Applications of Digital Twin Technology and Security Implications

Joonkoo Yoo

jkyoo88@sejong.org

Senior Research Fellow

Sejong Institute

1. Issue

The advancement of emerging technologies is exerting significant influence not only on the economy and industry but also on the security and military domains, drawing increased attention to the direction of technological innovation, areas of application, and future ripple effects. In this regard, assessments that the outcome of U.S. China strategic competition hinges on technological supremacy reflect the reality that technological innovation is directly driving economic development and strengthening national security. In addition, the temporal gap between security and military applications and economic and industrial applications of emerging technologies, often described as spin-off versus spin-on, is narrowing. More recently, technological innovation is being pursued with consideration from the pre-design stage of the technology life cycle for simultaneous security, military, economic, and industrial applications, or spin-up. In other words, advances in AI, which leads emerging technologies, are reshaping paradigms in both economic and security domains, and technologically advanced countries, including the United States and China, are promoting AI innovation policies at the national strategic level. Although major technology leading countries are advancing AI strategies and policies represented by the concept of “sovereign AI,” uncertainty persists regarding the future impact and direction of AI innovation. In particular, concerns have been raised that an “AI winter“ similar to that of the 1970s and 1980s could reemerge, and there remains debate over whether large language models(LLM) can overcome the so called ”singularity” threshold. While the United States holds a dominant position in computing technologies within the AI field, China is adopting a strategy focused more on applied and commercialized AI technologies, and AI development is increasingly shifting toward application areas such as “physical AI.“

At the same time, the phenomena of nexus and convergence among emerging technologies have recently attracted attention. A primary driver of such nexus and convergence is the far reaching impact of AI, which possesses broad general purpose and instrumental characteristics. In earlier phases of technological convergence, the rapid advancement of ICT technologies highlighted the use of IoT(Internet of Things) and related ICT systems in the operation of critical infrastructure. More recently, however, the integration of AI into multiple technological sectors and domains has become particularly pronounced. In this context, digital twin technology, which creates three dimensional virtual representations of the physical world and enables customized solutions to a variety of issues through digital simulation, has emerged as a notable future AI enabling technology. Digital twin technology is characterized by the nexus and convergence of multiple technologies, including data systems, cloud computing, IoT and sensors, blockchain, AR(augmented reality) and VR(virtual reality), and AI. It is applicable across a wide range of fields, including manufacturing processes, smart cities, testing and monitoring, healthcare, cybersecurity, and military operations.

However, the nexus and convergence of emerging technologies, as exemplified by digital twin technology, also complicate efforts to respond to threats associated with emerging technologies. In the field of emerging technology security, risks may be distinguished between those inherent in the intrinsic characteristics of the technologies themselves and those arising from the malicious use of such technologies. In cases where diverse technologies converge, as with digital twin systems, the corresponding response frameworks are necessarily more complex. Accordingly, the assessment of technological risks stemming from nexus and convergence must be designed and validated through multilayered analytical approaches. As major countries are designating new categories of emerging technologies that reflect these nexus and convergence characteristics and are formulating corresponding innovation strategies and road maps, South Korea has also reached a juncture at which its existing core technology innovation strategies warrant reassessment.

2. Overview and Characteristics of Digital Twin Technology

A. Significance and Importance of Digital Twin Technology

A digital twin is a technological system that serves as a digital counterpart to a real world physical product, system, or process, namely a physical twin, for purposes such as simulation, integration, testing, monitoring, and maintenance. It may be defined as “a set of adaptive models that emulate the behavior of a physical system within a virtual system capable of receiving real-time data and updating itself over its life cycle,” thereby enabling the provision of real-time actions. The earliest substantive application dates to 2010, when the National Aeronautics and Space Administration (NASA) sought to improve simulations of the physical models of spacecraft. During the 2010s and 2020s, the manufacturing sector expanded the concept of digital product definition into the broader digital twin framework encompassing the entire manufacturing process. As a result, digital twins have come to be applied not only to production itself but also to all business processes contributing to production. The digital twin concept consists of three distinct components: △ physical object/process and its physical environment, △ digital representation of the physical object or process, and △ communication channel between the physical representation and the virtual representation. AI, blockchain, big data, the IoT, and cloud computing function as integrated technological components within digital twin systems. The rapid advancement of ICT-based technologies has played a critical role in the emergence and development of digital twin technology. Unlike Augmented Reality(AR), which visualizes real-world information by overlaying additional data onto reality, and Virtual Reality(VR), which generates entirely artificial environments, digital twin technology is not merely data visualization. Rather, it constitutes data-based replication, with real-time connectivity and bidirectional interaction as its core features.

The principal advantages of digital twins include: △ real-time decision making, △ automation and enhanced efficiency, △ cost reduction and predictive maintenance. By collecting and analyzing real-world data in real time, digital twins provide information necessary for timely decision making. This capability enables rapid responses in operational environments and supports decisions based on up-to-date information, thereby contributing to the maintenance and strengthening of competitiveness. The growing prominence of digital twin technology is related to divergent assessments of advances in large-scale generative AI and to the broader trajectory of AI application and commercialization. Although the current period is often characterized as an “AI spring” or “AI boom,” concerns have been raised that research outcomes in areas such as large generative language models, autonomous driving, and artificial general intelligence may fall short of expectations, recalling the two previous “AI Winters” of the 1970s and 1980s. For example, the Apple research team has argued that generative AI models, including those developed by OpenAI, DeepSeek, and Claude Sonet Thinking, are fundamentally forms of pattern-matching thereby emphasizing the limitations of existing AI reasoning models. Although this position has faced criticism, especially the argument that pattern matching remains useful and is likely to continue for at least the next decade, the growing emphasis on commercialization has shifted attention toward physical AI, including AI enabled robotics and digital twins, even if an immediate AI winter does not materialize.

B. Scope of Application of Digital Twin Technology

As the nexus and convergence among emerging technologies such as cyber, space, and AI accelerate, digital twins represent a leading example of a convergent technology, and their scope of application demonstrates significant scalability. Owing to their convergent and integrative characteristics, digital twin technologies may be expanded and applied across tool, domain, and sector level technological areas. Digital twins also constitute a representative dual use technology with applicability in both commercial and military contexts. The technical modification required for adaptation between commercial and military applications, as well as the temporal gap between the two sectors, is relatively manageable. The commercial scope of digital twin technology is extensive and includes manufacturing processes, smart cities, energy, medicine, and the automotive and aerospace industries. In manufacturing processes, digital twins may be constructed on the basis of data generated at each stage of production, enabling real time monitoring and optimization of the production environment. In addition, throughout the product life cycle, digital twins may be used to monitor and optimize product performance, thereby contributing to quality improvement.

C. Military Applications of Digital Twin Technology

Most of the subsystem technologies that comprise a digital twin are readily applicable to the military domain. As a result, the scope of military utilization of digital twin technology is extensive. Digital twins were previously employed in the development of complex structures such as jet engines. More recently, however, they have emerged as core tools for tracking dynamic threats in the battlespace, enhancing situational awareness, and optimizing defense logistics.

As national security threats become increasingly sophisticated, real time digital twins are playing a pivotal role in securing operational advantage within a rapidly evolving defense environment. They support enhanced military decisionmaking and optimized force deployment.

Although digital twin technology has been particularly emphasized in civilian manufacturing processes, its importance has grown in military manufacturing as well. Military manufacturing must ensure that materiel meets required performance standards under diverse and adverse operating conditions. This necessitates the creation of extreme environments during requirements planning and design stages in order to validate system performance. In addition, weapon systems possess complex architectures, which require monitoring across each stage of the manufacturing process. Following fielding, weapon systems also involve ongoing maintenance demands for diverse subsystems and components.

In light of these considerations, the United States has taken a leading role. In 2010, the Defense Advanced Research Projects Agency(DARPA) employed digital twin technology through the Adaptive Vehicle Make(AVM) program to enhance the efficiency of research, development, and manufacturing processes for ground vehicles. Digital twin technology was also implemented in the development of the F-35 program to manage and measure design, fabrication, and manufacturing processes in real time.

Digital twin technology enables not only the projection of the physical world into virtual space but also the identification of threats within the virtual environment itself, thereby facilitating a range of cyber operations. In other words, digital twin technology may be applied to cyberspace, where information exchange and sharing occur through networks and computers. Cyber operations are generally divided into defensive activities, which protect information systems from cyber threats and attacks, and offensive activities, which deny, disrupt, degrade, or destroy an adversary’s cyber capabilities. When digital twin technology is employed, artificial intelligence can generate attack scenarios and tools, enabling simulated penetration testing that exposes vulnerabilities in defensive systems. In particular, the twin environment allows for the assessment of potential damage within virtual space. For example, NASA has developed a digital twin of the “IIR GPS“ satellite to conduct simulated intrusion operations, including hacking and vulnerability monitoring, in support of cybersecurity detection. The military application of digital technology has likewise been led by the United States. The U.S. Department of Defense “Digital Engineering Strategy Initiative“ formally articulated the digital twin concept at the policy level.

3. Challenges and Implications in the Security Context

A. Convergence and Acceleration of Emerging Technologies

Digital twin technology is exerting broad influence across emerging technology security, economic security, and military security domains. Accordingly, addressing the potential negative effects and misuse of digital twin technology has become an important policy consideration. Due to the nexus and convergent characteristics of emerging technologies, not only the inherent risks embedded in such technologies but also security threats enabled by their application are becoming increasingly sophisticated and complex. Supply chain security concerns may arise with respect to the individual advanced technologies that constitute digital twin systems. In addition, stability concerns have been raised regarding sensitive technological elements such as real world data. The application of digital technologies in the military domain is possible in both offensive and defensive dimensions. Should digital technologies be adopted at scale in the military sphere, they may function as a potential game changer. As digital twin technology becomes more widely adopted, security vulnerabilities and related threats associated with its various constituent technological components are likely to intensify and expand. The convergent characteristics of digital twin technology are particularly relevant to the conduct of multi domain operations, as they enable integrated information, intelligence surveillance reconnaissance, and operational execution across land, maritime, space, and cyber theaters.

B. Rising Uncertainty and Potential Transformation in AI Innovation

AI Technological innovation has accelerated advances across the individual subsystems that constitute AI. As a result, interest in the future trajectory of AI innovation continues to grow. At the present stage, however, implementation of digital twin technology remains at an early level overall. Due to high costs and technical complexity, most countries are deploying digital twin systems on a limited and fragmented basis for specific operations rather than at scale. By contrast, leading technological powers such as the United States and China have demonstrated significant commitment to the development and demonstration of digital twin technologies. There remains a strong possibility that the timeline and scale of digital twin deployment could accelerate rapidly. At the same time, progress in digital twin systems is driving innovation across the underlying AI subsystems, including data, computing capabilities such as semiconductors and equipment, and algorithms and models in the software layer. In recent years, generative AI-based on large language models such asChatGPT and DeepSeek has advanced rapidly. Attention is increasingly focused onthe scope and durability of their long-term impact. Major technological powersare uniformly emphasizing the concept of “sovereign AI.” However, the specificstrategies and policy approaches for advancing sovereign AI differ by country.In practice, with the exception of the United States and China, most countriesface structural limitations in pursuing sovereign AI policies across alltechnological domains of AI. In emerging technology sectors, maintainingdecisive technological advantage is critical. A “fast follower” strategy islikely to prove ineffective. Accordingly, a more viable approach for manycountries may be to concentrate investment in areas aligned with their domesticAI ecosystems in order to sustain comparative advantage.

Recent concerns regarding the potential onset of an “AIwinter” carry important implications for AI innovation trajectories. Theseconcerns stem from the perception that heavy investment in large language modelcentered innovation may yield limited returns relative to capital expenditures,a pattern that echoes experiences in the 1970s and 1980s. China’s recentprogress in AI applications has also influenced this debate. There is a growingpossibility that investment may shift toward commercialization and application-orientedsectors capable of generating short term returns. In this context, thedevelopment of “physical AI” and the nexus and convergence between AI and othertechnological domains constitute important drivers of change. Digital twintechnology is expected to play a leading role in this transition. Innovation indigital twin systems is increasingly viewed by major AI powers as a potentialalternative technological pathway in the event of an AI winter. Looking ahead,there is a substantial possibility of technological advancement through theintegration of digital twin systems with generative AI.

C. Intensification of Emerging Technology Controls and the Potential Emergence of a Post-Wassenaar Arrangement

As strategic competition between the United States and China in the technological domain intensifies, both countries’ emerging technology innovation strategies are being pursued in parallel with policies aimed at constraining the other’s technological development. During the first Trump Administration, the Export Control Reform Act of 2018 introduced a comprehensive technology control policy targeting Emerging and Fundamental Technology(EFT). This policy was driven by a perception of strategic urgency that China was overtaking the United States in key advanced technology sectors. Following the midterm elections of a second Trump Administration, it is anticipated that more comprehensive controls on emerging technologies vis à vis China will be advanced. In the recently announced AI Action Plan (July 21, 2025) and three executive orders, it explicitly calls for strengthened export controls on AI related technologies to China. China, in response to U.S. technology controls, has in recent years continued to strengthen its own export control legal framework. In 2025, China expanded export controls on key minerals, equipment, and manufacturing processes in which it holds a comparative advantage.In connection with the potential emergence of a post-Wassenaar arrangement, a central issue in recent export control debates concerns the expansion of controls over specific component technologies that constitute digital systems, including AI, computing, and software. These issues generate not only tensions between the United States and Western countries on one side and emerging economies on the other, but also divergences of interest among countries with otherwise similar positions. The United States has expressed a preference for establishing a separate consultative process to address these matters. It has been reported that the U.S. Department of State is considering the addition of advanced AI, computing, and software technologies that comprise digital systems as new items on the International Traffic in Arms Regulations(ITAR) Munitions List(ML) in order to expand regulatory control.

D. Deepening AI-Cyber Nexus Security Threats

AI and cyber technologies share a high degree oftechnical similarity. Both rely on common foundational technologies, includingnetworks, data processing and transmission systems, computing hardware, andsoftware. Digital twin technology likewise utilizes many of the technologiestraditionally applied within cyberspace, including data systems, IoT, cloud,and network architectures. In particular, ground-based systems supporting multidomain operations, including those related to space, have evolved largely intoICT based software systems and are therefore vulnerable to cyber attacks. Atthe same time, the recent increase in AI-enabled cyber operations hascontributed to the growing sophistication and complexity of cyber threats. Asdigital twin technology becomes more widely and systematically employed, it maybe exposed to a diverse array of such threats. A significant portion of AItechnologies is frequently utilized in offensive domains, whereas digital twinsystems may be applied in both offensive and defensive contexts. Ensuring thesecurity of communication channels between digital twin systems and IoT devicesconstitutes a central requirement for maintaining system stability. Issuesrelated to data integrity and personal information protection must also beconsidered as key safety and security concerns in the deployment andutilization of digital twin technologies. Blockchain based security approacheshave been proposed to enhance data integrity. While such approaches may offeradvantages in distributed environments, they may not be well suited to IoTresources due to constraints related to data integration and computingcapacity. The application of digital twin technology requires real time datasharing processes. At the same time, protection of personal information remainsa critical policy consideration.

4. Policy Considerations

As the development and evolution of emerging technologies are proceeding rapidly, as in the case of digital twin technology, the need to reassess national science and technology innovation strategies is becoming more prominent. At present, 12 national strategic technologies have been designated and innovation plans are being pursued. However, the timing appears appropriate for an overall review, and there is a growing view that the 12 fields may be excessively numerous. Specifically, instrumental technologies of different levels are mixed with domains and sectors, limiting the potential to enhance synergies and avoid overlapping investments. In light of the efficient allocation of limited resources, a reassessment is warranted. A primary challenge in most countries’ technology innovation strategies is the allocation of limited resources through stakeholder coordination. In this context, priorities must be set, and in some cases concentrated investment in selected technology areas should be considered. In designating specific fields of technological innovation, the accumulation of technological capabilities and engineering feasibility are important, but the selection of technologies that are essential from the perspective of South Korea’s long term national strategy is equally critical. In this regard, although digital twin technology remains at an early stage of application and entails substantial financial requirements and technical challenges depending on implementation objectives, it is evident that it will become a technological domain of the next generation in conjunction with AI. In the same context, considering the dual use nature of emerging technologies and the dynamics of technology competition, technology innovation strategies should be formulated not only in terms of technological development but also at the level of comprehensive security strategy, including emerging technology security, economic security, and military security. As both the United States and China are expected to strengthen technology control policies, the establishment of like minded groupings and minilateral cooperation frameworks is essential.

The interconnected and convergent characteristics of emerging technologies contribute to the increasing complexity and sophistication of new security threats, making responses to evolving threats urgent. Technologies such as digital twins are being applied across diverse fields, and associated threat factors are also increasing, raising concerns regarding the security of the linkages among individual technologies. In addition to responses to existing cyber threats and efforts to protect the vulnerabilities of critical infrastructure, risk assessments should take into account threats linked to emerging technologies, inherent risks within the technologies themselves, and vulnerabilities in distributed network systems. In particular, as security threats are increasing with the application of AI technologies, recent trends go beyond safety and security considerations to include broader security threat assessments.

In the process of global standardization discussions on digital twin technology, more active participation by South Korea is necessary. In the individual component technologies that constitute digital twins, Korea possesses competitive strengths. If participation in standardization discussions remains passive, there will be limitations in shaping standards favorable to national interests. The issues of emerging technology innovation and standard setting should be viewed as mutually reinforcing rather than as regulatory constraints, and approached from the perspective of standardization for innovation and the direction of innovation. Standardization discussions on emerging technologies also encompass national commercial interests, and it should be recognized that preempting standards constitutes a major national interest. Attention should also be paid to the fact that the United States and the European Union are establishing standards and risk assessment criteria that incorporate security considerations at the national level in relation to AI. In promoting South Korea’s emerging technology diplomacy and international cooperation, appropriate diplomatic tools are required, and major countries are equipping technology diplomacy tool-kits to conduct outreach aligned with their technology innovation policies and diplomatic directions.

Given that innovation in digital twin technology and many other emerging technologies, as well as national strategies, are inevitably formulated and implemented on the basis of interdisciplinary research, South Korea also faces an urgent need to establish platforms for interdisciplinary research and discussion. To this end, the government should support the formation of expert pools and the establishment of consortia among research institutions, and identify existing best practices. Interdisciplinary barriers often stem from gaps in mutual understanding, and a key cause is the absence of sustained and substantive platforms for research and dialogue. Efforts at the United Nations level to address AI governance and related issues likewise emphasize the importance of interdisciplinary discussion, and this consideration should be reflected in the selection of independent expert panels. It should also be noted that, in formulating and implementing technology innovation strategies, both the United States and China regard the removal of interdisciplinary barriers as a major issue and consider the establishment of interdisciplinary consultative bodies to overcome them as essential.