Tech & Innovation The global digital twin market is expected to grow to $110 billion by 2028 at a CAGR of nearly 61 percent, showing immense interest in this sector. While the technology itself isn’t new (the concept of a digital twin has been used for years, mostly for product design and simulation by utilizing a data-driven 3D digital companion), most recently, data and advanced analytics have enabled digital twin technology to do more than simply mirror key processes within physical assets. Now a digital twin strategy uses machine learning to predict outcomes based on historical data and algorithms specific to parts and/or whole systems. This has led to the technology’s rapid proliferation into A digital twin is a virtual model of a process, product, production asset, or service. Sensor-enabled and IoT-connected machines and devices, combined with machine learning and advanced analytics can be used to view the “twins” state in real-time and test how various external factors will affect the real-world model. Digital twin technology is extremely valuable as it enables organizations to not only monitor the health of their systems but also simulate the effects of potential changes, leading to improved decision-making, proactive fault detection, and innovative problem-solving strategies. Digital twin technology is extremely useful in the built world as it helps solve various industry-specific challenges, including cost optimization, safety, thorough planning, predictive modeling, and more. The Köhlbrand Bridge Built in 1974, the Köhlbrand Bridge is Germany’s second-longest road bridge as well as one of its busiest. The cable-stayed bridge, which serves around 38,000 vehicles per day, has played a crucial role in the local economy for half a century. The age of the bridge and the amount of daily traffic it supported meant that continuous real-time monitoring was the best way to identify repairs and minimize disruption to traffic. Without this, issues could go undetected and lead to larger problems that could affect the safety and operation of the bridge. The Hamburg Port Authority created a digital twin of the bridge. Over 500 IoT sensors were connected to a digital sensor in the bridge model, providing real-time monitoring and automatically issuing alerts if problems are detected. Additionally, the digital twin “clone bridge” could be put to the test with various stress simulations, allowing the Hamburg Port Authority to test different solutions and scenarios digitally. For an infrastructure asset as essential as the Köhlbrand Bridge, these insights are vital for ensuring safety and minimizing disruption. Where There is Data There is Vulnerability various markets, including the AEC industry. Digital Twin Definition and Examples As the market grows and this technology is deployed on more and more projects, security concerns must be taken into consideration. Where two or more vectors of data meet, so does a “window” for bad actors in search of said valuable data. To be successful, a digital twin must be intelligent, collaborative, interactive, immersive, and fully contextual within the OEM’s enterprise—which means feeding it live data. Live data, and its possible leakage, is a real security vulnerability.
The possibility of cyber-attacks in construction, in part, is amplified by the amount of confidential and proprietary information digitally stored and shared across projects and their long information technology chains. From planning through construction to the operation of a building, there are enormous amounts of data created by multiple stakeholders. Typically, these types of data connections are easily intercepted and its integrity vulnerable to actual modification by bad actors. Infrastructure, financial accounts, as well as the data of employees, projects, and business sensitive information may be at risk. For example, on January 30, 2020, French construction behemoth Bouygues announced that threat actors were holding 200GB of data ransom. Earlier, Bird Construction, a large Canadian construction company, suffered a similar ransomware attack in December 2019, where the threat actors were demanding $9,000,000 CAD in exchange for decrypting the 60GB of data they were holding ransom. As digital twin technology becomes increasingly integrated with critical systems and infrastructure, the data connection between these physical and digital counterparts creates a considerable opportunity for threat actors and can expose significant risk to organizations and the public. Cybersecurity Considerations Construction, design, and architecture companies implementing digi- tal twin technology need to enact some clear cyber security protocols to keep bad actors out and infrastructure safe. The external sensors used in digital twins are commonly small com- puter devices that have network connectivity (e.g. wireless, ethernet, etc.). However, these IoT devices are ‘lightweight,’ relatively cheap, and such purpose-built devices typically sacrifice security for mobili - ty, such as encryption or monitoring. Based on the environments in which they are deployed and their inher- ent vulnerabilities, there are three key cybersecurity considerations to keep in mind when utilizing digital twins: • Understanding and managing the risks of OT/IoT devices. IoT devices are resource constrained and purpose- built for a specific function such as monitoring temperature, motion, video, etc. The main resource constraint is power, such that they can be easily manufactured to be cheap, reliable and efficient to their specific utility. However, unlike personal computers which have much more computing power and perform a myriad of security functions in the background (e.g. antivirus, encryption, monitoring, etc.), many of these security functions get stripped away for OT/IoT devices so that they can dedicate their computations to their specific function. Of course, this opens up several security vulnerabilities within the devices and, in most cases, “backdoor access” to the broader network on which attackers transverse their access.
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