A Blind Spot in AI Biosecurity: Physics-Based Sequence Analysis

Current approaches to artificial intelligence (AI) biosecurity focus predominantly on the capabilities and accessibility of data-trained models. These approaches emphasize training-data restrictions, model-level safeguards, output monitoring, and controlled access to computational resources. However, this framework may overlook a distinct category of computational methods: analytical approaches based on physically defined molecular descriptors and mathematical transformations that do not require model training. The Informational Spectrum Method (ISM), based on the Electron–Ion Interaction Potential (EIIP), provides a concrete example. Generative AI introduces an additional consideration by enabling biologically knowledgeable users without programming expertise to implement computational tools that were previously beyond their technical capabilities. The resulting biosecurity challenge is not the creation of new scientific knowledge but the transformation of existing knowledge into accessible operational capabilities. Although EIIP/ISM provides a current example, the underlying issue is general and may become increasingly relevant as other physics-derived computational methods emerge.