniversary Asilomar Conference
endorsed data controls to prevent the use of AI for harmful applications such as
bioweapons development. To help design such controls, we introduce a five-tier
Biosecurity Data Level (BDL) framework for categorizing pathogen data. Each
level contains specific data types, based on their expected ability to contribute
to capabilities of concern when used to train AI models. For each BDL tier, we
propose technical restrictions appropriate to its level of risk. Finally, we outline a
novel governance framework for newly created dual-use pathogen data. In a world
with widely accessible computational and coding resources, data controls may be
among the most high-leverage interventions available to reduce the proliferation of
concerning biological AI capabilities.

Submission Number: 7