Strengthening Global Governance for AI-Enabled Biological Treat Detection

Artificial intelligence (AI) is rapidly transforming biological surveillance and public health preparedness by enabling faster detection of emerging pathogens, unusual transmission patterns, and potential biological threats. However, the growing integration of AI into biological threat detection introduces major governance challenges related to data sharing, model transparency, dual-use risks, accountability, and unequal global access to AI infrastructure. Existing global governance frameworks remain fragmented and ill-equipped for AI-era biological detection systems. This paper examines how governance frameworks can be strengthened to ensure that AI-enabled biological threat detection systems are effective, equitable, secure, and resistant to misuse. Drawing on a systematic review of 34 sources and a catalogue of 23 AI-enabled tools, we propose and apply a seven-dimension Governance Readiness Framework, STAE+ framework, to evaluate two representative systems - WHO EIOS and SeqScreen. Both tools score in the moderate range when assessed by raw score (EIOS 54/102; SeqScreen 55/102), but are reclassified as Low Governance Readiness under the Framework's Override Clause, which is triggered by critical zero scores on Accountability sub-dimensions in both tools, and additionally on Incident Reporting for SeqScreen. We argue that governance mechanisms must be grounded in transparency, accountability, equity, and shared security, and propose a structured set of recommendations with designated actors. Africa is used as the primary LMIC lens throughout, as the region carrying the highest relevant disease burden relative to AI tool development capacity.