3 Intellectual Property Risks To Avoid When Developing AI Technologies In The Life Sciences

Introduction

Artificial Intelligence (AI) is proliferating across the life science industry, accelerating drug design, diagnostics, personalized medicine, and more. The race to deploy AI technologies can make it tempting to move fast and "figure out the paperwork later." However, that approach can create intellectual property (IP) risk that that's expensive—or impossible—for companies to unwind. This article describes three common areas for IP risk—data rights, uncontrolled technical advice, and patent protection—and sets out practical steps for AI developers in the life sciences to protect value while maintaining AI development velocity.

1. Data Rights Are Not a Checkbox

Access to data does not equate to having legal rights to use it for AI training, fine-tuning, or commercial deployment. Public and private datasets frequently impose license terms that restrict downstream use, including commercialization, field-of-use, attribution, redistribution, and sublicensing. Once restricted data informs a model, it is typically infeasible to "un-train" the model to remove this contamination.

Risks

Ambiguous or insufficient data rights create material legal and business risk. Investors and acquirers scrutinize data origins and licenses as part of their diligence. Unresolved gaps in data rights can depress valuation, complicate representations and warranties, and trigger indemnity holdbacks. Contractual noncompliance with data use agreements can expose AI developers to breach claims, injunctions, loss of key inputs, and damages. Moreover, data sets that include patient data may implicate privacy or moral rights and non-compliance may impede international commercialization.

Practical Recommendations

Establish a documented data-governance program before using any dataset. Maintain a data register that tracks data sources, license terms, rights scope, field-of-use, attribution obligations, sublicensing limits, and termination triggers. Require legal review and written clearance for any dataset used in training or fine-tuning that will be productized. If feasible, limit to using data sources with clear commercial rights. Where data use terms are ambiguous or restrictive, secure express written consent or waivers tailored to the intended uses and redistribution models. Consider segmenting experimental and production training pipelines to prevent inadvertent roll-in of unvetted data – for example, gating movement of data from "research" to "production" based on legal approval.

2. Uncontrolled Advice Can Contaminate IP

Seemingly casual technical input—tips on model architectures, training recipes, data sourcing, prompt-engineering strategies, or evaluation benchmarks—from individuals outside formal company relationships can taint ownership of core AI assets. Pay particular attention when advice comes from personnel of large model labs, cloud providers, or universities, where their contributions may be subject to institutional IP and confidentiality regimes. Even well-intentioned exchanges in forums or conferences can blur inventorship, ownership of model weights and pipelines, or the origin of model training or fine‑tuning.

Risks

AI-specific risks include claims that third parties own or co-own model architectures, training pipelines, or model weights because their personnel contributed "key ideas." If an outside contributor used their employer's resources or are subject to institutional IP policies, those entities may assert ownership or march-in rights to the AI model. Use of third-party confidential information (e.g., proprietary datasets, nonpublic training tricks, machine learning rubrics, or evaluation suites) can risk trade secret misappropriation claims and injunctions that halt model training or deployment. Advice that imports license-restricted components (e.g., datasets, code, pre-trained checkpoints) can lead to license breaches and model contamination that cannot be "un-trained," jeopardizing model commercialization or investor diligence.

Practical Recommendations

Limit AI‑related collaboration to those within your company's formal relationships—employees and properly engaged contractors. Have all employees and contractors execute agreements that (i) includes present assignment of rights, (ii) prohibits sharing or receiving third‑party confidential/model‑lab information, (iii) requires disclosure of outside employer or institutional obligations, and (iv) confirms that no outside employer resources were used. Ensure invention assignments explicitly cover model development, AI know‑how, and derivative works. Establish clear "no‑improper‑source" ground rules for model development and training and avoid adopting unvetted training tricks, datasets, or checkpoints from online forums, direct messages, or threads. Centralize communications on company systems and ban use of personal accounts or unsanctioned repositories for dataset curation, fine‑tuning, or machine learning workflows. Maintain dated records of conception and reduction‑to‑practice tying specific model improvements to inventors.

3. Patent Protection: Beyond "Do It With AI"

Patents may be an attractive way to distinguish and protect your AI model from competitors, but eligibility and patentability require more than applying generic AI to known tasks. Strong candidates demonstrate technical improvements or capabilities not feasible with conventional methods, such as new architectures, training regimes, inference techniques, or control systems that yield measurable performance or reliability gains. Timing filings around technical milestones and public disclosures is critical.

Risks

Applications that merely automate known processes with AI face subject-matter eligibility and obviousness rejections. Public disclosures, open-source releases, conference papers, or product launches can start the patent clock or even bar patentability in some countries. Insufficient technical detail in a patent application can jeopardize enablement or written description, particularly for model training data, hyperparameters, or specialized system configurations. Misaligned patent strategy can waste resources on narrow claims that are easy to design around or hard to enforce.

Practical Recommendations

Work with patent counsel early to identify claimable advancements, develop claim strategy, and determine the appropriate technical detail to include in patent applications. Keep patent counsel informed about technical and business milestones and potential for public disclosures.

Conclusion

IP diligence is a force multiplier for AI technologies in the life sciences: disciplined data rights, controlled collaboration, and thoughtful patenting can facilitate freedom to operate, reduce litigation risk, and preserve exit-ready assets. Treat sensitive datasets, outside contributors, and distinctive model capabilities as triggers for proactive legal review. By integrating governance into the development lifecycle, life sciences organizations can scale AI confidently while safeguarding long-term enterprise value.