Data continues to be one of the most valuable assets in life sciences transactions. As pharmaceutical and biotech companies increasingly seek to leverage AI and machine learning technologies to accelerate drug discovery, optimize clinical trials, and develop personalized therapies, the strategic importance of data, such as proprietary drug research, patient information, and clinical trial results, has grown in importance. This shift is transforming how parties negotiate and structure deals, with data ownership, access rights, format and usage restrictions now occupying a central place at the transaction table. For dealmakers and their counsel, understanding the legal and commercial implications of data scope, data ownership, and use of data are essential to capturing value and mitigating risk in an evolving landscape.
Scope of Data
In any deal, the parties first consider the scope and source of data. In 2026, data creation and collection are faster than ever, accelerating the pace at which valuable datasets are generated and refined. As a result, more life sciences deals consider not only the data that parties already own at the Effective Date, but also the data that they might individually own in the future and that they might jointly create through their collaboration. Additionally, it is not uncommon to negotiate the use of third-party data that is combined or synthesized with existing data or synthetic data that is manufactured by AI tools. Life sciences deals are increasingly incorporating highly negotiated defined terms for each category of data, and these defined terms are increasingly covering data that is manufactured, synthesized, or generated.
As data volumes grow, dealmakers are experiencing longer diligence cycles and an increased need for audits. Understanding where data comes from, how it is generated, and what rights the purported owner has in the data is key to mitigating future risk. This requires a more rigorous diligence effort, such as tracing data provenance, verifying chain of title, and confirming that appropriate consents, licenses, and regulatory approvals are in place. In an environment where data can be aggregated, transformed, or synthesized across multiple sources, parties are investing more time and resources to ensure that the data assets being acquired or licensed are free of encumbrances and fit for their intended purpose. Further, these obligations continue post-signing, so parties are increasingly asking for complex audit rights to ensure ongoing compliance.
Data Ownership
Data ownership in life sciences agreements can take many forms, and the structure chosen often depends on the nature of the transaction, the relative bargaining power of the parties, and the strategic value of the underlying data. In some deals, one party retains sole ownership of all data generated or used in connection with the collaboration. This approach is common when a sponsor funds a clinical trial, or a licensor contributes proprietary datasets. Material contributions of capital and prior intellectual property often lead to sole ownership. In others, data may be jointly owned, with each party holding an undivided interest and the right to use, license, or exploit the data independently (or subject to negotiated restrictions). Ownership may also be allocated based on inventorship principles, particularly where the data relates to patentable discoveries, or by technology type (if each party is contributing distinct technologies), such as distinguishing between raw data, processed data, analytical results, and algorithms.
Increasingly, parties highly negotiate who owns improvements, derivative works, and new data generated from existing datasets. This issue frequently arises when AI systems are trained on licensed data or when parties collaborate on iterative research. Additionally, there are more AI summaries or infographics that can be quickly generated from a massive dataset. In some instances, the consumable summary or infographic has separate and distinct value causing ownership rights to be separately negotiated.
Data Use
Beyond ownership, parties must carefully negotiate data use rights and restrictions. Even where a party has access to valuable data, the scope of permitted use is often tightly controlled. Common restrictions include requirements to use data only in compliance with applicable laws and regulations, which are particularly important in life sciences given the overlay of privacy laws (such as HIPAA and GDPR), FDA regulations, and state data protection statutes. Agreements frequently prohibit reverse engineering of datasets or algorithms, as well as any attempt to re-identify anonymized or de-identified patient data. Occasionally, when the parties have competitive pressure, competitive use restrictions are also included, preventing a party from using licensed data to develop or support products or services that compete with the data provider’s own offerings. In addition, parties often limit data use to a specified field of use, such as a specific therapeutic or diagnostic area, indication, or technology platform. Alternatively, fields of use can be specified by internal research use only or external commercial use limitations. These field-of-use restrictions ensure that the data provider retains the ability to monetize the data in other contexts while granting the licensee meaningful rights within the agreed scope.
Increasingly in 2026, life science companies are leveraging AI agents and automated workflows, which can use and create data. Some parties require strict AI prohibitions with respect to certain data while others allow AI use under certain specifically defined restrictions. Strict prohibitions are becoming less common as AI use becomes more ubiquitous, especially in research and analysis settings. More typically, parties are prohibiting public AI tools that transfer ownership to the service provider and allow AI tools under an enterprise license that provides adequate cybersecurity assurances and preserves ownership of intellectual property rights.
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The value of data assets in life sciences transactions will likely continue to accelerate, raising the importance of scope of data, data ownership and data use during the negotiation. Understanding these terms is essential for finding value and managing risk in an environment that is quickly changing.
This article is a part of the 2026 Life Sciences Industry Report. Click here to read the full newsletter.
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