Beyond Security: Establishing Trustworthiness in Digital Twin Systems

A digital twin is a synchronised, dynamic virtual representation of a physical asset, process, or system, bridging the physical and digital worlds. In practice, digital twins act as a unifying layer that connects visualisation, data, and system behaviour to support insight, experimentation, and informed decision-making. As systems become more connected, data-rich, and autonomous, organisations face growing challenges around integration, trust, and decision-making at scale. Digital twins address these challenges by providing context, continuity, and a mechanism for testing and optimisation over time, while enabling faster delivery from experimentation to operational deployment. Their increasing adoption across industry reflects a shift away from experimentation for its own sake, towards practical deployment of deep technologies that deliver measurable operational, economic, and environmental outcomes. 

For these benefits to be realised at scale, trustworthiness and security are essential. Trustworthiness, encompassing security, privacy, reliability and resilience, builds confidence that complex systems will perform as intended under a variety of conditions. As part of Digital Catapult’s ambition to accelerate the practical application of deep tech, the UK Digital Twin Centre provides the expertise and resources necessary to develop solutions that are innovative, secure, and ready for real-world deployment.  

Trustworthiness: More than just security

In discussions about digital twin technology, the terms ‘trustworthiness’ and ‘security’ are often used interchangeably, yet they are not the same. While both are vital to any functioning system, trustworthiness is a broader concept encompassing security alongside other critical attributes such as safety, privacy, reliability, and resilience.  According to the Industrial Internet Consortium (IIC), “Trustworthiness is the degree of confidence one has that the system performs as expected. Characteristics include safety, security, privacy, reliability, and resilience in the face of environmental disturbances, human errors, system faults, and attacks.” . Security can therefore be understood as a critical building block, with trustworthiness emerging from how these blocks are combined, governed, and assured Understanding this relationship can help organisations assign appropriate weight and investment to each area when developing and deploying digital twins.  

What trust and security mean for digital twins 

Digital twin architectures increasingly integrate advanced capabilities such as artificial intelligence, cognitive reasoning, and immersive interfaces. As these technologies evolve, so too must the approach to trustworthiness and security, ensuring that both extend into these new, intelligent layers of interaction between various types of deep tech.   

As digital twins converge multiple deep technologies, each with distinct trust and security requirements, trustworthiness must be addressed at the level of the underlying components, from AI and decentralised technologies to IoT infrastructure connecting the physical world. 

The primary issues that underpin confidence in how a digital twin system operates concern data and control flows, authentication and authorisation, and governance across the digital twin ecosystem. Addressing these issues is not simply a technical exercise but a strategic enabler of adoption. Organisations that cannot demonstrate secure and trustworthy digital twin operations risk delaying or derailing their transformation initiatives. Building trust must start early in the design process, with clear governance, transparency, and communication around how data is collected, managed, and protected. 

Addressing security in a connected digital twin world 

In the digital twin ecosystem, security should be treated as a design principle, a foundational layer that underpins every aspect of system operation. Unlike traditional systems, digital twins continuously exchange data between physical and virtual environments, exposing multiple points of potential vulnerability. To ensure resilience and continuity, security measures must therefore be end-to-end, spanning infrastructure, data, applications, and supply chains. A secure digital twin system should: 

• Protect both physical and virtual infrastructure, including cloud and edge environments. This requires securing not only cloud platforms but also on-premise and edge devices that interact with the physical world. In practice, this includes hardening operating systems, segmenting networks between IT and OT environments, enforcing secure boot and firmware integrity on edge devices, and continuously monitoring both physical access and digital activity across the infrastructure. 

• Employ zero-trust architectures, verifying every interaction within the ecosystem, even post-authentication. Zero-trust means assuming no implicit trust between components, users, or services. Each interaction within the digital twin ecosystem should be explicitly authenticated, authorised, and logged, whether it originates from a human user, a device, or a software service. This typically involves identity-based access control, short-lived credentials, service-to-service authentication, and continuous validation rather than one-time perimeter checks. 

• Safeguard data assets, whether business-critical or personal, through encryption, monitoring, and strict access control. Protecting data in a digital twin requires securing it throughout its lifecycle: at rest, in transit, and in use. Practical measures include encrypting sensor data and model outputs, applying role-based or attribute-based access controls, monitoring for anomalous access patterns, and enforcing data governance policies that define who can access, modify, or export data and for what purpose. 

• Extend security to the entire supply chain, ensuring that third-party data sources, models, or AI components introduced into the digital twin meet the same rigorous standards. Digital twins often depend on third-party data sources, software components, and AI models. Extending security across the supply chain means validating the provenance and integrity of these components, assessing suppliers against defined security standards, and ensuring that externally sourced models or services are subject to the same controls, testing, and governance as internally developed components. 

Only by embedding security across the entire lifecycle can organisations ensure that their digital twins remain dependable, compliant, and resistant to emerging threats. 

Assurance: Making Digital Twins Dependable 

Even with robust security controls in place, true trust in digital twins depends on assurance, the degree to which users can rely on the system to perform accurately and transparently. Assurance provides the confidence that a digital twin’s data, behaviour, and outputs reflect the real world with reliability, and that its decisions can be explained, verified, and audited. 

To be considered dependable, a digital twin should: 

• Guarantee that all data used and generated is accurate, sufficient, and timely, minimising latency and error. 

• Provide explainable and traceable outputs, allowing users to understand how insights or actions are derived. 

• Demonstrate that the enabling technologies, including data services, intelligence, cyber-physical systems, integration and interoperability, user experience (UX) and immersive, are production-ready and reliable, not experimental or immature. 

This ensures that digital twins can be confidently integrated into business operations and used as trusted tools for decision-making, rather than as isolated proof-of-concepts.

Looking Ahead: Investing in trust and security 

As digital twins continue to shape the future of industry and infrastructure, investing in trustworthiness and security is no longer optional, it’s essential. These two factors determine not only whether a digital twin functions effectively, but whether it will be accepted, relied upon, and scaled within an organisation. Building trust is as much about perception as performance: users must believe the system is safe, resilient, and accurate if it is to deliver on its promise. 

Funded by Belfast Region City Deal and Innovate UK, with industry co-investment from Thales UK, Spirit AeroSystems, and Artemis Technologies, the UK Digital Twin Centre is making digital twins more accessible and meaningful by delivering pioneering use cases that demonstrate their transformative potential across the maritime, aerospace, and defence sectors.