Biosecurity in the Age of Artificial Intelligence: Challenges, Risks, and Governance

Abstract

The profound convergence of artificial intelligence (AI) and biological research, particularly within the realm of biotechnology, heralds an era of unprecedented scientific advancement and societal benefit. This integration offers transformative opportunities, from accelerating disease detection and revolutionizing drug discovery to pioneering novel applications in synthetic biology and biomaterials. However, this powerful synergy simultaneously casts a long shadow of significant biosecurity challenges. The inherent dual-use nature of AI technologies, capable of both immense good and catastrophic harm, necessitates rigorous examination. This comprehensive report delves into the intricate facets of the contemporary global biosecurity landscape, meticulously identifies the specific and evolving threats exacerbated or introduced by AI, critically evaluates the spectrum of existing and emerging safeguards, and thoroughly explores strategic pathways for robust international cooperation essential to mitigate these complex and multifaceted risks. The aim is to provide a detailed, evidence-based analysis to inform policy and practice in this critical interdisciplinary domain.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

1. Introduction

The trajectory of technological progress has witnessed few accelerations as profound and pervasive as that of artificial intelligence. From its theoretical origins in the mid-20th century to its current embodiment in sophisticated algorithms and neural networks, AI has undergone a remarkable evolution, transitioning from academic curiosity to a ubiquitous force reshaping industries and societal norms. Its integration into the life sciences, particularly biotechnology, marks a pivotal inflection point, fundamentally altering the pace and scope of biological discovery and engineering. AI’s unparalleled capacity to process and analyze vast, complex datasets, predict intricate molecular interactions with increasing accuracy, and even design novel biological compounds de novo has dramatically accelerated scientific progress, compressing decades of traditional research into mere years, or even months. This rapid advancement promises revolutionary breakthroughs in medicine, agriculture, environmental remediation, and countless other domains.

However, the very capabilities that underpin AI’s transformative potential in biology also give rise to acute concerns regarding its potential misuse. The ‘dual-use dilemma’ – where a technology developed for beneficial purposes can also be exploited for malicious ends – is not new to the life sciences, having long been a cornerstone of biosecurity discourse. Yet, the advent of AI imbues this dilemma with novel complexities and amplified risks. AI’s ability to lower the barrier to entry for complex biological experimentation, to generate novel biological entities that may evade existing countermeasures, or to optimize the lethality and transmissibility of known pathogens, presents an unprecedented challenge. Malicious actors, ranging from state-sponsored programs to non-state entities and individuals, could potentially exploit these AI-powered tools to create or enhance harmful biological agents, including those intended for bioweaponry. This paradigm shift necessitates a comprehensive, granular examination of the biosecurity implications of AI-biology convergence, a critical assessment of existing regulatory frameworks and their inherent limitations, and an urgent exploration of novel international cooperation efforts to ensure the responsible, ethical, and secure deployment of AI within the biological sciences. The imperative is to harness AI’s transformative power for global good while proactively defending against its potential for catastrophic harm.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

2. The Global Biosecurity Landscape

2.1 Current State of Biosecurity: Foundations and Fragilities

Biosecurity, fundamentally, encompasses the integrated measures and systems designed to prevent the loss, theft, misuse, diversion, or intentional release of biological agents and toxins, as well as to minimize the potential for accidental exposure. Its scope extends beyond biosafety (which focuses on preventing unintentional harm) to explicitly address deliberate malevolent acts. Historically, biosecurity protocols have evolved in response to a growing understanding of biological risks, from the containment of naturally occurring pathogens to the prevention of bioweapons proliferation. At the international level, the Biological Weapons Convention (BWC) serves as a cornerstone, prohibiting the development, production, stockpiling, acquisition, or retention of microbial or other biological agents, or toxins, of types and in quantities that have no justification for prophylactic, protective or other peaceful purposes, as well as weapons, equipment or means of delivery designed to use such agents or toxins for hostile purposes or in armed conflict. However, the BWC lacks a robust verification mechanism, relying instead on a norm of prohibition and national implementation measures (researchgate.net).

Globally, the practical implementation of biosecurity practices remains highly variable. While some nations, particularly those with advanced biotechnological capabilities, have established stringent national controls, comprehensive regulatory frameworks, and robust institutional oversight mechanisms (e.g., export controls on dual-use biological agents, national legislation criminalizing misuse, institutional biosafety and biosecurity committees), many others, especially in the Global South, still face significant challenges in developing and enforcing comparable standards. These disparities create potential vulnerabilities, as a weak link in one region can have global ramifications. The advent of AI introduces a new layer of complexity to this already fragmented landscape. On one hand, AI-driven tools possess the potential to significantly enhance existing biosecurity measures by improving biosurveillance, accelerating diagnostic capabilities, and streamlining the development of medical countermeasures (axios.com). For instance, AI algorithms can analyze vast amounts of genomic sequencing data to identify emerging pathogens or detect unusual outbreaks, bolstering early warning systems. On the other hand, AI’s transformative capabilities also introduce novel risks that can undermine traditional biosecurity safeguards, necessitating a rapid re-evaluation and adaptation of existing frameworks.

2.2 Emerging Threats from AI Integration: Lowering Barriers and Raising Ceilings

The profound incorporation of AI, particularly advanced machine learning, deep learning, and generative AI models, into biotechnology has not only accelerated scientific discovery but also fundamentally reshaped the threat landscape. These AI systems are increasingly sophisticated, capable of operating with a degree of autonomy and complexity that transcends previous technological capabilities. The primary emerging threats stem from several synergistic factors:

• Enhanced Accessibility to Dangerous Knowledge and Tools: AI models, especially large language models (LLMs) and advanced biological design tools (BDTs), can democratize access to highly sophisticated biological knowledge and experimental protocols that traditionally required extensive academic training, specialized laboratory equipment, and significant financial resources. This democratization implies that individuals or groups with limited formal scientific training or resources could potentially access and operationalize information previously confined to expert communities. For example, AI-driven BDTs have demonstrably generated novel protein sequences with high toxicity scores, mimicking known potent toxins like ricin and diphtheria toxin, raising concerns about the ease with which such information could be leveraged by nefarious actors (arxiv.org). The availability of open-source AI models further compounds this risk, as their widespread distribution makes control and oversight challenging.

• Accelerated and Optimized Bioweapon Development: The inherent dual-use nature of AI technologies means that capabilities intended for benign scientific research can be repurposed for malicious ends. Malicious actors could exploit these tools to significantly accelerate and optimize various stages of bioweapon development, from initial design to enhanced production and delivery. Historical instances of biological warfare, such as the Japanese Unit 731 in World War II or the Soviet bioweapons program, underscore the devastating potential of biological agents. AI’s role in this context is to lower the technical barriers, reducing the time, cost, and expertise required for bioweapon creation, while simultaneously raising the ‘ceiling of harm’ by enabling the design of agents with enhanced virulence, transmissibility, stability, or resistance to existing medical countermeasures (time.com). This acceleration could significantly shrink the window of opportunity for detection and response by national and international authorities.

• Novelty and Unpredictability: A particularly concerning aspect of AI-driven biological engineering is its capacity to generate entirely novel biological entities that may not have natural counterparts. This includes synthetic pathogens with engineered properties, novel toxins, or even ‘chimeric’ agents combining features from multiple known threats. The unpredictability of such novel agents, coupled with the potential for them to evade existing detection systems, diagnostic tools, and therapeutic interventions, poses a severe challenge to global health security and biodefense strategies.

• Autonomous Experimentation and Optimization: The integration of AI with robotics and automated laboratory systems is leading towards increasingly autonomous biological experimentation. While offering unprecedented speed and efficiency for legitimate research, this convergence could also enable malicious actors to conduct complex biological experiments with minimal human intervention, further lowering the technical and safety barriers for dangerous research. AI could optimize pathogen growth conditions, enhance their stability in different environments, or even design novel delivery mechanisms, all with minimal human oversight.

These emerging threats necessitate a proactive, adaptive, and collaborative approach to biosecurity, one that moves beyond traditional paradigms to address the unique challenges posed by intelligent autonomous systems in the biological domain.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

3. Specific Threats Posed by Dual-Use AI Technologies

The dual-use challenge presented by AI in biology is not monolithic but manifests through distinct capabilities and tools. Understanding these specific vectors of risk is crucial for developing targeted mitigation strategies.

3.1 Misuse of Large Language Models (LLMs)

Large Language Models (LLMs), such as OpenAI’s GPT series, Google’s Gemini, or Meta’s Llama, are trained on colossal datasets of text and code, allowing them to generate coherent, contextually relevant, and often scientifically accurate information. When these models are trained on extensive biological and chemical datasets, including scientific literature, patents, databases of molecular structures, and experimental protocols, they acquire a sophisticated understanding of biological principles and procedures. The risk lies in their potential to generate information that facilitates malicious biological activities, even for individuals lacking formal scientific training.

Specific misuse scenarios for LLMs include:

• Generation of Synthesis Protocols: LLMs can be queried to generate step-by-step instructions for synthesizing dangerous biological agents, toxins, or precursors. This could range from the theoretical design of a novel viral vector to detailed laboratory protocols for culturing specific bacteria or producing protein toxins. The accuracy and completeness of these generated protocols are increasing with model sophistication.
• Identification of Vulnerabilities: LLMs can analyze vast amounts of scientific literature to identify vulnerabilities in biodefense systems, such as weaknesses in existing vaccines, diagnostic methods, or therapeutic approaches against known pathogens. They could also suggest modifications to pathogens that would allow them to evade current countermeasures.
• Optimization of Pathogen Characteristics: While not directly designing molecules, LLMs could provide theoretical guidance on how to enhance pathogen virulence, transmissibility, stability, or resistance to antibiotics/antivirals based on existing scientific knowledge. This could involve suggesting specific genetic modifications or environmental conditions.
• Epidemiological Modeling for Attack Planning: LLMs could be used to model the potential impact of a biological attack, including optimal dissemination strategies, predicted infection rates, and potential societal disruption, thereby aiding malicious actors in planning and assessing the efficacy of their actions. They could analyze population densities, climate patterns, and infrastructure vulnerabilities.
• Dispensing of Biosecurity Evasion Advice: LLMs might inadvertently or deliberately provide advice on how to bypass biosecurity measures, such as suggesting methods for concealing dangerous research, procuring restricted materials, or evading gene synthesis screening protocols. This raises concerns about ‘jailbreaking’ models to extract sensitive information or instructions.

A study evaluating the ‘Moremi Bio Agent’ demonstrated that LLMs could indeed contribute to the design of toxic proteins with high toxicity scores, analogous to well-known toxins. This research highlighted the alarming dual-use capabilities of current AI-enabled biodesign pipelines, even when models are not explicitly trained for malicious purposes, underscoring the challenge of ensuring ‘alignment’ with safety principles (arxiv.org). The fundamental challenge is that the same knowledge base used for legitimate biomedical innovation can be recontextualized and leveraged for harmful ends, making it difficult to implement content filters that do not unduly restrict beneficial research.

3.2 Misuse of Biological Design Tools (BDTs)

Biological Design Tools (BDTs), powered by sophisticated AI and machine learning algorithms, are at the forefront of synthetic biology. These tools move beyond mere information generation to actively facilitate the design and engineering of novel biological constructs, including proteins, enzymes, genetic circuits, and even entire genomes. They often leverage generative models (e.g., Generative Adversarial Networks, Variational Autoencoders) and predictive algorithms to propose optimal sequences or structures for desired biological functions.

Specific misuse scenarios for BDTs include:

• De Novo Pathogen Design: BDTs can facilitate the design and synthesis of entirely novel pathogens or known pathogens with enhanced characteristics. This could involve designing viruses with increased infectivity, bacteria with multi-drug resistance, or toxins with novel mechanisms of action or increased potency. The ability to design sequences that do not exist naturally makes detection and countermeasure development significantly harder.
• Enhanced Virulence and Transmissibility: AI-powered BDTs can analyze genetic sequences and protein structures to identify specific mutations or modifications that could increase a pathogen’s virulence (its ability to cause disease) or transmissibility (its ability to spread). For example, a BDT could optimize spike protein designs for a virus to enhance receptor binding affinity in human cells.
• Resistance to Countermeasures: BDTs can be used to engineer pathogens that are resistant to existing medical countermeasures, such as vaccines, antibiotics, or antiviral drugs. This is achieved by designing specific mutations in genes coding for drug targets or immune epitopes, rendering current interventions ineffective. This could lead to the emergence of ‘superbugs’ specifically designed to bypass our current arsenal (axios.com).
• Increased Stability and Environmental Persistence: AI could design genetic modifications that enhance the stability of biological agents in various environmental conditions (e.g., temperature, humidity), making them more robust for dissemination and survival outside a host.
• Concealment and Evasion: BDTs could be used to design ‘stealth’ pathogens that evade existing biosurveillance systems or diagnostic tests, for example, by altering sequences used in PCR primers or antibody detection assays.

The convergence of LLMs and BDTs is particularly concerning. An LLM could provide the theoretical blueprint for a harmful agent, and a BDT could then translate that blueprint into a synthesizable genetic sequence, raising the ‘ceiling of harm’ from biological agents and potentially making them broadly accessible to a wider range of actors (arxiv.org). This integrated pipeline could significantly reduce the technical expertise and infrastructure previously required for sophisticated bioweapon development.

3.3 Other Advanced AI Capabilities and Associated Risks

Beyond LLMs and BDTs, other AI capabilities present distinct biosecurity risks:

• Autonomous Experimentation Platforms: The rise of AI-driven robotic laboratories and automated experimentation platforms (e.g., ‘cloud labs’) enables rapid, high-throughput biological experimentation with minimal human supervision. A malicious actor could potentially leverage such a system to conduct dangerous gain-of-function research, synthesize hazardous materials, or optimize pathogen characteristics at an unprecedented scale and speed, without the traditional oversight mechanisms present in conventional labs. The autonomous nature makes it harder to detect nefarious activities.
• AI for Evasion of Biosurveillance: While AI can enhance biosurveillance, it can also be used by malicious actors to evade it. AI could design pathogens or attack vectors that are less likely to be detected by existing sensor networks, epidemiological models, or genomic sequencing surveillance. This could involve optimizing release strategies to mimic natural outbreaks or designing agents with delayed symptoms to mask their deliberate origin.
• AI for Supply Chain Exploitation: AI algorithms can analyze complex global supply chains for biological materials, equipment, and expertise. A malicious actor could use this capability to identify vulnerabilities, source restricted components, or pinpoint points of illicit diversion, thereby facilitating the acquisition of necessary materials for biological weapons development.
• AI-Generated Misinformation and Disinformation: AI, particularly generative AI, can create highly realistic and persuasive misinformation or disinformation campaigns related to biological threats, pandemics, or public health interventions. Such campaigns could undermine public trust in official guidance, spread panic, or incite social unrest, thereby complicating effective responses to natural or deliberate biological events. For instance, AI could generate fake news articles or social media posts designed to sow confusion about vaccine efficacy or the origin of an outbreak.

The multifaceted nature of these threats underscores the urgency for a comprehensive, adaptive, and collaborative approach to AI biosecurity, moving beyond traditional threat perceptions to address the unique challenges posed by intelligent autonomous systems in the biological domain.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

4. Existing and Proposed Safeguards

Mitigating the complex biosecurity risks posed by AI-biology convergence requires a multi-layered approach, integrating regulatory frameworks, institutional initiatives, and specific mitigation strategies. While some safeguards exist, they are often nascent or ill-equipped to address the unique challenges presented by rapidly evolving AI capabilities.

4.1 Regulatory Frameworks: Evolving to Meet the Challenge

Current regulatory frameworks for biotechnology and AI are largely disparate and often lack comprehensive biosecurity considerations explicitly addressing the convergence. Traditional biosecurity regulations, such as those governing select agents and toxins, focus on physical containment, personnel vetting, and material transfer controls. While necessary, these do not directly address the intangible risks associated with AI models that can generate dangerous knowledge or designs.

• National AI Strategies: Many countries have begun to develop national AI strategies, but a significant gap remains in integrating biosecurity concerns within these frameworks. For example, a report by the Nuclear Threat Initiative (NTI) found that only 27% of 141 Global South countries assessed had national AI strategies, and none of these explicitly addressed biosecurity concerns (nti.org). This highlights a critical need for policy development that explicitly recognizes the dual-use nature of AI in biology.
• Emerging AI Legislation: Pioneering legislation, such as the European Union’s AI Act, represents a significant step towards regulating AI. While its primary focus is on fundamental rights and safety, it includes provisions for ‘high-risk’ AI systems. For biological applications, this could potentially cover AI used in medical devices or critical infrastructure, indirectly touching on some biosecurity aspects. However, it may not adequately address the open-source nature of many dual-use AI models or the indirect generation of harmful information. The U.S. has also issued executive orders on AI safety, calling for measures like red-teaming and responsible development (whitehouse.gov), but comprehensive legislation specifically targeting biosecurity in AI is still in development (axios.com).
• Limitations: A key limitation of existing and nascent regulatory frameworks is their inherent slowness to adapt to rapidly evolving technological capabilities. The pace of AI development often outstrips the legislative process, creating a regulatory lag. Furthermore, jurisdictional challenges arise with global AI models, as regulating their development, deployment, and use across borders proves complex. The intangible nature of AI (e.g., algorithms and data) also makes it harder to regulate than physical biological agents.

4.2 Institutional Initiatives: Industry, Academia, and NGOs as First Responders

Beyond government regulation, institutions across academia, industry, and the non-governmental sector are proactively engaging with the biosecurity implications of AI. These initiatives are often at the forefront of identifying risks and developing practical safeguards.

• AI Safety Research: Organizations like the Center for AI Safety, Anthropic, and OpenAI have dedicated research divisions focused on ‘AI safety’ or ‘AI alignment,’ which includes addressing catastrophic risks such as misuse. OpenAI, for instance, has publicly committed to preparing for future AI capabilities in biology, acknowledging the potential for misuse and advocating for responsible development (openai.com). This often involves ‘red-teaming’ AI models – intentionally probing them for vulnerabilities and misuse capabilities – to understand and mitigate risks before public release (time.com).
• Responsible AI Development: Leading AI companies and research institutions are developing internal guidelines and ethical frameworks for responsible AI development and deployment. These often include principles of safety, fairness, transparency, and accountability, with increasing emphasis on dual-use considerations for biological applications. Some are exploring methods to prevent their models from generating harmful biological information, for instance, by implementing specific content filters or training their models away from dangerous outputs.
• Academic and NGO Engagements: Academic centers (e.g., MIT Media Lab, Stanford University) and NGOs (e.g., NTI, Federation of American Scientists (FAS), Helena) are conducting crucial studies, convening expert discussions, and advocating for robust biosecurity measures for AI. These initiatives often highlight specific biosecurity implications in fields like virology and synthetic biology, pushing for greater awareness and policy development (time.com; fas.org; helena.org).
• Professional Codes of Conduct: There is a growing call for professional bodies in AI and biotechnology to develop and enforce codes of conduct that address dual-use research and responsible innovation, emphasizing the ethical obligations of scientists and developers to consider and mitigate potential harm.

4.3 Proposed Mitigation Strategies: A Practical Toolkit

To proactively address the escalating biosecurity risks from AI, several concrete mitigation strategies have been proposed and are undergoing active development and implementation:

• Pre-Release Evaluations and Independent Audits: This strategy advocates for rigorous, independent evaluations of AI models before their public or widespread deployment. Analogous to the stringent testing required for new drugs or medical devices, this approach aims to understand an AI system’s capabilities, its potential for misuse, and the effectiveness of integrated safeguards. Such evaluations should involve ‘red-teaming’ by independent experts from diverse fields (e.g., biosecurity, AI ethics, national security) to identify and test for dangerous outputs or vulnerabilities. The challenge lies in defining the scope of such evaluations, establishing independent auditing bodies, and ensuring transparency while protecting proprietary information (fas.org).
• Differentiated Access Controls and Tiered Release: Rather than a blanket open-source release, this strategy proposes a nuanced approach to AI model access based on assessed risk. High-risk AI systems (e.g., powerful foundation models capable of generating biological sequences) might be subject to differentiated access controls, ranging from restricted access (e.g., through APIs with usage monitoring), to licensing agreements, or even ‘gated’ access for vetted researchers. This approach seeks to balance the benefits of innovation and collaboration with the imperative for security. The determination of ‘high-risk’ and the criteria for access require careful deliberation and international consensus.
• Enhanced Screening Mechanisms for Gene Synthesis: The proliferation of synthetic biology means that genetic material can be ordered online and synthesized by commercial providers. Universal and enhanced screening of gene synthesis orders is a critical barrier against the successful misuse of AI in biological weapon development. AI can be leveraged here, too, by improving the accuracy and efficiency of screening against known sequences of concern and detecting ‘red flags’ in novel or modified sequences that indicate potential malicious intent. The challenge is to extend these screening mechanisms globally and to ensure that they are sufficiently robust to detect AI-generated designs (arxiv.org).
• Responsible Disclosure and Vulnerability Reporting: Establishing secure and standardized mechanisms for researchers and developers to responsibly disclose identified AI biosecurity vulnerabilities to relevant authorities or AI model developers is crucial. This is akin to ‘bug bounty’ programs in cybersecurity, encouraging ethical hacking to strengthen defenses rather than exploit weaknesses.
• ‘Safety Brakes’ and ‘Kill Switches’: For highly powerful or autonomous AI systems with potential for catastrophic misuse, the concept of technical ‘safety brakes’ or ‘kill switches’ is being explored. These would be mechanisms, either programmatic or human-initiated, to halt or significantly limit the operation of an AI system if it exhibits dangerous behavior or is suspected of being misused. The feasibility and reliability of such mechanisms are subjects of ongoing research and debate.
• Personnel Vetting and Training: Recognizing that human actors remain central to the biosecurity equation, enhanced vetting procedures for individuals working with high-risk AI and biological technologies, along with continuous biosecurity awareness training, are essential. This includes fostering a strong culture of responsibility and ethical conduct within the scientific and AI communities.

Implementing these safeguards requires close collaboration among governments, industry, academia, and civil society, emphasizing continuous adaptation as AI capabilities evolve.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

5. International Cooperation Strategies

Given the borderless nature of both biological threats and AI technologies, international cooperation is not merely beneficial but absolutely essential for effective biosecurity in the AI era. No single nation possesses the capacity to unilaterally address these complex and evolving challenges.

5.1 Global Governance Frameworks: Strengthening International Norms and Mechanisms

Establishing robust international agreements and institutions to govern dual-use AI technologies in biology is a paramount task. Lessons can be drawn from existing non-proliferation regimes, such as those for nuclear and chemical weapons, while recognizing the unique characteristics of AI:

• Reinvigorating and Adapting the Biological Weapons Convention (BWC): The BWC, while foundational, was drafted long before the advent of modern biotechnology and AI. Its Article I prohibition on biological weapons remains relevant, but its lack of a verification mechanism and its general language make it difficult to apply directly to intangible AI models. International efforts should focus on strengthening the BWC through mechanisms such as regular review conferences, enhanced national implementation measures, and discussions on how the Convention’s scope applies to AI-enabled biological threats. This includes developing shared understandings of ‘peaceful purposes’ in the context of AI-driven biological research and engineering.
• New International Instruments or Protocols: The unique characteristics of AI (e.g., rapid evolution, intangible nature, open-source availability) might necessitate new international instruments or protocols that specifically address AI’s role in biological threats. These could focus on establishing global norms for responsible AI development in biology, promoting data sharing for threat intelligence, and creating mechanisms for international risk assessment and response. However, negotiating new treaties is a lengthy and complex process, fraught with geopolitical challenges.
• Lessons from Other Regimes: Drawing parallels with nuclear and chemical security agreements offers insights. Key considerations include establishing robust verification methods (though this is far more challenging for AI and biology than for physical weapons), balancing power and capabilities between nations, ensuring frameworks are adaptable to rapid technological change, managing trade-offs between transparency and security (e.g., how much source code or model data can be shared), and designing effective enforcement mechanisms (arxiv.org). The International Atomic Energy Agency (IAEA) model of oversight and assistance could provide a template for an equivalent body focused on AI and biological risks.
• Role of United Nations and Specialized Agencies: The UN, through its various organs and specialized agencies like the World Health Organization (WHO), plays a crucial role in fostering dialogue, setting global norms, and coordinating responses. The UN could establish a dedicated expert group on AI and biosecurity to continuously monitor developments, assess risks, and propose policy recommendations. The WHO’s role in global health security, particularly in epidemic preparedness and response, naturally extends to addressing AI-enabled biological threats.

5.2 Regional Initiatives: Building Consensus and Capacity from the Ground Up

While global frameworks are essential, regional cooperation can often be more agile and responsive, serving as vital testbeds for policy development and implementation. Regional blocs can foster consistent, cross-border policies that address AI’s potential to enhance biotech research while mitigating biosecurity disruptions.

• European Union Initiatives: The European Commission has identified biotechnology as a critical area for the EU’s economic security, highlighting genetic modification and synthetic biology as key sources of both opportunity and risk (councilonstrategicrisks.org). The EU AI Act, while broad, sets a precedent for regional regulation of AI, and future iterations or complementary policies could focus more specifically on biosecurity. Regional initiatives can facilitate harmonized regulatory standards, shared risk assessments, and coordinated responses to cross-border biological incidents.
• Asia-Pacific Economic Cooperation (APEC): Within APEC, discussions on biotechnology and digital economy cooperation could integrate biosecurity concerns, promoting best practices among member economies. Given the significant biotechnological capabilities and diverse regulatory landscapes in the Asia-Pacific, regional dialogue is crucial for establishing common understandings and trust-building measures.
• African Union and ECOWAS: These regional bodies can play a vital role in developing localized strategies for AI biosecurity, tailored to regional needs and capacities. This includes fostering expertise, promoting responsible innovation, and facilitating information sharing among member states.
• Information Sharing and Threat Intelligence: Regional initiatives can foster robust information-sharing mechanisms for threat intelligence, allowing member states to share data on emerging AI-enabled biological risks, suspicious activities, and successful mitigation strategies. This is crucial for early detection and coordinated response to potential deliberate biological events.

5.3 Capacity Building in the Global South: Ensuring Inclusivity and Resilience

Engaging the Global South in global dialogues and capacity-building efforts on regulating AI models and enhancing biosecurity is not merely an act of equity but a strategic imperative. Ignoring the perspectives and capacities of these regions would create dangerous gaps in global biosecurity, potentially leading to the emergence of ‘safe havens’ for malicious actors or undermining the universality of international norms.

• Bridging the Digital and Biotechnological Divide: Many countries in the Global South lack the advanced infrastructure, technical expertise, and financial resources to develop comprehensive AI biosecurity frameworks. Capacity building should focus on providing technical assistance, training programs for scientists and policymakers, and support for developing robust regulatory and oversight mechanisms. This includes facilitating equitable access to beneficial AI technologies while embedding biosecurity safeguards.
• Integrating AI-Biosecurity into National Strategies: Supporting countries in the Global South to integrate AI-biosecurity considerations into their national AI strategies, national biodefense plans, and public health preparedness efforts is crucial. This ensures that biosecurity is not an afterthought but a foundational element of national policy.
• Advocating for Inclusive International Regulation: Countries in the Global South bring unique perspectives on issues of technology transfer, equitable access, and the potential for misuse in diverse socio-economic contexts. Their active participation in international regulatory discussions is vital for creating more inclusive, effective, and globally legitimate governance frameworks (nti.org).
• Strengthening Biosecurity Defenses and Mitigation Capabilities: This involves supporting the establishment of robust national biosafety and biosecurity systems, enhancing laboratory capabilities for pathogen detection and characterization, and building expertise in bioinformatics and AI risk assessment. It also means strengthening capabilities for rapid development and deployment of countermeasures in partnership with developed nations and international organizations.
• Funding Mechanisms: Establishing dedicated international funding mechanisms or leveraging existing ones (e.g., through the WHO or World Bank) to support biosecurity capacity building in the Global South is critical. This funding should be sustainable and targeted to address specific needs, from infrastructure development to human resource training.

Through concerted international efforts that embrace global inclusivity, the world can collectively build a more resilient and secure biosecurity architecture capable of addressing the complex challenges posed by the convergence of AI and biotechnology.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

6. Conclusion

The convergence of artificial intelligence and biotechnology represents a pivotal moment in human history, offering truly transformative potential for scientific advancement, public health, and global prosperity. From revolutionizing drug discovery and vaccine development to enabling unprecedented precision in disease diagnosis and personalized medicine, AI promises to unlock solutions to some of humanity’s most intractable challenges. However, this profound synergy is inextricably linked to significant and escalating biosecurity risks, primarily stemming from the inherent dual-use nature of AI technologies.

The ability of AI to democratize access to sophisticated biological knowledge, accelerate the design and optimization of novel pathogens, and potentially lower the technical barriers for malicious actors, introduces a complex threat landscape unlike any seen before. The misuse of large language models for generating dangerous protocols, the application of biological design tools for engineering enhanced pathogens, and the potential for autonomous experimentation all highlight an urgent imperative for proactive and robust mitigation. These threats are global in scope, transcending national borders and necessitating a coordinated international response.

Addressing these multifaceted challenges requires a comprehensive and adaptive approach. This includes the urgent development and implementation of robust regulatory frameworks that can keep pace with rapid technological evolution, moving beyond traditional biosecurity paradigms to explicitly address AI’s unique risks. It also demands a strengthening of institutional initiatives across academia, industry, and non-governmental organizations, fostering responsible AI development, pioneering AI safety research, and conducting rigorous pre-release evaluations. Crucially, a suite of proposed mitigation strategies, from differentiated access controls to enhanced gene synthesis screening, must be broadly adopted and continuously refined.

Ultimately, effective biosecurity in the AI era hinges on unparalleled international cooperation. This encompasses strengthening existing global governance frameworks, such as the Biological Weapons Convention, while exploring the necessity for new international instruments tailored to AI’s specific challenges. Regional initiatives can serve as vital testbeds for policy development and coordinated responses, and critically, robust capacity-building efforts in the Global South are essential to ensure inclusive governance, prevent dangerous capability gaps, and build a truly resilient global biosecurity architecture. By proactively implementing safeguards, fostering a culture of responsible innovation, and forging deep, sustained global collaboration, the benefits of AI in biological sciences can be realized to their fullest potential, while simultaneously mitigating the existential threats they inadvertently enable.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

References

• [arxiv.org] ‘AI Safety and Biosecurity: The Path to Responsible Innovation’ (https://arxiv.org/abs/2306.13952)
• [arxiv.org] ‘Evaluating Dual-Use Capabilities of Current AI-Enabled Biodesign Pipelines’ (https://arxiv.org/abs/2505.17154)
• [arxiv.org] ‘AI Governance: Lessons from Nuclear, Chemical, and Biological Weapons Control’ (https://arxiv.org/abs/2409.02779)
• [nti.org] ‘Risky Business: Exploring AI Biosecurity Governance in the Global South’ (https://www.nti.org/risky-business/exploring-ai-biosecurity-governance-in-the-global-south/)
• [fas.org] ‘Bio-X-AI Policy Recommendations’ (https://fas.org/publication/bio-x-ai-policy-recommendations/)
• [councilonstrategicrisks.org] ‘Advances in AI and Increased Biological Risks’ (https://councilonstrategicrisks.org/2024/07/12/advances-in-ai-and-increased-biological-risks/)
• [whitehouse.gov] ‘Remarks by Dr. Liz Sherwood-Randall, Assistant to the President for Homeland Security, on Countering Bio-Terrorism in an Era of Technology Convergence’ (https://www.whitehouse.gov/briefing-room/speeches-remarks/2024/05/07/remarks-by-dr-liz-sherwood-randall-assistant-to-the-president-for-homeland-security-on-countering-bio-terrorism-in-an-era-of-technology-convergence/)
• [openai.com] ‘Preparing for Future AI Capabilities in Biology’ (https://openai.com/index/preparing-for-future-ai-capabilities-in-biology/)
• [time.com] ‘The Pentagon Is Funding a Lab to Test How AI Could Be Used to Make Viruses’ (https://time.com/7279010/ai-virus-lab-biohazard-study/)
• [time.com] ‘The Age of AI Is Here. So Is the Risk of AI-Fueled Bioterrorism’ (https://time.com/7014800/ai-pandemic-bioterrorism/)
• [axios.com] ‘Homeland Security Official Warns of AI’s Role in Biological Weapon Development’ (https://www.axios.com/2024/06/26/ai-defense-biological-weapons-homeland-security)
• [axios.com] ‘AI Biosecurity Laws and Regulations’ (https://www.axios.com/2024/08/23/ai-biosecurity-laws-regulation)
• [axios.com] ‘AI and Superbugs: Biosurveillance Fear and Biodefense’ (https://www.axios.com/2024/05/09/ai-superbugs-biosurveillance-fear-biodefense)
• [helena.org] ‘Helena Biosecurity Project’ (https://helena.org/projects/helena-biosecurity)
• [researchgate.net] ‘The Role of AI in Biosecurity: Balancing Innovation and Risk Mitigation’ (https://www.researchgate.net/publication/389143067_The_Role_of_AI_in_Biosecurity_Balancing_Innovation_and_Risk_Mitigation)