Cyber ranges allow the testing and integration of many kinds of technologies. Digital twins apply that idea to a specific target: a virtual replica of a real network or system, kept in step with the original so teams can test its behaviour, validate security controls, and investigate weaknesses under controlled conditions.
It is easy to overspend on many technologies, and cybersecurity is no exception. "Throwing money at the problem" is rarely the best solution. Cyber range technology, and the digital twins approach in particular, allows organisations to test their cybersecurity arrangements on a small scale yet robustly, to identify potential vulnerabilities and shortcomings without necessarily duplicating a full-scale IT system merely for testing purposes.
What is a digital twin in cybersecurity?
A digital twin, traditionally defined, is a virtual representation of a physical object or system that is created by using data and simulation models to replicate the real-world characteristics and behaviour of the object. Digital twins are used to monitor, analyse, and optimise the performance of the physical counterpart in real time.
For instance, IBM exemplifies the use case of a digital twin as follows: "The object being studied — for example, a wind turbine — is outfitted with various sensors related to vital areas of functionality. These sensors produce data about different aspects of the physical object's performance, such as energy output, temperature, weather conditions and more. This data is then relayed to a processing system and applied to the digital copy."
The turbine example also points to what changed. A traditional twin mirrors a physical object and relies on physical sensors to feed it real-world data. When the subject is itself digital, however, such as a network, an application, or an entire IT estate, there is no hardware to instrument: the system already generates its own data, from logs to configurations to live traffic. That makes an all-digital system an unusually natural candidate for twinning, and it explains why the idea moved so readily from the factory floor into cybersecurity.
Even in an all-digital context, the concept of "twin" is still useful. A cyber range can be used as a "digital twin" to simulate and test the behaviour of a network or cyber system in a controlled environment, without the risk of damaging the real system. To put it simply, a digital twin in cybersecurity is a virtual replica of a specific network or IT system that stays in sync with the original. Teams use it to simulate attacks, test defences, and evaluate changes without putting the live system at risk.
So what actually makes a cyber range a digital twin, rather than a sandbox, a staging environment, or a generic testbed? Two things: coupling and fidelity. A true digital twin is coupled to a specific production system, syncing its topology, configuration, and state continuously or on a schedule, so the model tracks the real system as it changes. A sandbox or generic testbed is a static, disposable snapshot, realistic enough to run a test but not tied to any one network. Fidelity then varies by scope. Some domains, notably space and safety-critical OT, justify a full, live-synced replica; for most enterprise testing a representative snapshot is not a compromise but the sensible choice, because it is safe to break, cheaper to run, and carries no risk of the test rig quietly diverging from the system it represents.
When production is off-limits
Using a digital twin setup allows the testing of a cyber system in a number of ways: running defensive operations, simulating offensive attacks, testing different AI-related scenarios, and many more. Taking a granular approach to testing their systems helps the organisation make the most of their investment in whatever technology they are looking to deploy – and defend against breaches and attacks.
In each of these domains, testing on the live system is either impossible or unacceptable, and the reasons differ.
Smart cities
A smart city fuses conventional IT with the OT, ICS, and SCADA layers that run transport signaling, water, power, and public services. These systems share networks yet cannot be paused for testing: an intersection, a substation, or a pump station is a live dependency for thousands of residents. A twin lets teams probe the seams where IT meets OT and rehearse cascading failures without touching production. CybExer built exactly this with the University of Tartu, using a digital twin of Tartu's city centre to test smart-mobility resilience, and offers it as a Smart City Cyber Range.
Defence networks
Military networks carry classified traffic and are frequently air-gapped, so the real systems are off-limits for experimentation and cannot be reached from the open internet. A faithful, offline replica in a defence cyber range lets red and blue teams rehearse intrusions, resilience, and command-and-control workflows at the right classification level, without exposing live infrastructure or breaching the air gap the network depends on.
Space systems
Once a spacecraft launches there is no physical access and no second attempt, so behaviour has to be validated exhaustively beforehand. RF links, propagation delay, and constrained onboard compute all shape how a system responds, and a partial sample will not capture them, which is why space usually warrants a full twin. CybExer has done this in practice, from a digital twin of a small satellite with the European Space Agency to its role as an ESA key technology partner building a dedicated Space Cyber Range.
Industrial control systems
Industry 4.0 plants run legacy PLCs and controllers built for uptime and safety, not for security testing, and a live line cannot be taken down or probed without risking product, equipment, or people. A twin lets teams ask the question that actually matters in OT: not just whether an attack was blocked, but whether essential processes keep running, and recover safely, while it is underway. This is the preproduction OT testing that a digital twin makes possible without endangering the live plant.
What to replicate and what to sample
For instance, to obtain a sufficient amount of testing data and experience, one does not necessarily need to build a full digital twin such as a 20,000-workstation network; merely 20 sample workstations might do just as well. (In other cases though, especially space applications, it is usually a good idea to build a full digital twin for testing purposes.)
The catch is that a representative twin is a sampling problem, not just a shrinking one. Include one of every host archetype that shapes an attack path: domain controllers and identity providers, DNS and DHCP, the key application and file servers, jump and admin hosts, and OT gateways and historians. Bulk endpoints can collapse to a few golden images per build. Make the fidelity deliberately uneven: model domain controllers, identity, and security tooling in full, because that is where privilege and detection live, and sample the noisy majority. Preserve every trust boundary, VLAN, and segmentation rule so lateral movement stays honest even at reduced host counts. But the trade-offs are real: a smaller environment will not reproduce every effect of scale. It may simplify lateral movement, change detection thresholds, and miss the traffic and workload patterns created by thousands of endpoints. Validate those against production telemetry, or reserve them for a full twin.
From model to validation
Process-wise, a digital twin of a network or cyber system can be established and tested through the use of a cyber range as follows:
- Model the network: The first step is to create a model of the network or cyber system that is being tested. This model should include all the components and their interconnections, as well as the rules and protocols that govern the behaviour of the system.
- Configure the cyber range: The cyber range should be configured to replicate the physical and logical environment of the network or cyber system being modelled. This includes installing the necessary software, hardware, and networking components, and configuring the environment to match the model.
- Introduce scenarios: Once the cyber range has been configured, scenarios can be introduced to simulate real-world conditions and events. These scenarios could include cyberattacks, data breaches, and other security incidents, as well as routine activities such as software updates and network maintenance.
- Monitor and analyse: The behaviour of the network or cyber system can be monitored and analysed in real time as the scenarios are played out in the cyber range. This data can be used to identify security vulnerabilities, improve incident response procedures, and optimise the performance of the network or cyber system.
- Validate and refine the model: The results of the simulation can be used to validate and refine the model of the network or cyber system. This can involve updating the model to reflect new insights, modifying scenarios to better reflect real-world conditions, or making changes to the environment to optimise performance.
Where the model breaks down
A twin has real limits. The most immediate is drift: a production network changes constantly (patches, new hosts, re-segmentation) while the replica does not, so a twin that is accurate on day one quietly diverges. Without scheduled re-sync and maintenance, the twin comes to represent a network that no longer exists. That upkeep is also why not every system deserves a twin: for small, stable, or simple environments, the modelling and maintenance cost can outweigh the insight it yields. Fidelity has hard edges, too. Precise timing, hardware-specific behaviour, and third-party cloud or SaaS dependencies are difficult to reproduce faithfully. Finally, a high-fidelity twin is a working map of the network it mirrors, and it must be access-controlled and protected accordingly.
What EU rules mean for resilience testing
A wave of EU regulation is turning resilience testing from good practice into a legal duty for whole sectors. NIS2 (Directive (EU) 2022/2555, with national transposition due by 17 October 2024) requires essential and important entities, utilities among them, to adopt cybersecurity risk-management measures and assess their effectiveness; the European Commission estimates it covers around 160,000 organisations. DORA (Regulation (EU) 2022/2554, in force from 17 January 2025) goes further for the financial sector, requiring the entities designated as significant to run threat-led penetration testing on a roughly three-year cycle. The Cyber Resilience Act (Regulation (EU) 2024/2847) adds a different lever from 2027, obliging makers of products with digital elements to build in security and handle vulnerabilities before they ship. A cyber-range digital twin is a controlled place to rehearse against all three without touching production.
AI as system, defender, and adversary
AI can be the system under test, part of the defence, or the force driving the exercise. A twin can contain an AI system, replicating an AI-enabled application so its model and integrations can be attacked and observed. It can test an AI-enabled defence, measuring how a machine-learning detector behaves against replayed traffic rather than against a benchmark. Or it can use AI to drive the exercise, with AI-assisted adversary emulation generating novel attack paths and adapting payloads.
Each of these raises the fidelity bar. Testing an AI component faithfully means reproducing not just topology but the model's behaviour, its data, its guardrails, and its nondeterminism, which defeats simple repeatable test cases. It also widens the attack surface a twin has to cover: prompt injection and excessive agency, both named in the OWASP Top 10 for LLM Applications, alongside model and data drift and unsafe tool use by autonomous agents, the adversarial-ML techniques catalogued in MITRE ATLAS. An isolated, production-representative twin in an AI cyber range is the safe place to reproduce those conditions and prove an AI system holds up under real conditions before it is trusted in production.
Putting this into practice
CybExer has experience from integrating all the best known commercial cybersecurity vendors' products into our cyber ranges, allowing the stakeholder teams to maximise their usefulness. We have also integrated some of the most complex operational technology systems for preproduction testing. In practice that spans the tools security teams already run, from SIEM and EDR to firewalls, identity, and OT and ICS monitoring. If you are weighing a digital-twin or cyber-range approach for your own environment, talk to a cyber range expert.
