Risks of digital twins and how to manage them

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Digital twins have become one of the star technologies of digital transformation, but their deployment brings with it a host of risks and dilemmas that are often overlooked . We're not just talking about machines, factories, or smart cities, but also virtual replicas of people, critical processes, and entire industrial systems.

In this context, understanding the risks of digital twins, their impact on cybersecurity, privacy, and the operation of key sectors such as healthcare, logistics, and critical infrastructure is something that can no longer be postponed. We will break down what they are, where they excel, where they fall short, and what real measures can be taken to ensure this technology works in our favor, not against us.

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What exactly is a digital twin and how does it work?

A digital twin is, essentially, a virtual model that very accurately replicates an object, system, process, or even a person . It's not just a blueprint or a pretty 3D image, but a living representation that is continuously fed with real-world data.

In industrial settings, for example, a train, a wind turbine, or a production line are equipped with sensors that collect information on speed, temperature, pressure, energy consumption, vibrations, and component wear. This data is sent to a processing platform where the digital twin is updated almost in real time.

From there, the virtual copy becomes a safe laboratory : simulations are run, operational changes are tested, failures are anticipated, maintenance costs are estimated, and the impact of any modification is calculated before touching the physical system.

To achieve this fidelity, digital twins combine various technologies such as Big Data, Artificial Intelligence, Machine Learning, photogrammetry, BIM (Building Information Modeling), and predictive analytics. The more and better the model is fed, the more it resembles the original and the more reliable it becomes for decision-making.

Brief history and evolution of the concept

The origins of the current idea of ​​a digital twin can be traced back to several milestones. At a theoretical level, David Gelernter was already talking about "mirror worlds" in 1991 , but it was the challenges of industry and space that brought the concept to fruition.

During NASA's Apollo program, ground-based replicas of the spacecraft were built to reproduce conditions and potential problems . In the famous Apollo 13 mission, these models allowed for testing solutions in a controlled environment and ultimately saved the real mission. Years later, Dr. Michael Grieves formally applied the concept of a digital twin to manufacturing, and in 2010, John Vickers, also of NASA, popularized the term "digital twin."

With the rise of the Internet of Things and cloud computing, this technology has become more accessible, moving from NASA laboratories to factories, hospitals, cities, and supply chains . Today, platforms like Azure Digital Twins allow for the replication of buildings, entire neighborhoods, or complex infrastructures to analyze traffic, energy consumption, maintenance, or emergencies.

Advantages and disadvantages of digital twins

On the positive side, digital twins offer a range of powerful benefits. Process optimization is perhaps the most obvious : by simulating changes in the virtual environment, the real operation can be adjusted without blind testing or unnecessary downtime.

This capacity for controlled experimentation directly improves decision-making . Business leaders no longer rely solely on intuition or static reports; they can rely on predictive models that show what would happen if specific parameters were changed , production rates were modified, or new products were introduced.

Another major advantage is the reduction of costs and operational risks . Testing scenarios in a virtual environment prevents damage to real equipment, production downtime, and costly errors. Furthermore, by monitoring the status of critical assets in real time, it's possible to move from reactive to predictive maintenance, extending the lifespan of machinery and infrastructure.

However, the other side of the coin is equally important. Developing and maintaining a high-quality digital twin is complex and expensive . It requires specialized teams in modeling, OT/IT data integration, cybersecurity, and advanced analytics. There is no single standard platform, and each project typically involves several customized solutions.

It's also important to consider that not all objects or processes require this level of sophistication . For simple or low-impact systems, the investment in advanced sensors, communications, storage, and models may not be financially worthwhile. Furthermore, if the model isn't properly calibrated, there's a risk of making flawed decisions based on an incomplete or outdated virtual "reality . "

Cybersecurity risks in industrial digital twins

When a digital twin is connected to critical physical systems, cybersecurity becomes a critical issue . We're no longer just talking about data theft, but about direct impacts on industrial processes, essential services, and the safety of people.

A very illustrative example is that of a railway operator that uses a digital twin to decide when to perform maintenance, remove trains from service, or adjust frequencies . If an attacker manipulates sensor data or the model itself, they could make the system display a fictitiously "healthy" state of the brakes, wheels, or motors.

This invisible alteration could lead to serious mechanical failures during operation, posing risks to passengers, staff, and third parties . The same applies to refineries, power plants, chemical plants, and transportation networks: a small manipulation in the digital twin can translate into a major problem in the physical world.

Among the most relevant cybersecurity risks are data manipulation (intentional alteration of readings or parameters), exploitation of vulnerabilities in the OT, IoT and IIoT infrastructure , denial-of-service (DoS) attacks against the platforms that support the twin, and a wide field of exposure due to the large number of connected devices.

Added to all this is the enormous diversity of devices, manufacturers, and versions that coexist in industrial environments. Every sensor, PLC, gateway, or server is a potential entry point if it is not properly managed, updated, and protected. Ultimately, the digital twin amplifies both the opportunities and the attack surface.

Digital twins and OT/ICS cybersecurity: from reaction to proactivity

In critical infrastructures based on OT (Operational Technology) and industrial control systems (ICS), digitalization has boosted efficiency but has also opened up an unprecedented front of cyber threats . Power plants, refineries, automated factories, and transport networks now operate as hyperconnected ecosystems.

For years, defense has relied on firewalls, network segmentation, IDS/IPS, and incident response plans . These are necessary tools, but essentially reactive: they act only after an anomaly is detected or when an attack is already underway. In scenarios like the ransomware attack against Colonial Pipeline or the TRITON/TRISIS malware, this delayed response has proven insufficient.

The key contribution of digital twins in this context is to enable a change in mindset: to stop waiting for the blow and start simulating and anticipating it . An OT/ICS digital twin doesn't just replicate machines, but networks, configurations, data flows, security rules, and dependencies between systems.

In this virtual environment, simulated attacks can be launched, known malware can be tested, ransomware scenarios can be evaluated , or the consequences of altering a critical configuration can be verified. All of this can be done without touching the real systems and without jeopardizing operations or physical security.

When these types of virtual twins are integrated with advanced cybersecurity platforms, the simulation results can trigger automated measures , prioritized recommendations, or configuration changes in defense systems. The virtual environment effectively becomes a training ground and a place for continuous refinement.

Autonomous red teaming and continuous attack simulation

One particularly powerful use case is the combination of digital twins with AI-powered autonomous network teaming . Instead of relying solely on one-off exercises with human teams, automated agents can be deployed to constantly bombard the twin with simulated cyberattacks.

These agents can test a virtually unlimited variety of attack vectors , from exploiting known vulnerabilities to complex lateral movements, privilege escalations, or combined IT and OT attacks. The organization thus gains a dynamic map of weaknesses, preferred attacker routes, and potential consequences.

Because the digital twin is kept synchronized with the real-world environment, the simulations reflect the current state of the infrastructure and its exposure to the latest threats . Instead of testing laboratory hypotheses, the simulations test configurations and conditions that are nearly identical to those in production.

Furthermore, the twin exercises allow incident response teams to train safely. Analysts can practice detection, escalation, internal communications, and recovery from different types of attacks without compromising anything critical.

This continuous testing capability makes the digital twin a strategic tool for moving from defensive and reactive cybersecurity to a proactive and scenario-based stance , something especially critical in infrastructures where failure is not an option.

Human digital twins: privacy, ethics and new threats

While the risks are high when the twin replicates machines, the situation becomes much more delicate when we're talking about people . So-called human digital twins (HDTs) are beginning to gain traction in the healthcare sector, biomedical research, and even in corporate environments.

A digital twin of a person can include genetic information, physical traits, medical history, lifestyle habits, ongoing biometric parameters , and behavioral data. All of this is used to simulate how that individual would respond to certain drugs, therapies, or environmental conditions.

In medicine, this has enormous potential: testing drugs and treatments in virtual models before applying them to real patients can accelerate therapy development, reduce risks, and lower costs, especially in early stages. But, as researchers like Julia Etkin explain, the price in terms of data privacy and security can be high.

One of the most serious problems arises when this data is transferred to servers in other countries with less stringent legal frameworks than the patient's country of origin. Even with anonymization techniques, the unique nature of certain data (such as genetic variants) can allow for the re-identification of the individual, especially if cross-referenced with other databases.

Thus, a patient's digital twin can become an almost complete reconstruction of their biological identity and health vulnerabilities in the hands of third parties who don't always have good intentions. If we take this scenario to the extreme, the situation becomes especially disturbing with high-profile individuals, such as politicians, high-ranking military officers, or intelligence officials.

Psychological, occupational and ethical impact of human digital twins

Beyond privacy, human digital twins raise profound questions about identity, responsibility, and labor relations . We're not talking about a simple, friendly avatar, but about models capable of mimicking behaviors, work routines, and cognitive patterns.

Imagine a company creates a digital twin of an executive to simulate financial or strategic decisions . If an attacker manages to manipulate that model, they could force toxic recommendations, extract login patterns, or even automate dangerous actions based on the original executive's behavior.

The psychological aspect also comes into play: how does it affect self-esteem and a sense of accomplishment to know that your "digital clone" can do much of your work? The idea of ​​the double working while the original rests is tempting, but it opens the door to uncomfortable questions about the value of human labor and the division of responsibilities.

If a data transfer system (DTS) makes a wrong decision that causes a disaster, who is responsible? The model's developer, the organization using it, the data owner, or the regulator? The current legal and ethical framework is only just beginning to address these issues, while technology advances at breakneck speed.

From the perspective of a CISO or security officer, a human digital twin can become one of the organization's most sensitive assets , both because of the concentration of personal data and the potential for manipulating behaviors and decisions.

Digital twins in logistics and supply chains

In the logistics sector, digital twins have become a key tool for visualizing and optimizing entire supply chains . Unlike traditional monitoring systems, they integrate historical data, real-time operational information, and predictive models.

A digital logistics twin allows for the simulation of distribution centers, multimodal transport networks, and supply flows without disrupting real-world operations. It enables the detection of bottlenecks, the testing of new routes, and the evaluation of the impact of demand spikes or critical infrastructure closures.

In demand and supply planning, these models help analyze consumption variations, seasonality, or unexpected events and see how they affect inventory, merchandise replenishment, and transportation availability. The company can better prepare its resources and adjust contracts with suppliers and logistics operators.

In the design of logistics centers and warehouses, a digital twin makes it easier to calculate real capacity, visualize internal product flows, analyze order preparation times , and test the introduction of new technologies (robots, automated systems, etc.) without stopping operations.

Transportation also benefits: comparing routes, modes of transport and multimodal combinations under different scenarios (adverse weather, congestion, regulatory restrictions) allows choosing strategies with a better balance between cost, time and reliability.

Risks, cybersecurity and resilience in logistics environments

However, this entire deployment is not without risks. A logistics digital twin collects and processes a large amount of sensitive information about suppliers, routes, stock levels, critical points in the supply chain , and response times to incidents.

If this information falls into the hands of malicious actors, it can be used to plan large-scale sabotage, theft, disruptions, or fraud . An attack that alters the model or its data inputs could lead to flawed decisions with a direct impact on product availability, costs, or customer service.

Therefore, integrating digital twins into logistics requires specific cybersecurity and data governance measures , as well as close coordination with technology providers and external operators. It's not enough to view the twin as an optimization tool; it must also be treated as a critical asset from a security perspective.

At the same time, if done correctly, digital twins become allies in risk management : they help anticipate equipment failures, identify early signs of disruptions, and plan contingency actions before a problem escalates into a crisis.

They are also used to analyze the environmental footprint of operations , simulate changes in transport modes or warehouse configurations, and assess how to reduce emissions and energy consumption without compromising productivity.

Essential safeguards: how to reduce risks

To balance the benefits with the risks, it is crucial to address the security of digital twins from the project design stage , not as an afterthought. This involves conducting thorough risk analyses from the initial phase and reviewing them throughout the system's lifecycle.

A first line of defense is the continuous monitoring of assets, networks, and data . At the device level, it's advisable to monitor running processes, access, configuration changes, and anomalous activity. On the network, it's recommended to define a baseline for "normal" traffic and trigger alerts for significant deviations.

Regarding the data, it is essential to validate the authenticity of the source, the integrity of the content, and the consistency of the structure . Any suspicious data feeding the digital twin can trigger incorrect decisions, so the checks must be rigorous.

Another key element is the use of secure communication protocols and channels . Protocols without encryption, source/destination verification, or error handling should be avoided. In environments where adopting modern protocols is not feasible, defensive layers such as proxies, jumpers, IDS/IPS, and strong segmentation can be added.

In critical systems, redundancy and backups take center stage : alternative communication routes, secondary systems in passive mode capable of assuming the main role, and uninterruptible power supply (UPS) solutions to withstand power outages or dangerous fluctuations.

Asset management, hardening, and context-specific approach

Asset management is another key pillar. In an environment with numerous sensors, PLCs, servers, gateways, and IoT devices , having an up-to-date, detailed, and dynamic inventory is essential to understanding the scope of any incident and to prioritizing patches and replacements.

System hardening helps reduce the attack surface : closing unnecessary ports, limiting the execution of applications to only those that are essential, blocking the use of removable media, and strictly applying the principle of least privilege.

It's also important to understand that standard IT security measures don't always fit well in digital twin environments , especially when OT/ICS are involved. Availability requirements, the long lifecycles of industrial equipment, and the limitations of certain devices necessitate adapting defense strategies.

In the case of human digital twins and health data, there are additional legal obligations such as GDPR in Europe or HIPAA in the United States, as well as the need to carefully control cross-border data transfers and to apply robust anonymization and pseudonymization techniques.

Ultimately, the security of a digital twin cannot be treated as an isolated technological appendage , but as part of a global strategy involving management, cybersecurity teams, operations, legal, suppliers, and maintenance personnel.

Digital twins represent one of the most powerful tools for optimizing processes, anticipating failures, and making data-driven decisions in virtually any sector. However, they also concentrate sensitive data , expand the attack surface, and raise significant ethical and legal dilemmas. Those who can leverage their potential while remaining mindful of these threats, integrating best practices in cybersecurity, data management, and a human-centered approach, will have a clear competitive advantage in an environment where the boundary between the physical and digital worlds is becoming increasingly blurred.