A digital twin is a virtual simulation of a product, process, or system that organisations can use to test for vulnerabilities, optimise performance, and gain insights to make better-informed decisions.
Digital twins (also known as digital twin technology or digital twinning) were first used during the development of NASA’s Apollo space program in the 1960s. Since then, they have helped transform the way organisations in many different industries use data and analytics, optimise systems and processes, and enhance decision-making.
Digital twins work by creating a virtual representation of a physical product, process, or system based on real-time data from a variety of sources including smart sensors, open data sources, 1D and 2D/3D capture scans, generative AI, and Internet of Things (IoT) devices. This “live” connection between the original and the twin allows digital twins to constantly mirror the behavior and performance of the object or system they’re replicating.
Because digital twins are updated continuously with data from the original object or system, they can create a virtual monitoring or testing sandbox that’s accurate, scalable, and always up to date. This enables organisations to monitor, analyse, and run unlimited numbers of tests on a simulation of an object or system without taking up resources or causing any real-world disruptions.
Digital twins and cybersecurity
In the field of cybersecurity, organisations can use digital twins to continuously assess their environment for vulnerabilities, simulate cyberattacks based on the latest threat, and improve the reach and accuracy of their threat detection and response capabilities to proactively predict and prevent cyberattacks before they occur.
Digital twins vs. virtual twins
Both digital twins and virtual twins are virtual simulations that organisations can use to optimise their products or systems without running the risk of any real-world consequences. But there are several important distinctions between them as they serve different purposes.
Virtual twins are used to create a detailed simulation of a new product or process when it’s still at the design stage so organisations can try out different features or characteristics without having to build multiple prototypes. That means virtual twins operate within a closed data system at a fixed point in time, and they typically are no longer useful after the real product or system is launched.
Digital twins, on the other hand, are “living” models that replicate a product, process, or system using dynamic, up-to-date data drawn continuously from the original in real time. This allows them to be used to analyse and optimise the performance of a product, process, or system on an ongoing basis throughout its entire lifecycle, as real-world needs or conditions evolve.
While virtual twins replicate a product or system in its entirety, digital twins strategically simulate only the components essential for monitoring or testing, delivering a precise and effective emulation of the infrastructure. This lets them offer the same benefits but without the time or expense of making a perfect virtual clone.
What are the benefits of digital twin technology?
Digital twins offer numerous benefits and advantages for businesses of all sizes and in many different industries, including:
- Significant cost savings by accelerating product development and manufacturing and improving the efficiency of operations and maintenance throughout the lifecycle of a product or system.
- More streamlined and optimised design, planning, maintenance, operations, and manufacturing processes.
- Real-time visibility into risks, systems, and processes.
- Ability to simulate multiple different environments, circumstances, and scenarios without any downtime, disruption, or need to carry out tests on physical products or systems.
- Reduced environmental impact with less waste than physical testing models.
- Improved product lifecycle management from design and manufacturing to maintenance and operations.
- Smarter, clearer, and more informed decision-making.
What are the three main types of digital twins?
While there are several type of digital twins, there are three main kinds to focus on: product digital twins; process digital twins; and system digital twins.
1. Product digital twins
A product digital twin is a virtual replica of all the physical components, specifications, and characteristics of a single product, such as an automobile engine, an air compressor, or the wings on an airplane.
Product digital twins let organisations test the design, engineering, and performance of a product at any stage in its lifecycle, from the initial designs to its use under real-world conditions. They can also dramatically speed up the development of new products by allowing multiple different iterations to be tested and analysed without needing to build numerous physical prototypes.
2. Process digital twins
A process digital twin is a virtual simulation of a complete process from start to finish, such as a manufacturing process, a logistical or operations network, or a production line in a factory.
Process digital twinning allows organisations to monitor and test how all the different components of even the most complicated processes work together, and then identify ways to improve their performance or efficiency.
3. System digital twins
System digital twins are virtual replicas of entire complex interconnected systems, from supply chains and utility energy grids to infrastructure and transportation networks, customer experience journeys, or storefront retail operations.
System digital twins enable organisations to optimise the design and performance of complete end-to-end systems, including every individual component as well as how all the different pieces fit and work together.
What technologies enable digital twins?
Digital twins are made possible by the seamless integration of a number of different but interrelated advanced technologies. These include:
- IoT networks and advanced sensor technologies to provide the vast amounts of real-time data needed to create accurate simulations.
- Cloud computing and data storage solutions to securely store and process enormous volumes of data, and handle incredibly complex analytics and calculations.
- Machine learning (ML) algorithms to analyse and assess data, and carry out autonomous actions without the need for explicit human instructions, oversight, or intervention.
- Generative AI technologies and large language models (LLMs) to generate the necessary data, accelerate the development of digital twins, and improve the efficiency and accuracy of digital twin simulations.
Examples of applications of digital twin technology
The efficiency and versatility of digital twinning means that the technology has found a wide range of applications in a variety of industries. Some of the more prominent of these are in manufacturing and supply chain management, smart city infrastructure development, and healthcare.
Use of digital twins in manufacturing and supply chain management
Digital twinning lets organisations create virtual simulations of anything from individual products to entire production lines. Features and components can be tested without having to actually build physical versions of each iteration, which helps optimise performance and increase efficiency in every aspect of the manufacturing lifecycle from product design and engineering to supply chains and product lifecycle management (PLM).
Digital twins also allow organisations to remotely monitor their manufacturing facilities and supply chain operations in real time so they can identify any bottlenecks or try out changes using the latest real data.
Digital twin applications in smart cities and infrastructure
Digital twin technology can be leveraged to model, manage, and improve complex urban systems such as urban infrastructure and transportation networks, wastewater systems, power grids, and public services.
By enabling urban planners and other stakeholders to track and optimise traffic flow patterns, emergency response services, energy consumption, and more, digital twins can help cities become smarter, cleaner, and more energy efficient.
Digital twinning in healthcare and patient monitoring
In the healthcare field, digital twins can be used to accelerate the development of new medical devices and technology, deliver more personalised medicine, and enhance the way patients are monitored and treated by healthcare providers.
For example, surgeons can use digital twins to model organs or practice complicated new surgical techniques. Doctors can run a variety of simulations on digital twins of individual patients to aid in diagnosis or determine which course of treatment is likely to have the best outcomes.
Hospitals employ digital twin simulations to optimise their operations, track the spread of diseases, or monitor a patient’s progress in real time. And healthcare manufacturing companies use digital twinning to build better medical equipment or technologies, and bring new innovations to market faster and for less.
Role of digital twinning in cybersecurity
Digital twinning has also begun to play a pivotal and rapidly growing role in how organisations design, analyse, and improve their cybersecurity infrastructure.
Cybersecurity benefits of digital twins
One of the most powerful cybersecurity applications of digital twinning is its ability to continuously simulate and probe an organisation’s defences to identify potential points of vulnerability. When this kind of digital twin simulation is integrated into a cybersecurity platform, the simulation findings can be used to trigger automatic remediations or targeted recommendations for fixes.
Digital twins can also be used in combination with autonomous red teaming, using AI agents to continuously bombard a replica environment with an unlimited number and variety of simulated cyberattacks. (For more on red teaming, including a definition, see the section below titled ‘Digital twins vs. red/blue/purple teams’).
These simulated attacks can dramatically improve the ability of cybersecurity teams to identify, assess, and mitigate security risks without having a direct impact on real-world IT environments or resources.
Digital twins are always updated in real time, simulated attacks can draw on the latest threat intelligence to replicate the newest or most dangerous vectors of attack. They can also be tailored to a specific industry or sector to mimic the biggest emerging threats they’re currently facing.
Lastly, digital twins can also greatly enhance an organisation’s incident response capabilities by allowing cybersecurity teams to practice, train for, and improve the way they respond to real attacks and threats in a virtual environment.
Security risks of digital twins
Because they’re connected to the systems they’re replicating and contain vital data about them, digital twins themselves need cybersecurity protections. It is crucial for digital twins to be based on accurate, high-integrity data, and to protect the privacy of any sensitive, confidential, or proprietary information. It’s also important to prevent digital twins from being hacked—which could allow attackers to manipulate the model into generating deceptive results or even disrupt operations in the physical system it’s being used to mirror.
To protect their real-world operations and their virtual simulations, organisations need to monitor and strictly control who has access to both the original data and any digital twins. They should also make sure their cybersecurity measures are as robust and up-to-date as possible so that cybercriminals never get the chance to corrupt or invade their systems.
Digital twins vs. red/blue/purple teams
Red, blue, and purple teams are all used to simulate a cyberattack and improve an organisation’s defences. Red teams take on the role of an attacker to identify vulnerabilities and assess the effectiveness of an organisation’s cybersecurity controls. Blue teams assume the opposite posture, defending against cyberattacks to improve threat detection and response. Purple teams, meanwhile, help red and blue teams work together effectively, share information and insights, and improve security performance.
Red, blue, and purple teams employ human actors who carry out specific tests or tasks at a single point in time, working in an actual “live” environment. As mentioned, this is a powerful way to identify vulnerabilities and assess the effectiveness, however, as with anything it can be risky working in a live environment.
This is where the advantage of a digital twin becomes even more attractive. Digital twins can carry out millions of simulated attacks within an entirely virtual environment on a continuous, ongoing basis, with the environment also updated continually to reflect changing real-world conditions. This allows organisations to test their cybersecurity defences around the clock to constantly identify any gaps, flag new threats, and remediate any deficiencies in their security posture.
What is the future of digital twinning?
Recent advances in computing power and AI technologies have transformed the speed, capabilities, and effectiveness of digital twins to an extraordinary degree. As the technology continues to evolve, several new trends and innovations have the potential to reshape or even revolutionise the future of digital twinning.
These include the adoption of digital twins by an exponentially growing number of new businesses and industries, the emergence of digital twin information sharing and data marketplaces, and the possibility of integrating digital twins with virtual reality (VR) or augmented reality (AR) to create more fully immersive simulations.
Learn more about the future of digital twining and take a deeper dive into how Trend Micro is using this technology to help organisations create more resilient, adaptive, and future-ready cybersecurity posture.