Data localisation is becoming an increasingly common policy as governments seek to protect sensitive health information, retain control over critical datasets, and ensure that data is governed under national legal frameworks.
These aims are legitimate and, particularly in the healthcare context, often well-founded. Health data is among the most sensitive categories of personal information, and governments have valid reasons to prioritise patient trust, regulatory oversight, and the resilience of national health systems. Many organisations are now consulting data protection law firm and lawyers to navigate evolving localisation and compliance requirements in healthcare sectors.
The challenge is that, in practice, some healthcare data localisation laws go further than many general data protection regimes by requiring health data to remain in-country while not even providing the usual cross-border transfer mechanisms, such as adequacy determinations, standard contractual clauses, or binding corporate rules.
For healthcare and MedTech businesses operating across multiple markets, that creates a very different operating environment; one in which the law may be clear, but the data itself becomes much harder to use in ways that support modern care, product improvement, and regional or global scale.
Data Fragmentation: A Cascade of Risks
The biggest consequence of localisation is fragmentation. Healthcare systems and connected MedTech products depend on access to broad, diverse, and continuously updated datasets.
i. Loss of Data Scale and Learning Capability
- When data must be kept in separate national or sector-specific silos, companies lose the ability to train, validate, benchmark, and improve their tools across larger populations. That matters because AI tools, predictive analytics, remote monitoring systems, and continuously improving software all perform better when they can learn from a wider range of clinical contexts.
ii. Reduced Accuracy and Product Effectiveness
- In fragmented data environments, algorithm accuracy can weaken, model updates become less effective, and benchmarking across different populations becomes less reliable. A product that performs well on a narrow local dataset may not behave the same way when clinical workflows, demographics, co-morbidities, or disease prevalence change.
- The result is not just a technical inconvenience; it can reduce product usefulness, slow innovation, and make healthcare companies less able to deliver the outcomes that regulators, providers, and patients want from digital health. Businesses often seek guidance from the best technology lawyers when structuring compliant data governance and cross-border healthcare operations.
iii. Impaired Post-Market Surveillance
- Fragmentation also affects post-market surveillance. For software and connected devices that continue to evolve after launch, the ability to detect patterns, drift, safety issues, and unusual events depends on data flowing back from the field in a timely and usable way.
- If those signals are broken up across multiple jurisdictions or architectures, it becomes harder to see the full picture, harder to identify early warning signs, and harder to improve products safely and efficiently.
Cost and Operational Drag
i. Infrastructure Duplication and Increased Costs
- Beyond product performance, localisation can create substantial operational friction. Companies often have to duplicate infrastructure, maintain separate environments, and manage different compliance pathways in different countries.
- That increases cost, slows deployment, and makes it harder to support a single coherent product strategy across a region or globally. For startups and smaller companies, the burden can be especially heavy because they do not have the scale to build and maintain multiple parallel systems.
ii. Repetitive and Ongoing Compliance Burdens
- Issues arise where exemption pathways are available but must be applied for repeatedly. If a business needs permission for similar cross-border uses again and again, or if approvals are time-limited and tied to narrow categories of use, then compliance becomes a recurring bottleneck rather than a one-time legal review.
- Instead of enabling innovation, the legal process starts to absorb time, management attention, and budget that could otherwise be spent on clinical validation, product safety, and market expansion. This is one reason why many healthcare companies engage healthcare & life sciences lawyers to assess operational and regulatory risks linked to localisation obligations.
iii. Interoperability Requirements
- Mandatory integration with national health exchanges can also be difficult for new entrants. Interoperability is a worthwhile goal, but if integration costs are high, technical requirements are complex, and the process is effectively unavoidable at the outset, smaller companies may struggle to enter the market at all.
- A more balanced approach would be to reward interoperability through reimbursement preferences, accelerated approvals, or procurement scoring, rather than treating immediate integration as a rigid threshold that every company must meet in the same way.
Security and Trust Are Not Automatic
i. Local Storage ≠ Stronger Security
- Another important point is that local storage does not automatically create stronger security or trust. Good security depends on architecture, encryption, access controls, monitoring, incident response, and governance.
- A dataset stored locally can still be vulnerable if the system around it is weak, while a well-designed global system can often be more secure because it benefits from stronger controls, central oversight, and broader threat intelligence.
ii. Trust is Driven by Outcomes, Not Geography
- The same is true for trust. Patients, clinicians, and regulators usually care most about whether a product is safe, transparent, reliable, and accountable. They are less concerned with whether the data sits in one jurisdiction or another than with whether the product is clinically sound, whether it is resilient to cyber threats, and whether the organisation can demonstrate responsible data handling.
- In some cases, localisation can even create more attack surfaces by forcing multiple duplicative environments instead of one highly secured architecture.
Better Regulatory Alternatives
i. Risk-Based Transfer Approvals – A more workable model would be to keep the underlying policy objective while reducing the unintended harm. One option is to adopt risk-based approvals for low-risk transfers, rather than requiring the same level of review for every use case.
ii. Recognition of Intra-Group Safeguards – Another is to recognise safeguards for intra-group transfers so that transfers between controlled affiliates do not need to be reapproved each time they occur under the same governance framework.
iii. Clearer Carve-Outs for Certain Data – Regulators could also create a clearer carve-out for anonymized or de-identified data. If the data is properly stripped of identifiers and subject to strong technical and organisational safeguards, it should be possible to permit compliant cloud processing without repeated exemptions. That would allow companies to innovate, improve safety, and support analytics without undermining legitimate privacy concerns.
iv. Longer-Term Standing Approvals – A further improvement would be longer-term standing approvals for recurring legitimate purposes. If a company can show that a category of transfer is routine, low risk, and governed by strong controls, then a standing approval would be more efficient than repeated short-term permissions. In the same vein, automatic exemptions for multiple data transfers within the same approved category would reduce duplication for both regulators and businesses.
v. Federated Data Governance Models – Regulators could also consider a federated data governance model, where sensitive health data remains within the country, but approved algorithms, analytics queries, or model parameters are permitted to move across borders under supervision. This allows governments to preserve sovereignty over raw datasets while still enabling multinational research, AI improvement, and benchmarking across diverse populations.
vi. Regulatory Sandboxes – Another substantial improvement would be the creation of regulatory sandboxes or innovation corridors for health data use. Under such frameworks, approved healthcare companies, hospitals, or researchers could test controlled cross-border data use cases under regulatory oversight, with strict safeguards, auditability, and defined limits.
vii. SCC-Type Contractual Mechanisms – Finally, where cross-border transfer remains restricted, governments could consider SCC-type mechanisms or similar contractual safeguards tailored to health data. These would not remove oversight, but they would give organisations a clearer route to lawful transfer where the risks are controlled and the purpose is legitimate.
Conclusion
- Health data deserves strong protection, and governments are right to treat it with particular care. However, strict localisation rules can sometimes create unintended consequences as discussed above. A more effective approach is not simply to ask where data is stored, but how it is governed, secured, and used.
- Risk-based transfer mechanisms, strong safeguards, and proportionate regulatory pathways are more likely to protect patients while still allowing healthcare businesses to innovate, scale, and deliver better outcomes across borders.
Authors: Shantanu Mukherjee, Varun Alase
