Digital twins and cybersecurity: the new asset that also needs protecting

Industry 4.0 no longer distinguishes between the physical plant and the digital plant

Industry 4.0 has stopped being a promise for the future and has become the way factories, power grids, water treatment plants, and railway systems operate today. At the center of that digital transformation sits a technology that has moved from engineering concept to business asset: the digital twin.

A digital twin is no longer just a virtual mock-up used to simulate a machine. It’s a living replica, fed in real time with data from sensors, controllers, and corporate systems, that makes it possible to predict failures, optimize processes, and make decisions without touching the physical asset. And that’s exactly where the security problem lies: a digital twin isn’t an isolated model, it’s a permanent bridge between the IT world and the OT (Operational Technology) world.

When that bridge is built without access control, without identity management, and without visibility over who — or which machine — is entering it, the digital twin stops being a competitive advantage and becomes the most sophisticated entry point an attacker could wish for.

This article looks at what a digital twin really is, how it changes the attack surface, what its main risks are, and what role PAM, Zero Trust, and European and U.S. regulatory frameworks play in protecting it.

What a digital twin really is (beyond the 3D model)

Reducing a digital twin to a 3D representation of a machine falls well short of the reality. In practice, it’s a complete information system made up of three layers that interact continuously:

• Physical layer: the real asset — a turbine, a production line, a substation — instrumented with sensors and actuators.
• Data layer: the constant stream of telemetry feeding the virtual model, along with historical, maintenance, and operational context data.
• Model layer: the digital representation — mathematical, physics-based, or AI-driven — that simulates the asset’s behavior and allows scenarios to be tested without risk to the real operation.

What sets a digital twin apart from a simple simulation is that continuous, bidirectional synchronization with the physical asset. In many advanced implementations, the model can even send instructions back to the control systems. That capacity to act — not just observe — is what multiplies the potential impact of any security incident.

Technical architecture of a digital twin: where the risk actually lies

A typical industrial Digital Twin architecture integrates, at minimum, these layers:

1. Field and Industrial IoT (IIoT): sensors and actuators that capture physical variables and send them to the layers above.
2. Controllers (PLCs) and SCADA: the real-time control level, traditionally isolated, that today is increasingly connected to the digital twin.
3. MES: coordinates production execution and provides manufacturing context and traceability.
4. ERP and corporate systems: business data that enriches the model and connects it to strategic decision-making.
5. APIs and integrations: the connective tissue that lets every layer — and often external vendors — exchange data with the digital twin.
6. AI and analytics engines: the prediction and predictive-maintenance algorithms that consume the model’s data and, in many cases, generate automatic actions.
7. Cloud infrastructure: the typical setup is a hybrid architecture where the model resides, fully or partially, in the cloud.

Each layer has its own identity model, its own protocols, and often its own security standards. The digital twin doesn’t add one layer of risk: it adds seven.

How the attack surface changes

Before the digital twin, OT environments enjoyed protection based on physical isolation. The digital twin breaks that isolation by design, because it needs a constant flow of data between the plant, the ERP, the cloud, and external vendors. This has three direct consequences:

• Accelerated IT-OT convergence: every integration is a new potential vector toward assets that were previously inaccessible from the outside.
• Multiplication of integration points: APIs, IoT connectors, and analytics dashboards become targets just as attractive as the industrial controllers themselves.
• Perimeter extended to third parties: machinery manufacturers and integrators frequently end up with remote, privileged access to the client’s infrastructure.

The result is a perimeter that can no longer be drawn as a line around the plant, but as a mesh of identities, APIs, and data flows that crosses organizations and clouds.

The five major security risks of the digital twin

1. Data manipulation. Subtly altering the data feeding the model can lead to incorrect predictions or wrong control instructions being sent back to the physical process, all while the model appears to keep functioning normally.

2. Theft of intellectual property. A digital twin condenses years of engineering knowledge: process parameters, optimal configurations, and, in many cases, the product’s design. Compromising it can be more profitable for an attacker than spying on the physical plant, because that intellectual property is already consolidated and ready to exfiltrate.

3. Supply chain attacks. Integrators, IIoT component manufacturers, and cloud providers all take part in the digital twin’s lifecycle, each with their own access. A compromise at any of those links can propagate all the way to the physical process it represents.

4. API compromise. APIs connect the digital twin to IoT, SCADA, MES, ERP, and the cloud. A poorly authenticated API, with excessive permissions or inadequate encryption, is, in practice, an open door to industrial control systems.

5. Identity and credential abuse. The risk that cuts across the other four. Shared credentials, passwords embedded in IoT devices, service accounts with permanent privileges, and vendor access that’s never revoked are the most exploited vector in connected industrial environments.

Human identities vs. machine identities

A security program that only accounts for human identities leaves out most of the actors interacting with the system. Managing machine identities — with automatic rotation and least privilege applied to devices and services too — is today just as critical as managing human users, if not more so.

Human identities: process engineers, maintenance staff, external integrators, and data analysts. Managing them follows classic IAM principles: strong authentication, periodic access review, and full session traceability.

Machine identities: IoT sensors, PLC controllers, MES services, API connectors, cloud workloads, and AI agents that consume and generate data autonomously. They grow at a far faster rate than human identities in any Digital Twin, and yet receive far fewer safeguards: static credentials, certificates that never rotate, service accounts with broad privileges granted “for operational convenience.”

The role of PAM, Zero Trust, and least privilege

Faced with such a distributed architecture, traditional perimeter security loses its meaning. The approach that best fits the reality of the digital twin is Zero Trust: no access is granted by default, and every request is explicitly and continuously verified and authorized, regardless of where it comes from.

On that foundation, Privileged Access Management (PAM) plays a central role:

• Granular control of privileged access to every layer of the architecture, preventing a single compromised credential from opening a path to the entire system.
• Recording and auditing of maintenance or remote support sessions, especially from external vendors.
• Machine credential lifecycle management, with automatic rotation and the elimination of embedded or shared passwords.
• Effective enforcement of least privilege, granting each identity exactly the permissions it needs, for exactly as long as it needs them, and no more.

Combining PAM, Zero Trust, and least privilege doesn’t eliminate the digital twin’s attack surface, but it ensures every point on that surface is governed, auditable, and can be contained if compromised.

Relationship with NIS2, IEC 62443, NIST SP 800-82, and the Cyber Resilience Act

• NIS2 (Directive (EU) 2022/2555): establishes risk management and incident notification measures for essential and important entities in sectors such as energy, transport, or industry. A digital twin that connects control systems to the cloud and to vendors falls squarely within its supply chain and access control obligations.
• IEC 62443: the international reference standard for the cybersecurity of industrial automation and control systems. Its zone and conduit segmentation requirements, and its security levels, apply directly to the boundary between the digital twin and SCADA/PLC systems.
• NIST SP 800-82 (Rev. 3): NIST’s reference guide for operational technology security, with architecture recommendations and controls relevant to hybrid IT-OT-cloud environments like those of the Digital Twin.
• Cyber Resilience Act (Regulation (EU) 2024/2847): requires security by design and by default throughout the lifecycle of products with digital elements, including the IIoT devices and software that make up a digital twin.

None of these frameworks explicitly mentions “digital twins,” but all four converge on the same requirements its architecture demands: access control, identity management, segmentation, and security by design.

Best practices for protecting a digital twin

• Inventory every identity — human and machine — that interacts with the digital twin at each layer.
• Apply least privilege and segmentation between the model layer, the OT layer, and the cloud layer.
• Centralize privileged access management for vendors and integrators, with recorded sessions and single-use credentials.
• Eliminate static and embedded credentials in IoT devices and controllers, replacing them with automatic rotation.
• Establish specific controls over APIs: strong authentication, rate limiting, encryption, and monitoring for anomalous use.
• Validate the integrity of the data feeding the model, to detect manipulation before it translates into incorrect decisions.
• Incorporate the digital twin and its vendor chain into the risk assessment required under NIS2.

Conclusion

The digital twin is no longer an innovation experiment that can be left outside the security strategy: it’s an operational replica of the business, connected in real time to the plant, the ERP, and the cloud, and therefore just as critical as any of the systems it represents. Treating it as a purely technological project, disconnected from cybersecurity, is the most common starting mistake — and also the most costly.

A Digital Twin’s architecture allows no shortcuts: every layer — IoT, PLC, SCADA, MES, ERP, APIs, AI, cloud — needs identity governance, segmentation, and traceability from day one, not as a layer bolted on afterward. Endurance answers that challenge, unifying PAM, IAM, VDI, and DLP into a single product capable of controlling both human access and the machine identities that multiply the attack surface of these environments.

Organizations that build this discipline in before scaling their digital twin project won’t just avoid becoming the weak link in their supply chain: they’ll turn access control into part of the digital asset’s own value.

Bibliography

• European Union. Directive (EU) 2022/2555 (NIS2 Directive). EUR-Lex. https://eur-lex.europa.eu/eli/dir/2022/2555/oj
• European Union. Regulation (EU) 2024/2847 (Cyber Resilience Act). EUR-Lex. https://eur-lex.europa.eu/eli/reg/2024/2847/oj/eng
• European Commission. Cyber Resilience Act — Shaping Europe’s Digital Future. https://digital-strategy.ec.europa.eu/en/policies/cyber-resilience-act
• NIST. Special Publication 800-82 Revision 3, Guide to Operational Technology (OT) Security. 2023. https://csrc.nist.gov/pubs/sp/800/82/r3/final
• International Electrotechnical Commission. IEC 62443 series — Security for industrial automation and control systems. https://www.iec.ch/blog/understanding-iec-62443
• ENISA. NIS2 Technical Implementation Guidance. https://www.enisa.europa.eu