Unlocking insight when data cannot move

Organisations are under pressure to generate more value from sensitive data while operating under increasingly strict security, privacy and sovereignty requirements. In many cases, the data needed for AI and advanced analytics already exists, but it cannot realistically be centralised, shared or exposed.

Sensitive operational data, regulated personal information, classified datasets and sovereign data assets are growing in volume and importance just as organisations face tighter controls on how that data can be accessed, shared and processed. As a result, significant volumes of high‑value data remain underused. Many AI and advanced analytics initiatives stall not because models are inadequate, but because organisations cannot safely access or use the right data. In many cases, the data required to drive meaningful AI outcomes already exists, but governance, sovereignty and security constraints prevent it from being effectively utilised. This challenge is becoming one of the defining barriers to data‑driven transformation.

The limits of data sharing in a regulated world

Traditional data strategies have relied on sharing and centralising data – moving datasets into common platforms where they can be analysed, combined and enriched. In highly regulated environments, however, this model is increasingly unsustainable.

Health organisations must safeguard patient confidentiality. Defence and national security bodies must protect classified information and operational advantage. Enterprises operating across borders must navigate sovereignty, jurisdictional control and regulatory fragmentation. In these contexts, transferring or exposing data, even to trusted partners, introduces unacceptable risk.

The result is growing operational friction:

  • Valuable datasets remain siloed
  • Collaboration across organisational or national boundaries is restricted
  • AI initiatives struggle to scale beyond narrow, controlled use cases
  • Organisational transformation becomes delayed, increasing cost and slowing time-to-value

What is required is not more data movement, but a fundamentally different model for secure collaboration and analytics.

A shift from data sharing to secure collaboration

To unlock insight in constrained environments, organisations need to move from sharing data to computing across data without moving it.

Trusted computation enables analysis and collaboration across distributed datasets that organisations do not own or directly control, without relinquishing privacy, security or sovereignty. Rather than transferring raw data, analytics and computation are performed in ways that protect the underlying information. Insight can be shared, while control remains firmly with the data owner.

This approach is enabled by a class of capabilities often referred to as Privacy Enhancing Technologies. These allow organisations to collaborate, analyse and generate insight across sensitive or regulated data while preserving confidentiality and control.

As regulatory pressure intensifies and cross‑organisational collaboration becomes essential, trusted computation is becoming a practical requirement for delivering advanced analytics and AI at scale.

Turning capability into operational reality

While the technology foundations for trusted computation exist, the challenge for most organisations lies in operationalising them.

Embedding privacy‑preserving approaches into real environments means aligning with existing data architectures, security models, governance frameworks and regulatory obligations. It requires careful integration with enterprise platforms, AI pipelines and operating models. Without this, advanced capabilities remain isolated experiments rather than drivers of sustained value.

This is where specialist privacy‑preserving technology and expertise must be combined with enterprise‑scale delivery experience.

Through our collaboration with Duality, Capgemini is addressing this challenge for organisations operating in highly regulated and security‑sensitive contexts. Duality brings deep expertise in privacy enhancing technologies designed to protect restricted datasets while enabling meaningful secure analytics and data collaboration. Capgemini’s role is to deliver these capabilities into real operating environments, integrating them with existing data and AI architectures, aligning to governance and compliance requirements, and ensuring they can be adopted and scaled with confidence.

The focus is not on the technology itself, but on enabling secure, scalable and operationally viable outcomes.

“The traditional model of moving data into a central environment for analysis is becoming increasingly difficult in regulated industries”, said Alon Kaurman, CEO and Co-founder of Duality. “Healthcare organisations, financial institutions and public sector teams are all dealing with growing restrictions around how sensitive data can be accessed and used. Together with Capgemini, we are helping organisations analyse and collaborate across sensitive data while keeping control with the data owner.”

Privacy as an enabler, not a constraint

When implemented effectively, privacy enhancing technologies change the role of privacy and security in data driven innovation. .

Instead of acting as a barrier, privacy becomes the enabler of collaboration. Organisations can generate insight across boundaries that were previously closed. Time to insight is reduced. Risk is managed rather than shifted. Most importantly, innovation becomes sustainable rather than periodic.

This is particularly relevant in sectors such as health and defence, where collaboration across organisational, national or ecosystem boundaries is increasingly essential, but where trust, assurance and sovereignty cannot be compromised.

The future of data‑driven transformation in these environments will not be defined by who owns the most data, but by who can derive insight responsibly, securely and at scale.

Trusted computation is a critical step in that direction.

 “Aggregating data can be a barrier to analytical ambition and AI-driven insight, introducing operational complexity and an expanded risk profile. Capgemini and Duality Technologies enable clients leverage trusted collaboration and accelerated compliance, while fully preserving privacy and unlocking value. By combining advanced privacy-enhancing capabilities with global enterprise transformation expertise, this strategic collaboration delivers confident, compliant and collaborative innovation.”

Andy Lea,
Vice President, Capgemini Invent

Book a confidential discovery call or explore a trusted-computation pilot with Capgemini and Duality to see how your organisation can securely analyse and collaborate across sensitive data without exposing it.

Contact: amy.robinson@capgemini.com