Artificial intelligence and synthetic biology: biosecurity risks, dual-use concerns, and governance pathways

Artificial intelligence (AI) is accelerating discovery timelines in synthetic biology, expanding opportunities for therapeutic breakthroughs, sustainable bio-manufacturing, and rapid response to health and environmental challenges. At the same time, this acceleration shifts biosecurity risks from physical materials toward a broader socio-technical landscape involving models, datasets, and distributed automation. This literature review synthesizes evidence from 119 peer-reviewed articles published between January 2015 and August 2025, focusing on biosecurity risks, dual-use concerns, and governance responses at the AI–synthetic-biology interface. Findings indicate that AI systems consistently increase design throughput and lower expertise barriers, enabling faster medical and industrial innovation but also heightening risks of repurposing for harmful molecules or genetic sequences. Existing governance remains fragmented: biosafety regimes emphasize physical agents and laboratories, while AI governance frameworks focus on privacy and fairness—leaving critical blind spots for biological misuse scenarios. Mitigation measures identified in the literature converge on layered controls, including risk-tiered access to high-capability models, systematic red-teaming prior to release, strengthened DNA-synthesis screening (including short fragments), audit logging, secure data infrastructures, and international capacity-building. Evaluation gaps persist, and harmonized metrics—such as synthesis-screening coverage, red-team testing frequency, accredited biofoundries, and early-warning lead times—are recommended for systematic monitoring of governance effectiveness. Addressing these challenges is essential to ensure that AI-enabled synthetic biology advances responsibly, balancing its transformative potential for health and sustainability with global biosecurity.