Federal biological datasets occupy an uncomfortable regulatory position. Open-access norms in science assume that sharing data accelerates discovery, and that is largely true. Biological data also carries national security implications that pure openness cannot ignore: sequence data, biosynthetic pathway information, and genomic repositories can be exploited by adversarial actors in ways that other scientific data cannot. At the same time, overly restrictive access regimes create their own problems. They disadvantage researchers at under-resourced institutions and in lower-income countries, concentrate the benefits of biotech innovation among a small set of well-resourced actors, and slow the scientific collaboration that produces the most important advances.

The governance challenge is building frameworks that resolve this tension rather than simply choosing a side. My work focuses on managed access models: tiered governance structures that allow biological data access based on institutional verification, research purpose, and biosecurity risk profile. The managed access framework I developed at EBRC, published in 2026, proposes a concrete architecture for federal repositories. I am organizing a Congressional briefing on this work for members of the BioTech and Artificial Intelligence caucuses.

The same infrastructure question runs beyond federal repositories. In ACS Synthetic Biology I argue that biofoundries, the automated laboratories that run synthetic biology experiments at scale, cannot validate results across institutions or train AI tools on pooled data without shared standards, and that the community can set those standards itself rather than waiting for a mandate.