Viruses, Vectors, and Villains: Governing the Risks and Rewards of Artificial Intelligence in Virology

Artificial intelligence (AI) is rapidly transforming virology by strengthening pandemic preparedness, enhancing our molecular understanding of virus–host interactions, and accelerating the discovery and development of novel antiviral therapies. Yet, the same technologies also pose urgent biosecurity risks, particularly by enabling the development of bioweapons or identifying strategies that maximize harm. This paper presents a critical content analysis of current and emerging AI applications in virology, including tools used to detect synthetic alterations in viral genomes, assess the severity of new variants, and design clinical vectors for gene therapy. It also highlights the potential for misuse, whether intentional or due to poor data quality and flawed model training. Drawing on case studies, public databases, and documented applications from research institutions and biotechnology firms, the analysis shows that AI can integrate large datasets to reduce reliance on animal testing in drug development, improve therapeutic precision, and allocate resources more effectively during outbreaks. However, the increasing accessibility of AI tools and genomic data also creates vulnerabilities, especially as models become capable of autonomously interpreting the scientific literature and mining bioinformatics databases. To address this dual-use dilemma, the paper proposes targeted and adaptable policy recommendations for governments, research institutions, and commercial biotech firms, emphasizing pre-emptive oversight, responsible innovation, and ethical AI deployment. These recommendations are designed for immediate relevance yet flexible enough to evolve alongside the expanding role of AI in global health.