As industrial control systems face increasingly sophisticated threats, many organizations are turning to digital twins, which are virtual replicas of physical environments, to conduct security testing and threat detection without endangering critical infrastructure. From power grids to manufacturing facilities, these virtual replicas serve as consequence-free laboratories where security teams can identify vulnerabilities, test patches, and simulate attacks before malicious actors discover and exploit weaknesses in production systems.
But what exactly is a Digital Twin? Recently I took part in a conversation with Dale Peterson on LinkedIn about the topic and I think there’s some confusion as to what the term actually means. The reality is the term is extremely broad, but for the sake of this discussion I will focus on cybersecurity use cases within critical infrastructure as I think it’s important to understand the purpose of the digital twin before deciding what it should model.
The concept of digital twins has evolved significantly since its introduction. According to NASA, digital twins are "integrated multiphysics, multiscale, probabilistic simulation of a vehicle or system that uses the best available physical models, sensor updates, fleet history, etc., to mirror the life of its flying twin". This original aerospace-focused definition has broadened considerably over time.
Gartner defines a digital twin as "a digital representation of a real-world entity or system. The implementation of a digital twin is an encapsulated software object or model that mirrors a unique physical object, process, organization, person or other abstraction". This definition emphasizes the representational aspect but doesn't fully capture the dynamic nature of modern twins.
The IEEE and the Digital Twin Consortium offer a more comprehensive definition, describing digital twins as "virtual representation[s] of real-world entities and processes, synchronized at a specified frequency and fidelity". Even more importantly is the sub-bullet under this definition which states “Digital twins are motivated by outcomes, tailored to use cases, powered by integration, built on data, guided by domain knowledge, and implemented in IT/OT systems.”
This is important because it recognizes that the digital twin will have different requirements based on the purpose of the digital twin.
The World Economic Forum (WEF), International Society of Automation (ISA) and others all have very similar definitions. And ISA in particular goes further to define different categories based on the unidirectional or bidirectional flow of data.This includes terms like digital model which has a manual data ingestion and digital shadow to indicate a one-way automated copy and lastly reserves the term digital twin for models that support bidirectional automated data synchronization with the real world.
All of these evolving definitions reflect the maturation of digital twin technology from static models to dynamic, intelligent systems that provide real-time insights and predictive capabilities. What is clear though, is that even more important than the types of data these models contain, is the use of data to drive use-case specific simulations of real-world environments with the appropriate level of context to solve very specific problems.
For industrial control systems cybersecurity, digital twins serve as safe environments for vulnerability assessment, security control testing, and threat simulation without risking operational disruption.
Before diving into specific types of digital twins, it’s helpful to first understand the critical importance of selecting the appropriate level of abstraction for the model. The effectiveness of a digital twin depends entirely on capturing the right elements at the right level of detail (or fidelity) for the specific challenge being addressed.
Digital twins for ICS cybersecurity can be conceptualized across multiple layers of abstraction, each serving distinct analytical purposes.
The abstraction levels incorporated into a digital twin must directly serve its intended security purpose. Consider these examples:
In the paper Deriving a Cost-Effective Digital Twin of an ICS to Facilitate Security Evaluation, the authors position the case for security use case modeling instead of seeking to create highly complex digital replicas ”suggest a method for creating a digital twin that is network-specific, cost-efficient, highly reliable, and security test-oriented”.
In essence, we should begin with the specific security questions that need answering, then work backward to determine which systems and interactions must be modeled and at what fidelity. This prevents the creation of unnecessarily complex models that consume resources without delivering proportional security insights.
The key insight here is that digital twins don't need to model everything, they need to model the right things at the right level of detail. Our thought process here is that selective fidelity is more valuable than comprehensive mediocrity in security modeling. By carefully selecting which abstraction tiers to emphasize based on specific security objectives, organizations can create more effective, focused digital twins that deliver actionable insights while maintaining reasonable resource requirements.
Network digital twins replicate the communication infrastructure within industrial environments, including routers, switches, firewalls, and the traffic patterns between them. Modern networks often include endpoint and identity based characteristics that can also be modeled within these scenarios.
Process digital twins model the industrial processes themselves, capturing the behavior of machinery, production lines, and the physics of industrial operations.
Device digital twins replicate individual industrial devices like PLCs, RTUs, HMIs, and sensors.
These specialized twins focus on industrial protocols like Modbus, DNP3, Profinet, and EtherNet/IP.
Some organizations create twins using identical hardware to their production environments, but in isolated lab settings. While usually prohibitively expensive, this approach provides the highest fidelity for security testing.
More commonly, organizations use virtualization technology to create software-based replicas of their industrial systems. This approach is more cost-effective and scalable, but often lacks the logic and automation required for complex scenario modeling.
Hybrid approaches combine physical devices for critical components with virtualized elements for the broader environment. In many cases, utilizing a hardware-in-the-loop capability to facilitate the modeling of systems that cannot be easily virtualized such as many IED and ICS platforms.
Using emulation technology, these twins can mimic the behavior of industrial devices and networks without requiring identical hardware. These approaches commonly utilize artificial intelligence, graph databases and other similar technologies to emulate the physical environment.
Digital twins represent a paradigm shift in how organizations approach ICS cybersecurity. By providing safe environments for security testing, analysis, and training, they enable proactive defense without risking operational disruption. As industrial systems become increasingly complex and interconnected, digital twins will likely become essential components of mature ICS security programs.
For organizations just beginning their digital twin journey, starting with focused twins of critical network segments or high-risk devices can provide immediate security benefits while building the foundation for more comprehensive implementations in the future. Major OEMs like Siemens, Honeywell, ABB, and AVEVA continue to innovate in this space, offering increasingly sophisticated digital twin solutions that specifically address cybersecurity challenges in industrial environments, while the rapid adoption of AI has created opportunities for new cybersecurity startups to rapidly scale and leverage digital twin technologies for cybersecurity.
Frenos sits at the forefront of this rapidly evolving innovation, by creating the industry’s first OT Security Posture Management (OT-SPM) leveraging an on-premise network digital twin along with application level modeling to emulate specific threat actors and their ability to impact ICS crown jewels. This continuous modeling leverages advanced AI agents to orchestrate both adversarial as well as defensive ICS architect level recommendations to bolster the defensive capabilities for critical infrastructure organizations.