Interscale Content Hub – As a proverb says, every big profit comes with an equally big risk. So, while we know it is a groundbreaking concept, the potential risks of digital twins is real.
The digital twins interconnected nature and the valuable data they hold make them prime targets. These risks cover cybersecurity, operational challenges and wider social implications.
In this guide, we’ll look at the security, privacy, and operational issues around digital twins.
The aim here is to help you and other professionals get the most out of your digital twin strategies.
Cybersecurity Threats to Digital Twins
Data Theft and Leakage
Digital twins, by their very nature, store and process huge amounts of data from their physical versions.
This data can include sensitive info about how things are running, performance metrics, and even proprietary algorithms.
That’s right, digital twins are massive data powerhouses. As Mark Hearn and Simon Rix point out in “Cybersecurity Considerations for Digital Twin Implementations”, a breach could be catastrophic.
The worst-case scenario could be the loss of intellectual property, a compromised competitive advantage, or even threats to public safety if critical infrastructure is involved.
Mark Hearn and Simon Rix point to a survey of 220 security decision-makers in industrial and manufacturing organisations, carried out by Irdeto and Vanson Bourne.
The upshot is that 79% of industrial and manufacturing businesses have been hit by an IoT-focused cyberattack in the past year. This shows just how high the risk of data theft is in connected systems.
Tampering and Sabotage
In their paper, “On The Use of Risk Measures in Digital Twins to Identify Weaknesses in Structures,” Facundo N. Airaudo and colleagues point out how malicious actors could manipulate a digital twin’s data.
These risks can lead to inaccurate simulations, wrong predictions, or even physical damage to the real-world asset the twin represents.
Let’s say there’s a manufacturing plant where a digital twin has been compromised. That could cause production errors, equipment malfunctions or even safety issues.
The detailed numerical simulations and CVaR optimisation methods show how important it is to make sure that such tampering is prevented in order to maintain structural integrity and operational safety.
Access Control Challenges
Let’s think about how digital twins could be used in healthcare. If someone gets access to them without permission, they could see patient data or change treatment plans.
If you don’t have proper access controls in place, you’re more likely to have unauthorised access, which could lead to data breaches and system manipulation.
In their paper, “Digital Twins: Potentials, Ethical Issues, and Limitations,” Dirk Helbing and colleagues point out that access control is really important to make sure only the right people can get into both the digital and physical systems.
They suggest putting in place multi-factor authentication and strict role-based access controls to help reduce these risks.
Interconnected System Vulnerabilities
It’s rare for digital twins to exist in isolation. They often link up with different systems, like IoT devices, sensors and cloud platforms.
If there’s a breach in one system, it can spread through the network and affect the digital twin and the physical entity it’s connected to.
For example, a digital twin of a smart building, hooked up to lots of sensors and systems, could be at risk if those connections aren’t properly secured.
The interconnectedness of these systems is discussed in “Digital twins in safety analysis, risk assessment and emergency management,” where Enrico ZIo and colleagues point out that if these systems are all connected, there’s a risk of a chain reaction across critical infrastructure systems.
Because everything is connected, we need to have a complete security plan that covers all the potential weak spots in the network.
Privacy Concerns and Regulatory Compliance
Digital twins handle a lot of data, which raises some pretty big privacy concerns.
For this reason, it’s vital to comply with privacy regulations like the Australian Privacy Act and the General Data Protection Regulation (GDPR) to avoid legal issues.
Helbing and Argota Sánchez-Vaquerizo, in “Digital Twins: Potentials, Ethical Issues, and Limitations,” looks at the ethical implications of digital twins, especially when they involve personal data.
They highlight the need to keep personal data safe and to stop it being used inappropriately.
So, companies need to make sure they’re using strong encryption and regularly checking their systems to make sure they’re following these rules.
For reference, kindly read “Digital Twins in the Built Environment: A Cheat Sheet to Efficient Projects.”
Operational Risks
Aside from cybersecurity, digital twins also pose operational risks that can have a big impact on project success and return on investment.
These risks aren’t as obviously harmful as cyberattacks, but they can still be pretty damaging if you don’t manage them properly.
IT issues like outages, natural disasters, or cyberattacks can really mess with digital twin operations, leading to downtime and financial losses.
The 2024 Data Breach Investigations Report by Verizon Business shows that there’s been a 180% increase in attacks exploiting vulnerabilities as the main way to get into systems compared to previous years.
This shows just how much of a challenge it is for digital twins to keep on working without any problems.
On top of IT issues, digital twins, especially those used in financial modelling or containing sensitive intellectual property, are vulnerable to fraud and theft.
The Verizon Business report also points to a worrying trend of employee betrayal, where insiders steal data for personal gain, sometimes in collusion with external actors.
Another consideration is about the accuracy and reliability of a digital twin’s insights depending on the quality of the data it’s based on. As they say, “garbage in, garbage out.”
If the data is wrong or missing bits, it can lead to misleading simulations, flawed predictions and ultimately, poor decision-making.
Enrico Zio and Leonardo Miqueles say it’s crucial to have solid data validation and cleaning processes in place to make sure the digital twin is based on accurate information.
It can also be tricky to get the balance right between model complexity and accuracy.
If a model is too simple, it might not capture the full details of the real-world system. But if it’s too complex, it can become difficult to understand.
Another thing to think about is how the different systems can work together.
If we can make sure all the software platforms can exchange data seamlessly and work together, we can get the most out of the digital twin.
If there’s no integration, you can end up with data silos, communication breakdowns and missed opportunities for optimisation.
On top of that, creating and looking after a digital twin is a big ask in terms of resources. You need to invest a lot in software, hardware and people with the right skills.
For a small startup, the cost of putting a digital twin in place might be too high, whereas a larger company might see it as a good investment.
It’s important to look carefully at the costs and resources needed to make sure that the benefits outweigh the investment.
Finally, working with digital twins requires specific skills in areas like data science, modelling and cybersecurity.
Some organisations might need to train their staff or bring in external experts to get the most out of digital twins.
If a company doesn’t have the right expertise, they might not be able to make the most of a digital twin, which would limit the technology’s benefits.
You might find it helpful to read “Simulation vs Digital Twin: Why They Get in Debate? Why You Need Both?“
What is Digital Twin Risk Management?
Digital twin risk management is a way of identifying, evaluating and dealing with the risks associated with digital twins.
It’s a complete process where we look at all the risks, including cybersecurity, operational and broader societal risks.
Zio and Miqueles suggest a few different ways to manage risks effectively, including doing comprehensive risk assessments, making sure your cybersecurity is rock-solid, checking the quality of your data, validating your models, and developing some new skills.
But, the issue with digital twin risk management is that it requires a lot of resources to operate.
If you get too caught up in digital twins and risk management, you’ll forget about running your main business.
That’s why it’s best to use a provider or consultant.
At Interscale, we provide custom solutions to help organisations get to grips with the challenges of digital twin implementation, from the first risk assessment to ongoing management and optimisation.
Our team of experts can walk you through the whole process, making sure your digital twin projects are secure, reliable, and deliver maximum value.
Give us a shout, and we can arrange a meeting. Our team is here 24/7 to guide you through the process and help you turn challenges into opportunities.
In case you’re looking for a quick overview, kindly visit and read our Interscale BIM Management Support page here.
In Closing
Digital twins are a huge step forward, but there are a few things to look out for.
This means you need to find that sweet spot between pushing the envelope and making sure digital twins are a benefit for your company, not a hassle.
That’s why it’s a great idea to use a team of experts like Interscale has.
Knowing is only half the battle. At the same time, you’ve got to tackle the risks of digital twins without messing up your main business.
