Biological Intelligence in the AI Era

Recent outbreaks of disease such as those associated with the Hantavirus or the Ebola virus serve as reminders that biological threats remain an enduring challenge for global health security1. At the same time, rapid advances in artificial intelligence (AI), synthetic biology,2 biomanufacturing,3 and other emerging biotechnologies are reshaping the biological risk landscape, the implications of which are increasingly brought to the forefront of policy discussions worldwide. Even several leading AI companies recently signed an open letter urging US lawmakers to strengthen safeguards against AI-enabled biological weapons, underscoring growing concerns about the security implications of emerging technologies.

These outbreaks, technological advances, and policy developments collectively highlight the growing importance of biological intelligence,4 which is the ability to identify, integrate, and interpret relevant biological, technological, and policy signals before a novel crisis emerges. Strengthening such capabilities can help decision-makers better anticipate, assess, and respond to increasingly complex and evolving biological risks in a more timely and effective manner.

This article explores key challenges and opportunities for strengthening biological intelligence in the AI era, drawing on perspectives from experts from government, the private sector, and academia.

Evolving Biological Risks and Adapting Biological Intelligence

AI and emerging biotechnologies are creating new opportunities in outbreak detection, genomic analysis, risk assessment, and drug discovery. At the same time, advances in areas such as pathogen design, genome editing, and biological synthesis are expanding access to sophisticated biological capabilities, potentially enabling a broader range of actors to undertake activities that were once limited to well-resourced institutions.

These developments may lower traditional barriers to access increasingly sophisticated biological tools and knowledge, enabling not only governments, academic institutions, and major biotechnology companies to do so, but also smaller entities, non-state actors, and individuals with limited specialized training.

At a recent GMF roundtable event, Melissa Hopkins, emerging technology adviser at the Johns Hopkins Center for Health Security, emphasized that governance mechanisms must evolve with technological advances. She noted that while large language models may facilitate access to biological knowledge, biological AI models such as AlphaFold may expand biological capabilities themselves, underscoring the need for stronger safeguards. Measures such as nucleic acid (DNA and RNA) synthesis screening, software and hardware safeguards, and stronger testing and evaluation standards will become increasingly important as AI capabilities advance. Assessments of AI-related biological risks should distinguish between capabilities demonstrated in benchmark evaluations and real-world applications since performance under controlled conditions may not directly translate into practical biological outcomes or operational impact. While concerns regarding AI-enabled biological misuse continue to grow, recent evidence suggests that a significant gap remains between performance in benchmark evaluations and the ability to execute complex biological operations in practice.

In this environment, biological intelligence should evolve beyond merely tracking advances in biotechnology. It must assess who may gain access to emerging capabilities, how those capabilities can be operationalized, and where gaps may exist among technological advances, real-world implementation, and existing governance frameworks. By integrating these insights and providing policymakers with timely, evidence-based assessments, biological intelligence can help anticipate emerging biological risks, inform policy responses, and support decision-making in an increasingly complex biological risk landscape.

The Convergence of Biological and Digital Risk

The convergence of the biological and digital revolutions is fundamentally reshaping the nature of biosecurity risk. Biological research and biotechnology are increasingly interconnected through genomic and clinical data, AI-enabled analysis and design, cloud laboratories,5 digital platforms, and globally distributed research networks. As a result, biological risk can no longer be understood solely in physical terms. Digital vulnerabilities—including cyber-enabled misuse, data manipulation, misinformation, disinformation, and AI-assisted biological design—are becoming integral components of the biosecurity landscape, reflecting the growing convergence of biological and digital risk.

Airfinity Vice President for Data and Partnerships Katharina Lauer emphasized at the GMF roundtable that the definition of biological intelligence has expanded. She noted that, “Biointelligence in the AI era is not just about finding the next pathogen faster. It is about understanding how biological risk moves through data systems, institutions, markets, digital infrastructure and public trust.”

Her observation reflects the broader reality that biological crises increasingly unfold across biological and digital domains. Pathogens may spread through populations, but the consequences of biological events are often shaped by the flow of information, the integrity of data systems, public trust, and the resilience of digital infrastructure. Strengthening resilience against biological threats consequently requires more than traditional approaches centered on epidemiology, clinical care, treatment, and public health preparedness. It also demands greater attention to data systems, digital infrastructure, information integrity, and public trust. In this context, building biological intelligence platforms capable of integrating biological and digital risk signals—and providing policymakers with timely, reliable, and actionable assessments—will become increasingly important for strengthening global health security and biosecurity.

Building the Infrastructure and Partnerships for Biological Intelligence

Much of the infrastructure, expertise, and data that underpin biological intelligence no longer resides solely within government institutions. Biotechnology companies, AI developers, data analytics firms, cloud service providers, and other private-sector actors are playing increasingly important roles in identifying, assessing, and responding to biological threats. To effectively leverage these capabilities, governments must build trusted partnerships with private industry, research institutions, and technology providers. Effective biological intelligence depends on individual organizations, technological capabilities, and the networks that connect them.

At the GMF roundtable, Dr. Dan Hanfling, clinical professor of emergency medicine at the GW School of Medicine and Health Sciences argued that trust and cooperation must be cultivated before a crisis occurs. He highlighted the importance of sustained engagement among governments, industry, academia, and international partners, noting that expert dialogues and other engagement mechanisms including Track 1.5 and Track 2 discussions can help build the relationships and shared understanding necessary to respond effectively to biological events.

Efforts to strengthen such infrastructures and partnerships are already underway across the West. In the United States, the National Security Commission on Emerging Biotechnology (NSCEB) emphasizes the importance of strengthening governance to balance innovation and security. In Europe, discussions about the proposed EU Biotech Act reflect efforts to develop regulatory frameworks that enhance the biotechnology sector’s competitiveness and resilience. In Japan, biotechnology has been identified as one of the country's strategic growth sectors, and discussions within the government's Biotechnology and Synthetic Biology Working Group increasingly go beyond innovation policy to address biosecurity, biosafety, and economic security.

Beyond national initiatives, Perimeter Systems founder and CEO Matthew McKnight argued during the GMF roundtable that biological intelligence should increasingly be viewed as a dedicated intelligence collection discipline, one that requires sustained investment, international data-sharing arrangements, and globally distributed sensing capabilities. At scale, such infrastructure could support shared platforms for early warning and attribution, which would strengthen collective capacity to detect and assess emerging biological threats.

Realizing this vision demands far greater collaboration than that which exists today. Despite growing national efforts, cooperation across borders and sectors remains insufficient. Effectively addressing international biological threats requires strong partnerships, trusted networks, and sustained diplomatic engagement and intelligence-sharing among governments, experts, and private-sector stakeholders. Such relationships will form a critical foundation for effective biological intelligence in an increasingly distributed world.

The views expressed herein are those solely of the author(s). GMF as an institution does not take positions.

• 1

  Health security refers to the collective measures, policies, and systems designed to protect populations from health threats that can destabilize societies and economies. These include infectious disease outbreaks, bioterrorism, and other health hazards.

• 2

  Synthetic biology is the science of designing and engineering the genetic information of living organisms, such as viruses, bacteria, and cells, to create new functions or enhance useful biological capabilities. 

• 3

  Biomanufacturing refers to the use of microorganisms, cells, and other biological processes to produce pharmaceuticals, vaccines, chemicals, materials, and other valuable products.

• 4

  Biological intelligence refers to the ability to identify, integrate, and interpret biological, technological, and policy signals relevant to emerging biological risks. It extends beyond traditional epidemiological surveillance by assessing the risks and opportunities associated with advances in biotechnology and digital systems while drawing on information from public health, medical countermeasures, security, economics, and governance. Its purpose is to provide policymakers with timely, reliable, and actionable assessments that support decision-making before crises emerge.

• 5

  Cloud laboratories (cloud labs) are highly automated laboratory facilities that enable researchers to conduct experiments remotely through digital platforms, thereby expanding access to advanced biological research capabilities.