Global Data Privacy in Life Sciences: One Framework, Many Realities

🌎 The Landscape has changed drastically

A decade ago, a Life Sciences company operating across multiple countries worldwide could reasonably manage its Data Privacy obligations with a single privacy team, a well-drafted informed consent form, and a solid understanding of the different national laws. That era is over!

Since the General Data Protection Regulation (GDPR) became applicable on May 25, 2018, privacy teams have had to absorb not only a mature and comprehensive regulatory framework but also an evolving landscape of adjacent legislation. While the GDPR is trying to “omnibus” its general principles, other regulations have come into force or have been updated. The EU Clinical Trial Regulation has applied since January 31, 2022. CTIS has been mandatory for new EU Clinical Trial applications since January 31, 2023. The European Health Data Space was published on March 5, 2025, and entered into force on March 26, 2025. The UK GDPR has operated as a retained framework since the end of the Brexit transition period on December 31, 2020. China’s PIPL took effect on November 1, 2021, and Brazil’s LGPD largely took effect on September 18, 2020.

The result is not exactly regulatory chaos, but rather a fragmented compliance landscape. This fragmentation creates significant challenges for organizations conducting multinational Clinical Trials, collaborating with Contract Research Organizations (CROs) and clinical sites, and transferring Health Data across jurisdictions.

🧬 Why this change is more significant for the Life Sciences sector

Most industries process Personal Data primarily in customer and employee contexts. By contrast, the Life Sciences sector deals with special categories of data, including health and genetic data. A single Phase III Clinical Trial may involve participants across fifteen countries, biological samples stored in three different jurisdictions, and data flowing between a Contract Research Organization (CRO), Sponsor, Regulatory Authorities, and independent ethics committees. Every data transfer, every consent form, and every data retention schedule must comply with multiple legal frameworks, which do not always align.

Let’s take the example of the section dedicated to Data Protection in the informed consent form.

Across the EU, reliance on the scientific research derogation under Article 9(2)(j) does not guarantee a harmonized approach to the legal basis under Article 6. France generally relies on the Sponsor’s legitimate interests, Germany requires participants’ consent, while Spain may rely on compliance with a legal obligation. As a result, Sponsors running multinational Clinical Trials often need to adapt the ICF from one Member State to another, preventing a uniform consent framework across the EU.

Cross-border data transfer requirements provide another illustration of this complexity. The EU’s Standard Contractual Clauses (SCCs), updated in June 2021, remain the primary transfer mechanism for data leaving the EEA. However, China requires a government-administered security assessment for such transfers. India is still finalizing its cross-border transfer rules under the DPDPA. As a result, managing cross-border transfers in a global clinical research context can quickly become highly complex without a structured and centralized compliance approach.

🔬 Clinical Research: A sector within a sector

Clinical research deserves specific attention, as it combines almost every Data Protection challenge in one process. Sponsors must navigate the interplay between Data Protection law and Good Clinical Practice (GCP), which, while originating as international ICH standards, are now embedded in the Clinical Trial regulatory framework and form an integral part of applicable regulatory requirements.

The ICH E6(R3) guideline, finalized in 2023, together with its recent Annex 2 on modern and decentralized trial designs, provides greater flexibility and updated expectations regarding data integrity and the risk-based approach, as well as explicit recognition of electronic systems and multiple data sources, alongside strengthened requirements for system validation, security, reliability, and the governance of outsourced data. However, it does not resolve the tension between the GDPR’s data minimization principle and the clinical necessity of collecting comprehensive participant data for regulatory submission purposes. This has led to ambiguous questions such as: Can we collect the full date of birth? Can we collect race and ethnicity?

There is also the question of secondary use. Health data collected in the Clinical Trial context is valuable for real-world evidence generation, enabling deeper insights into treatment effectiveness, safety, and outcomes in routine clinical practice. However, reusing those data for another purpose might trigger legal analysis under almost every Data Protection law. The GDPR’s concept of a “compatible purpose” under Article 6(4) provides some flexibility, but its application across EU Member States is not uniform.

Finally, Decentralized Clinical Trials (DCTs) have added another layer of complexity to an already fragmented regulatory landscape. A participant in Germany uploading device data to a cloud platform operating in the US, with the output received by a medical monitor in Japan, may involve at least three regulatory frameworks before a single clinical data point reaches the Sponsor’s database.

🧩 What does a structured framework look like?

The good news is that the underlying principles of Data Protection are more consistent across jurisdictions than the specific rules suggest. Lawfulness, fairness, transparency, purpose limitation, data minimization, accuracy, storage limitation, security, and accountability are not purely European concepts. They appear, in various ways, in every significant Data Protection law established over the past decade. These principles are the basis on which a coherent global Data Protection framework can be built, typically across three layers.

The first layer is a common policy structure. Global policies set minimum standards based on the most protective applicable law, supplemented by jurisdiction-specific annexes addressing local requirements. This avoids the need to simultaneously cover every law and eases the process of updating the framework when new Data Protection laws are introduced.

The second layer is integration into the process. Data Protection should not be a checkpoint at the end of the study design process. On the contrary, it should be embedded where decisions are made. For example, in protocol design, vendor selection, data architecture, and transfer mechanisms. The so-called Privacy-by-Design principle (Article 25) now appears in most frameworks. Taking it into consideration from the beginning of the processing is more than a documentation exercise: it decreases risk and improves quality.

The third and final layer is governance and decision support. Complex decisions require a mechanism for reaching consistent and documented conclusions. This mechanism can be a cross-functional Privacy committee, a documented escalation path to the DPO, or standardized decision templates for recurring scenarios. It needs to be flexible, agile, and adapted to the regulatory expectations across the relevant jurisdictions targeted by the company.

💡 Key takeaway

The goal is not to achieve perfect compliance in every jurisdiction at the same time. Rather, the goal is to build an organization that can quickly identify the relevant requirements, apply consistent principles, and make well-documented decisions when the rules do not provide a clear answer.

As we have seen, the fragmentation is real, but a structured Privacy framework reduces friction and improves the quality of decisions made under regulatory uncertainty. Organizations working with experienced Data Protection advisors who understand both the regulatory landscape and the operational realities of the Life Sciences sector are better prepared to navigate this environment.

Author: Gregory Collet, PhD, CIPP/E