Sovereign AI as the backbone of LSHC GCC innovation: Prioritising data privacy in a digital age

In recent times, Global Capability Centres (GCCs) have become drivers of innovation for global Life Sciences and Healthcare (LSHC) companies. These centres are constantly engaged in drug discovery, drug development, regulatory practices, and other commercial functions.¹ With this increased role in the company’s performance, these GCCs are also held with increased responsibility. These centres handle huge volumes of protected health information (PHI), clinical trial data, and proprietary IP, and such assets require efficient governance to safeguard patient interests.^{2, 3, 26}

In this context, sovereign AI has emerged as the best solution available to these GCCs. These are AI systems developed, trained, and deployed within a specific jurisdiction’s legal and technical boundaries. This approach gives greater control to organisations over where data resides, how it is processed, and who can access it.⁴ This setup turns out to more relevant for LSHC GCCs, which are navigating complex global regulations while ensuring the confidentiality of patient and research data.²

In 2023, healthcare data breaches hit a record high, reaching 725 incidents involving 500 or more records exposing 133 million patient records in the US alone.⁵ The financial stakes are also equally steep, with average breach costs in the healthcare sector hitting $10.93 million per breach.⁶ These instances clearly highlight the urgent need for sovereign AI, as GCCs can no longer afford to treat data privacy lightly. This article explores the key risks LSHC GCCs face, how sovereign AI can mitigate them, and what global examples like France and the UAE teach us about getting it right.

Key risk areas in LSHC GCCs

Regulatory complexity and data breaches

LSHC GCCs often have to handle sensitive patient data across geographies, and consequently require adherence to multiple jurisdictions, like the US (HIPAA), EU (GDPR), and APAC countries with stringent localisation laws. Having operations under multiple jurisdictions often leads to increased risk of non-compliance and data breaches.⁷

For instance, it is reported that between 2009 and 2023 the US alone saw 5,887 healthcare data breaches, exposing over 519 million patient records, which means approximately 365,000 healthcare records were breached per day in 2023.⁸

Dependency on opaque third-party AI tools

The use of AI in healthcare and R&D has become indispensable. Therefore, in the absence of sovereign AI, LSHC GCCs often end up using external AI platforms that provide minimal visibility into data residency, data flows, training data, and model logic. Use of such external platforms, which are being utilised by multiple stakeholders, can significantly increase the risk of data losses and exposure.^{9, 10}

A major example is the MOVEit breach, where a software supply chain vulnerability affected more than 1,000 organisations and more than 62 million individuals, including various healthcare institutions. This incident clearly highlights the risks of depending on third-party tools without sovereign control.¹¹

Cross‑border data transfers and IP risk

A significant number of LSHC GCCs are set up to handle regulatory submissions, manage clinical trials, and support R&D initiatives. All these functions usually require collaboration and data exchange across borders. Without sovereign infrastructure, there is high risk of data breaches in such operations. As a result, governments put various restrictions on data transfers across borders, which eventually affects trade and business.⁷

According to the Information Technology & Innovation Foundation (ITIF), every one-point increase in data transfer restrictiveness leads to a 7% drop in trade output and 2.9% slower productivity growth.¹² For LSHC GCCs managing high-value research data, this can be a critical strategic concern.

How sovereign AI addresses these risks

Jurisdictional compliance by design

Sovereign AI is built and designed to ensure that data and modelling remain within legally defined boundaries like in-country data centres, sovereign clouds, or enterprise-controlled environments. This infrastructure reduces the risk of data leaks and makes compliance with global regulations like GDPR, HIPAA, and national privacy acts easier.¹³

For instance, Germany’s Bundescloud provides a sovereign infrastructure for public sector workloads, including healthcare, ensuring full legal compliance while maintaining operational autonomy.¹⁴

Explainable, clinically valid models

Many LSHC GCCs handle functions like clinical trials and operations, which mandate high regulatory scrutiny and explainability of the data generated. Sovereign AI makes this possible by allowing full visibility and tracking of training data, model performance, and decision logic.¹⁵

A survey of 1,000 physicians in the US revealed that over 70% would be unlikely to adopt AI systems lacking explainability, even if they demonstrated high accuracy.¹⁶ The transparency offered by sovereign AI can be very useful for auditability in clinical settings, where explainability is mandated for approvals and safe deployment.¹⁵

Full ownership of data, models, and IP

One of the key advantages of sovereign AI is that the organisations can maintain end-to-end control of models, data, and intellectual property since the infrastructure is developed in-house or within national cloud zones. This ensures research data, proprietary algorithms, and clinical insights are not exposed to third-party risk.¹⁷

For example, France’s Health Data Hub centralises national health data on local infrastructure, ensuring compliance while supporting over 100 AI-enabled public research initiatives.¹⁸

Case study 1: France’s Health Data Hub

Launched in 2019, France’s Health Data Hub (HDH) is a platform that combines health databases and facilitates its use for research and development. The hub is built under the GDPR-compliant Commission Nationale de l’Informatique et des Libertés (CNIL) framework, which prohibits use and exploitation of health data unless it is for medical purposes.

It originally used Microsoft Azure’s French data centres, but is slowly moving to more sovereign infrastructures to strengthen data privacy. HDH has more than 1,600 submitted projects and around 173 affiliated researchers, setting an example of how sensitive data can be used for innovation without compromising data privacy or public trust.^{18, 19, 20, 21}

Case study 2: UAE’s healthcare data sovereignty

Understanding the importance of healthcare data privacy and risks of data breaches, the UAE mandates local storage and processing of all patient data under Law No. 2/2019, with Law No. 49/2023 extending this to AI models trained on genomic data unless explicitly approved otherwise.

To further strengthen data protection, the UAE has partnered with firms like G42 and Microsoft Azure to host AI applications in sovereign cloud environments. This framework lets the UAE advance public health AI while retaining full control over citizen data.^{22, 23, 24, 25}

LSHC GCCs are at a point where use of AI in healthcare functions has become indispensable. As much as this development boosts innovation, it significantly increases the risk of non-compliance and data breaches.²⁶ As discussed earlier, the average cost of healthcare data breaches has reached nearly $11 million, highlighting the importance of building a privacy-first AI infrastructure.⁶

Sovereign AI has become the need of the hour in this context, especially for LSHC GCCs, as they are involved in handling of a large volume of critical and confidential data. This shift can enable LSHC GCCs to innovate with control, transparency, and resilience.¹⁷ For GCCs navigating an increasingly complex data landscape, that’s not just smart policy, but a business imperative.