Using AI as a Revolutionary Tool for Verifying Arms Control in the Biological Weapons Convention
- Edward M. Roche
- Sep 25
- 5 min read
In the ever-evolving landscape of global security, arms control treaties have long relied on verification mechanisms to ensure compliance and prevent the proliferation of dangerous weapons. From the Nuclear Non-Proliferation Treaty (NPT), which mandates inspections by the International Atomic Energy Agency (IAEA) to verify nuclear activities, to the Chemical Weapons Convention (CWC), where experts from the Organisation for the Prohibition of Chemical Weapons (OPCW) conduct on-site inspections to detect prohibited chemicals, verification is the cornerstone of trust among nations. The Biological Weapons Convention (BWC), adopted in 1972, also emphasizes verification, though its implementation has been more challenging due to the dual-use nature of biological research—technologies that can serve both peaceful medical purposes and weapon development.
On September 24, 2025, at the United Nations General Assembly, the U.S. President Donald Trump made a groundbreaking proposal: leveraging artificial intelligence (AI) to monitor and enforce the BWC. In his address, Trump pledged to lead an international effort to create an "AI verification system that everyone can trust" to detect and prevent the development of biological weapons. This announcement comes amid growing concerns about bioweapons proliferation, especially in light of recent advances in synthetic biology and gene editing. Trump's call for AI in arms control echoes ongoing discussions about modernizing verification tools, as traditional inspections struggle to keep pace with secretive research programs.
The proposal raises a crucial question: How can AI be effectively implemented to verify compliance with the BWC? Verifying biological weapons programs is notoriously difficult. Unlike nuclear or chemical weapons, where physical signatures like uranium enrichment or chemical precursors are detectable, biological research can be concealed in legitimate laboratories. Many countries keep such work secret, and distinguishing between valid scientific research (e.g., vaccine development) and weapons programs is complex. Cyberattacks or disinformation can further obscure activities, making traditional inspections insufficient. As the SIPRI has noted, biological weapons can be engineered to target specific human systems, disseminated via aerosols, water, or even agricultural attacks, complicating detection.
To make AI work in this context, principles from intelligence analysis could provide a robust framework. Intelligence analysis is designed to transform raw data into actionable insights, and its core tenet is to deliver advance warnings—providing decision-makers with information before an event occurs. This preemptive focus is essential for preventing bioweapons proliferation, where early detection of research could avert a crisis.
A key methodology from intelligence analysis is the Analysis of Competing Hypotheses (ACH), developed by Richards Heuer at the CIA in the 1970s. ACH is a structured technique to evaluate multiple explanations for observed data, reducing cognitive biases and improving accuracy. In the context of BWC verification, ACH could assess whether suspicious activities indicate weapons development or benign research. Randolph Pherson, a former intelligence analyst, has computerized ACH, creating tools like ACH2.0 software that automate the process. Pherson's work, including papers like "How Does ACH Improve Analysis?" and "Improving Intelligence Analysis with ACH," demonstrates how computerized ACH can handle large datasets, making it ideal for AI integration. (See References below)
Implementing ACH requires defining a set of "indicators"—observable signs of bioweapons development. These indicators form the backbone of the system, allowing AI to scan for patterns. Examples include:
Construction of high-containment laboratories (Biosafety Level 4 facilities) (BSL-4), which can be detected via satellite imagery or procurement records.
Recruitment of scientists specializing in genetics, virology, or molecular biology, often visible in public job postings or academic collaborations.
Purchases of dual-use equipment or chemicals, such as fermenters, centrifuges, or precursors like growth media, tracked through trade data or supply chain monitoring.
Interest at scientific conferences in weapon-relevant topics, e.g., aerosol delivery systems or gene editing for pathogens, gleaned from abstracts, attendee lists, or social media posts.
Government statements or policy shifts hinting at bioweapons programs, analyzed from official announcements or diplomatic leaks.
Other indicators: Abnormal patterns in biological sample shipments, unusual disease outbreaks in research areas, or cyber intrusions targeting biotech firms.
These indicators, drawn from intelligence assessments like SIPRI's analysis of biological weapons dissemination methods (e.g., aerosols, tampering) and the National Center for Biotechnology Information (NCBI)'s surveillance strategies, provide a comprehensive checklist. The Bergher report on bioweapons prevention emphasizes improving surveillance and forensics, which AI can automate. (See references below.)
AI's suitability for this task is unparalleled. Much of the necessary information is publicly available: scientific conferences post abstracts online, government pronouncements are in news archives, and trade data on equipment shipments can be accessed through databases like UN Comtrade. AI excels at scanning vast datasets in real-time, operating in multiple languages to monitor global sources—from English academic journals to Chinese state media. Tools like natural language processing (NLP) can identify subtle patterns, such as a sudden increase in gene-editing research in a secretive lab.
As AI detects positive signals (e.g., a lab purchasing BSL-4 equipment), it can "task" additional intelligence collection—directing satellites for imagery, human intelligence (HUMINT) for on-the-ground verification, or cyber tools for deeper analysis. This "tasking" mirrors intelligence protocols, where initial indicators trigger escalated scrutiny.
Using ACH with these indicators, AI can generate warnings for high-probability cases. For instance, if multiple indicators align (e.g., lab construction + scientist recruitment + chemical purchases), ACH evaluates hypotheses like "benign research" vs. "weapons program," assigning probabilities based on evidence. Pherson's computerized ACH provides a ready framework, adaptable to AI systems for automated hypothesis testing.
As probable cases emerge, military and intelligence services can focus resources, perhaps collaborating internationally under BWC auspices. Of course, challenges remain: distinguishing dual-use activities, ensuring AI's impartiality, and addressing privacy concerns in data collection. Ethical guidelines, like those from the UN GGE on cyber norms, must govern AI's use to avoid misuse.
President Trump's proposal marks a bold step forward. By harnessing AI, we can transform BWC verification from reactive inspections to proactive monitoring, reducing the risk of bioweapons proliferation. This advancement promises a safer world, where technology serves peace rather than peril. As Franklin might have said, "An ounce of prevention is worth a pound of cure"—and AI could be that ounce in arms control.
References
Randolph H. Pherson and Richards J. Heuer Jr., Structured Analytic Techniques for Intelligence Analysis, 3rd ed. (Washington, DC: CQ Press, 2020).
Berger, Katherine M. “Emerging and Enabling Technologies.” In Defense Against Biological Attacks, edited by S. K. Singh and J. H. Kuhn, 253–281. Cham: Springer, 2019. DOI: 10.1007/978-3-030-03071-1_13. Accessed September 25, 2025. https://www.ncbi.nlm.nih.gov.
Zhou, Dongsheng, Hongbin Song, Jianwei Wang, Zhenjun Li, Shuai Xu, Xingzhao Ji, Xuexin Hou, and Jianguo Xu. “Biosafety and Biosecurity.” Journal of Biosafety and Biosecurity 1, no. 1 (2019): 15–18. DOI: 10.1016/j.jobb.2019.01.001. Accessed September 25, 2025. https://www.ncbi.nlm.nih.gov.
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