Biosecurity and AI: The Dual-Use IP Challenge and Investment Opportunity

The convergence of artificial intelligence and biology is producing some of the most powerful and most contested technologies in the history of science. AI systems can now accelerate drug discovery by orders of magnitude, design novel proteins with therapeutic properties, analyze genomic data at population scale, and predict the evolutionary trajectories of pathogens with unprecedented accuracy. These capabilities represent genuine scientific and medical progress — and they also create profound biosecurity challenges, because the same tools that accelerate beneficial biology also accelerate the development of dangerous biological agents.

This dual-use reality sits at the heart of what makes biosecurity AI one of the most complex investment categories in deep tech. The IP that enables rapid vaccine development also, in principle, enables more sophisticated pathogen engineering. The sequence analysis algorithms that help public health authorities track disease spread also, in principle, help bad actors identify vulnerability patterns in natural pathogens. Managing this duality — developing genuinely useful biosecurity AI while implementing appropriate safeguards and navigating the regulatory landscape — requires a level of ethical and strategic sophistication that not every team is capable of delivering.

The Commercial Landscape: Defensive AI as a Growth Market

Despite the complexity, the commercial opportunity in biosecurity AI is large and growing rapidly. Government spending on biological threat detection, pandemic preparedness, and biodefense has increased dramatically since the COVID-19 pandemic demonstrated the catastrophic consequences of insufficient preparedness. In the United States, the Bioeconomy Executive Order of 2022 and subsequent congressional appropriations have channeled significant funding toward AI-enabled biosecurity capabilities. Similar investments are being made by the European Union, United Kingdom, and a range of allied governments.

The private sector market for biosecurity AI is equally compelling. Pharmaceutical companies are investing in AI-powered pandemic preparedness capabilities that can accelerate the development of novel vaccines and therapeutics. Agricultural biotech companies are deploying AI for crop disease surveillance and biosecurity monitoring of food supply chains. Life sciences research institutions are building AI systems for enhanced biosafety monitoring of laboratory practices. Each of these markets creates commercial opportunities for specialized AI companies with appropriate IP coverage.

From an IP perspective, the commercial defensive biosecurity AI market is less crowded than the general AI market. Many of the most capable AI companies have chosen to avoid biosecurity applications because of the regulatory and reputational complexity involved, leaving genuine white spaces for well-positioned deep tech startups. Companies that develop validated, government-approved biosecurity AI capabilities with comprehensive IP coverage are building moats that are extremely difficult for later entrants to replicate quickly — both because of the technical depth required and because of the time required to obtain the regulatory approvals and government certifications that major biosecurity customers demand.

Core IP Categories in Biosecurity AI

The biosecurity AI IP landscape is organized around four primary technical domains, each with distinct commercial applications and IP dynamics.

Pathogen Detection and Identification: AI systems for rapidly identifying known and novel biological agents — from AI-powered diagnostic sequencing platforms to anomaly detection systems for environmental biosurveillance networks — are the most commercially mature segment of biosecurity AI. The core IP in this category involves the specific algorithms, training data structures, and hardware-software co-designs that enable faster and more accurate pathogen identification than existing methods. Companies with validated detection performance and comprehensive patent coverage in this space are actively sought as acquisition targets by defense primes, major diagnostics companies, and life sciences conglomerates.

Genomic Risk Assessment: AI systems that can assess the risk profile of genetic sequences — identifying functional elements that indicate pathogen virulence, transmissibility, or resistance to known countermeasures — represent a dual-use technology with particularly sensitive IP considerations. The core methods are patentable as applications of AI to biological risk analysis, but the IP prosecution strategy must be carefully designed to protect commercial defensive applications without inadvertently creating roadblocks to legitimate research or inadvertently disclosing information that could assist malicious actors.

Countermeasure Development Acceleration: AI tools for accelerating the development of vaccines, therapeutics, and medical countermeasures against biological threats are among the highest-value categories in biosecurity AI. These tools overlap substantially with the commercial drug discovery AI market — the same techniques that accelerate vaccine design for pandemic preparedness also accelerate drug discovery for commercial indications. Companies that develop AI platforms with proven applications in both commercial drug discovery and biosecurity countermeasure development can pursue a dual-track business model that diversifies their revenue base while maximizing the commercial value of their IP portfolio.

Screening and Access Control: AI-powered screening systems for biosafety monitoring — analyzing laboratory personnel access patterns, equipment usage, and sample handling to detect anomalous behaviors that may indicate biosafety breaches — represent a growing commercial market as biosafety regulations tighten globally. The IP in this category is relatively less contested than in the core biology AI categories, creating good opportunities for well-funded startups to establish defensible positions before the major security technology companies enter the space.

The Dual-Use IP Challenge: Publishing vs. Protecting

The most distinctive IP challenge in biosecurity AI is the publication dilemma. Scientific publication is essential for commercial validation — government customers and pharmaceutical partners need to see peer-reviewed evidence of performance before they will procure biosecurity AI systems — but publishing detailed methods in biology AI creates potential dual-use risks that other technology categories do not face.

The research community has developed a framework of "responsible disclosure" for dual-use research of concern (DURC), which requires authors to assess whether their findings provide meaningful uplift to potential bad actors before publication. But this framework was developed for wet-lab biological research, not for AI methods, and its application to biosecurity AI is still being worked out. Companies in this space need to engage actively with biosecurity policy bodies — the National Science Advisory Board for Biosecurity in the United States, the Dual Use Advisory Committee of the UK Health Security Agency, and equivalent bodies in other jurisdictions — to develop publication strategies that maximize scientific credibility while managing dual-use risks.

From an IP strategy perspective, the DURC framework creates an unusual dynamic: in some cases, the appropriate IP strategy for a biosecurity AI method may be to maintain it as a trade secret rather than a patent, specifically to prevent the dual-use risks of public disclosure through the patent publication process. This trade-off between patent protection (which requires disclosure) and trade secret protection (which allows nondisclosure) must be evaluated carefully for each specific innovation in the biosecurity AI portfolio.

Navigating the Regulatory and Compliance Landscape

Biosecurity AI companies operate in one of the most complex regulatory environments in all of deep tech. Federal oversight from DARPA, BARDA, the NIH, the FDA, and the Departments of Defense and Homeland Security creates a layered compliance environment that can be extremely challenging to navigate. But the regulatory complexity is also a barrier to entry that protects established players and creates significant value for companies that have invested in regulatory expertise from early in their development.

For NL Patent AI Capital portfolio companies in biosecurity AI, we actively facilitate connections to regulatory specialists who understand the biosecurity regulatory landscape and can help structure the product development process to align with regulatory expectations from the beginning. The companies that try to develop their AI systems first and think about regulatory approval second consistently face expensive, time-consuming remediation challenges. The companies that design for regulatory compliance from day one are able to generate the evidence base — clinical validations, verification and validation datasets, algorithmic transparency documentation — that government customers require.

Investment from specialized investors — particularly those with established relationships with government customers and policy bodies — is itself a signal of commercial viability that biosecurity AI companies can use in their government procurement activities. Our fund's position in the biosecurity AI ecosystem, combined with Draper Associates' broader networks, provides our portfolio companies with a commercial credibility that standalone deep tech startups typically cannot achieve on their own at the seed stage.

For broader context on how NL Patent AI Capital evaluates IP in high-complexity categories, read our piece on Valuing AI Intellectual Property or visit our About page.