AI Biosecurity: Safe AI for Drug Discovery in the US

Most pharma teams are sprinting toward AI-driven drug discovery—and only a few are asking the harder question: what happens when models get “good enough” at biology to be dangerous?

That question isn’t academic. The same core skills that help a model suggest promising targets, summarize assay results, or reason over biological pathways are the skills that could lower the barrier for misuse. OpenAI’s June 2025 update on preparing for future AI capabilities in biology is a useful signal for where the U.S. tech ecosystem is headed: faster science, tighter safeguards, and more formal coordination with government and national labs.

For readers following this “AI in Pharmaceuticals & Drug Discovery” series, this moment matters because it reframes “AI safety” as more than policy talk. It becomes a practical requirement for any organization building AI into R&D, clinical ops, or lab workflows—especially as vendors begin offering increasingly capable models via digital services.

Why “better biology models” change the risk math

AI models in biology don’t just answer questions; they increasingly connect dots across modalities—papers, protocols, omics data, molecular structures, and experimental outcomes. That’s exactly what makes them valuable for pharmaceutical R&D.

But it also changes the risk equation in a specific way: capability compounds access. If a model can reason through biological problems at a high level, then the remaining barrier isn’t knowledge—it’s physical access (labs, materials, synthesis). Physical barriers help, but they’re not absolute.

Here’s the key point: dual-use is the default in life sciences. Many legitimate topics—virology, immunology, genetic engineering, protein engineering—can be used for beneficial research or harmful outcomes depending on intent and detail.

What “High capability in biology” really implies

OpenAI uses a Preparedness Framework that includes a “High” threshold for biology. The practical interpretation is blunt:

A “High” biology-capable model can provide meaningful assistance to novice actors with basic training, potentially enabling creation of biological or chemical threats.

For U.S. pharma and biotech leaders, this framing is useful even if you never touch that framework directly. It tells you what regulators, enterprise buyers, and platform providers are preparing for: a world where model output quality becomes sensitive infrastructure.

The upside: AI in drug discovery is already paying off

If you work in pharma, you’re not adopting AI because it’s trendy. You’re adopting it because timelines and costs are punishing.

A widely cited industry reality is that bringing a new drug to market often takes 10+ years and costs billions of dollars once you account for failures. Even modest improvements in early-stage decision quality can have outsized impact.

OpenAI points to models helping predict which drug candidates are more likely to succeed in human trials—a theme that fits the current direction of U.S. biotech: use AI to increase the probability of success per dollar spent.

Where this shows up in real workflows:

• Target identification and prioritization: synthesizing evidence across literature, pathways, and omics.
• Hit-to-lead and lead optimization: helping chemists and biologists iterate faster on hypotheses.
• Biomarker strategy: connecting clinical endpoints to mechanistic signals.
• Trial design support: summarizing inclusion/exclusion criteria tradeoffs and operational constraints.

My take: the biggest near-term win isn’t a fully automated “AI scientist.” It’s decision compression—getting from “we have 50 plausible options” to “we can justify testing 5” with clearer reasoning and documentation.

The downside: why step-by-step bio guidance is a different class of output

A common misunderstanding is that dangerous information is always obviously dangerous. In biology, the line is messier.

OpenAI’s safety approach makes a strong choice: for dual-use biological requests, models should provide high-level insights that support expert understanding while avoiding actionable step-by-step instructions and wet lab troubleshooting.

That matters because the riskiest assistance often isn’t “what is a virus?” It’s:

• Troubleshooting why an experiment failed
• Suggesting specific parameter tweaks
• Identifying practical alternatives when a method is blocked
• Providing procedural sequencing that reduces tacit knowledge gaps

In pharma R&D, those details can be perfectly legitimate inside a controlled environment. But at internet scale, the safest default is restraint.

A useful rule of thumb for AI teams

If you’re building AI copilots for R&D, a clean internal guideline is:

• General-access models: explanation, conceptual guidance, literature mapping, risk-aware summaries.
• Vetted-access systems: procedural detail, parameter guidance, troubleshooting—paired with stronger identity, logging, and governance.

You don’t want to discover after rollout that your “helpful assistant” is effectively a protocol generator.

What “defense in depth” looks like for AI biology services

Safety in AI biology can’t be one feature. It has to be a system. OpenAI describes multiple layers that, taken together, resemble what mature security programs do in other high-risk domains.

1) Training models to refuse or respond safely

The first layer is behavioral: training the model to refuse explicitly harmful requests and to handle dual-use prompts cautiously.

For pharma companies, the parallel is obvious: you’ll need model behavior policies that align with your use cases. If your assistant supports lab ops, your “allowed” set is broader—but your controls must be stronger.

2) Always-on detection for risky activity

OpenAI describes “always-on” monitors across product surfaces that can block unsafe responses and trigger automated or human review.

This is where many enterprise AI programs are still immature. If you can’t answer these questions, you’re exposed:

• What categories of bio-related prompts do we detect?
• Do we monitor prompts, outputs, or both?
• What’s the escalation path when something looks suspicious?
• Can we audit who asked what, when, and why?

3) Monitoring and enforcement

Policies without enforcement are just documentation. OpenAI emphasizes prohibiting harmful use and enforcing it, including account suspension and potential law enforcement notification for egregious cases.

In a corporate setting, “enforcement” becomes:

• Role-based access controls
• HR and legal workflows for misuse
• Vendor escalation and incident response playbooks
• Clear consequences for policy violations

4) Expert red teaming (and why biology is harder)

OpenAI calls out a real operational challenge:

• Many traditional red teamers know how to jailbreak systems but lack deep biology expertise.
• Many biology experts can assess harmfulness but don’t know how to attack AI systems.

Pairing those skill sets is the right move. For pharma and biotech, the equivalent is building review teams that combine:

• Computational biology / lab SMEs
• AI security / adversarial testing specialists
• Compliance and biosafety officers

5) Security controls for model weights and infrastructure

For organizations offering AI as a digital service, protecting models is a security problem, not a product preference. OpenAI describes weight protection via access controls, infrastructure hardening, egress controls, monitoring, and insider-risk programs.

If your team is hosting fine-tuned models for internal R&D, apply the same mindset: treat models and their training data like crown jewels, especially when they encode sensitive know-how.

What U.S. pharma and biotech leaders should do in 2026 planning

OpenAI’s note about hosting a biodefense summit and deepening partnerships with government and national labs points to a broader direction in the U.S.: AI-in-biology governance is professionalizing fast. Expect procurement, compliance, and security reviews to tighten.

Here are concrete actions that pay off whether you’re a pharma company, a biotech startup, or a vendor selling AI into regulated customers.

Create a “dual-use boundary” for your AI products

Write down what your system will and won’t do, with examples. Not legalese—operational rules engineers can implement.

A strong boundary statement includes:

• Prohibited request categories
• Allowed-but-limited categories (high-level only)
• Vetted-access categories (full detail with controls)
• Escalation triggers (what gets reviewed by humans)

Add biosafety to your model evaluation stack

Many teams evaluate AI assistants on helpfulness and accuracy. In life sciences, you also need:

• Actionability scoring: does the answer enable someone to do something in the real world?
• Harm potential review: evaluated by domain experts.
• Jailbreak robustness: tested by adversarial prompts.

The goal isn’t perfection. The goal is measurable risk reduction before scale.

Treat logs and review workflows as product features

If you’re building AI tools for drug discovery, don’t bolt on governance later. Design for:

• Immutable audit logs
• Case management for reviews
• Clear user identity and role verification
• Data retention aligned to regulated expectations

This is the unglamorous work that wins enterprise deals.

Plan for “tiered access” from day one

OpenAI signals interest in granting vetted institutions access to more helpful models while keeping general access more constrained.

That tiering is coming to the broader market. Buyers will increasingly ask for:

• Separate model routes for general users vs. regulated teams
• Stronger guarantees about what the model can output
• Organization-level controls (not just user settings)

People also ask: will AI slow down biology innovation because of safety?

Safety controls don’t have to slow innovation, but they do change how innovation is delivered.

My stance: the fastest path is a two-track system:

• Broad, high-level AI support that accelerates reading, synthesis, and hypothesis generation for everyone.
• Narrow, deeply integrated AI lab assistance for vetted teams with strong guardrails.

If you’re a U.S.-based pharma or biotech company, this is actually good news. It means the organizations willing to invest in governance and secure AI infrastructure will get access to more powerful capabilities sooner—and will be able to use them responsibly.

Where this fits in the “AI in Pharmaceuticals & Drug Discovery” story

AI in drug discovery is moving from “nice assistant” to “core capability.” The next phase isn’t just about smarter models—it’s about trusted deployment: monitoring, red teaming, enforcement, and security controls that make advanced AI usable in high-stakes scientific environments.

If you’re building or buying AI for pharmaceutical R&D in 2026, take this as the baseline expectation: your AI roadmap is also your risk roadmap. Teams that treat biosecurity as part of product quality will move faster—and will sleep better.

What would change in your organization if you assumed that, within a couple of model generations, AI assistance in biology becomes as sensitive as access to certain lab facilities?