Regulating genome language models: navigating policy challenges at the intersection of AI and genetics

Abstract

Genome Language Models (GLMs) represent a transformative convergence of artificial intelligence (AI) and genomics, offering unprecedented capabilities for biological discovery, healthcare innovation, and therapeutic design applications. However, these powerful tools create novel regulatory challenges that existing frameworks—whether AI governance or genomic privacy protections—cannot adequately address alone. This paper examines the critical regulatory gaps emerging at this intersection, highlighting tensions between AI principles that favor broad data access and genomic governance that demands stringent privacy protections and informed consent. We analyze how GLMs challenge conventional regulatory approaches as they pertain to applications in disease risk prediction, international research collaboration, and open-source model distribution. We propose a multilayered governance framework that combines policy innovations such as regulatory sandboxes and certification frameworks with technical solutions for privacy preservation and model interpretability. By developing adaptive governance strategies that bridge AI and genomic regulation, we can enable responsible GLM innovation while safeguarding individual rights, promoting equity, and addressing emerging biosecurity concerns in this rapidly evolving field.