Navigating GDPR in Digital Twins for Industrial IoT and Manufacturing

Digital twins have transformed industrial operations by enabling manufacturers to create dynamic, real-time virtual counterparts of physical assets. In concert with the Industrial Internet of Things (IIoT), these digital twins facilitate predictive maintenance, optimise supply chains, and enhance decision-making through data-driven insights. However, as these technologies collect and process vast amounts of data—some of which may pertain to individuals—they occasionally intersect with privacy legislation, particularly the General Data Protection Regulation (GDPR).

GDPR, enacted by the European Union in 2018, was designed to safeguard personal data and uphold individual rights in the digital age. While industrial systems are primarily focused on machinery and processes, the inclusion of human-generated data—for example, employee interactions with machines, sensor data that may be tied to operators, or location information—introduces complex compliance requirements. Understanding how GDPR applies in this context is essential for organisations making strategic investments in smart manufacturing and IIoT technologies.

Demystifying Digital Twins in the Manufacturing Landscape

In essence, a digital twin is a virtual model designed to replicate a physical object, process, or system. These models are continuously updated using real-time data from sensors and other sources, providing an accurate digital reflection of the physical counterpart. The integration of IIoT plays a pivotal role in feeding data into digital twins. Sensors embedded in equipment collect telemetry such as temperature, vibration, and performance metrics, while software analyses trends to offer actionable insights.

Within manufacturing, digital twins are often used to monitor machinery, simulate changes to production lines, test hypotheses for new configurations, and reduce downtime through predictive maintenance. The use of this technology leads to not only greater operational efficiency but also increased visibility into the daily workings of expansive facilities, enabling leaders to make proactive, informed decisions.

Yet, this same visibility can also capture behavioural patterns and workflows of human employees. Over time, the data from wearable devices, smart ID cards, shift scheduling systems, and collaboration with co-bots (collaborative robots) can reveal individual habits, movements, and even inferred health information. This is where the potential overlap with GDPR becomes evident.

Identifying Personal Data in Industrial Contexts

A common misconception in industrial settings is that GDPR is not applicable because systems are not designed to process personal data intentionally. However, GDPR defines personal data broadly—as any information relating to an identified or identifiable person. Even in manufacturing environments, data could inadvertently or indirectly be linked back to an individual, especially when collected systematically over time.

Consider wearable devices used for worker safety monitoring. These may track location, movement, or biometric data under the guise of improving workplace conditions and ensuring compliance with safety protocols. If this information can be associated with a specific employee, even if the system was designed to focus on safety metrics, it qualifies as personal data under GDPR.

Further challenges arise when layering this data into a digital twin. Associations can be made between specific workflow efficiencies and the individuals operating the machines, raising questions around surveillance, profiling, and consent. The more granular and individuated the data, the stronger the implications under GDPR.

Legal Basis for Processing in Industrial Settings

Under GDPR, every instance of personal data processing must have a legal basis. In the industrial digital twin context, several justifications might be applicable. These include fulfilling contractual obligations, complying with legal requirements (e.g., health and safety mandates), or pursuing legitimate interests provided these are balanced against the rights and freedoms of employees.

Legitimate interest is often leaned upon when companies seek to improve operational efficiency. However, it requires careful assessment. This involves a three-part test: establishing a legitimate interest, demonstrating necessity, and conducting a balancing exercise to weigh employee rights. Documentation is crucial here, both for internal governance and for furnishing evidence to regulators if questioned.

When the data processed is sensitive—such as health-related metrics from biometric wearables—GDPR imposes additional safeguards. Explicit consent may be required, which cannot be bundled into employment contracts as it must be freely given, specific, informed, and unambiguous. Given the power imbalance between employer and employee, obtaining valid consent in workplace settings presents a nuanced challenge.

Data Minimisation and Purpose Limitation Principles

Two foundational GDPR principles—data minimisation and purpose limitation—take on particular importance in the deployment of digital twin technology. Data minimisation mandates that organisations collect only the data necessary to fulfil intended purposes. Purpose limitation ensures that data collected for one reason is not repurposed in ways incompatible with the original intent.

In practice, this means that manufacturers must design their data flows to collect only pertinent data. If machine telemetry alone can drive predictive maintenance models, then adding employee behavioural metrics could raise compliance flags unless these serve a demonstrably necessary function.

Engineers and stakeholders developing digital twin architectures must map out data flows meticulously. Where personal data is collected, they should document the specific purpose, the data types involved, and controls to prevent misuse. Transparent data management improves not only privacy compliance but also professional trust among employees who might otherwise fear being monitored covertly.

Navigating Transparency and Employee Communications

One of GDPR’s core mandates is the principle of transparency. Individuals must be informed about how their data is used, their rights concerning it, and the legal basis underpinning its processing. When this involves employees in a manufacturing setting, organisations should invest in thorough, comprehensible communication strategies.

Privacy notices tailored to industrial workflows should be made accessible and jargon-free. These notices must detail the type of data being collected—especially if devices like wearables or biometric gateways are used—how long data will be retained, and the avenues available for employees to exercise data access, rectification, or objection rights.

Open communication not only supports legal compliance but also fosters a culture of trust. It reassures workers that technologies are there to improve safety and efficiency rather than to monitor productivity surreptitiously or punish deviations.

Data Security and Protection Measures

Digital twins rely on large streams of integrated data, often hosted on cloud platforms or transferred across systems for analysis. With the convergence of IT and operational technology (OT), cybersecurity becomes a critical concern. GDPR requires organisations to implement appropriate technical and organisational measures to ensure data security.

In the manufacturing space, the risk profile includes not only cyber intrusion but also internal misuse or accidental exposure. Role-based access controls, encryption, network segmentation, and real-time monitoring are some of the standard practices that should be applied. These must be evaluated in context: what is ‘appropriate’ depends on the sensitivity of the data, the risk of breach, and the potential consequences for both individuals and the organisation.

Regular audits, vulnerability assessments, and incident response protocols are now a compliance necessity. Documentation plays a critical role: maintaining records of security reviews, staff training, and incident logs can demonstrate GDPR accountability in a regulator’s scrutiny.

The Role of Data Protection Impact Assessments (DPIAs)

In cases where digital twin systems could lead to a high risk to individual rights—particularly when deploying new technologies or processing sensitive biometric or locational data—conducting a Data Protection Impact Assessment (DPIA) becomes essential.

DPIAs provide a framework to identify risks and outline mitigations before deployment. They evaluate the proportionality of data processing activities, the safeguards in place, and the potential impacts on data subjects. For digital twins in industrial settings, DPIAs can help anticipate concerns related to constant surveillance or profiling of employees.

Performing DPIAs should be seen not as an administrative exercise but as a design-stage step. Including legal, IT security, and HR professionals in these assessments ensures a holistic approach. Moreover, DPIAs can identify opportunities to process data more ethically by minimising data capture or increasing anonymisation.

Anonymisation and Pseudonymisation Strategies

Given the challenges of handling personal data, some manufacturers may explore ways to de-identify data. GDPR distinguishes between personal data, pseudonymised data (where identifiers are removed but could be re-linked), and anonymous data (where re-identification is impossible).

True anonymisation, when done correctly, puts data out of scope of GDPR. However, achieving this level of de-identification in dynamic, sensor-driven environments is difficult. Even pseudonymised data still requires GDPR compliance, though it carries a lower risk profile.

Nonetheless, these techniques remain valuable privacy-enhancing tools, especially when designing digital twins that rely on behavioural or human-machine interaction data. Where feasible, collecting aggregated or anonymised datasets should be seen as the default approach.

Looking Ahead: Ethical Innovation and Compliance Synergy

The proliferation of digital twins in manufacturing heralds an era of unprecedented automation, insight, and efficiency. However, as these systems increasingly model not just machines but also human interactions with those machines, responsible data management becomes critical.

Rather than viewing GDPR as a restraint, forward-looking manufacturers can embrace it as a framework for ethical innovation. Building privacy considerations into digital twin design not only mitigates regulatory risk but also enhances employee goodwill and social trust.

Cross-functional collaboration is key. Legal teams, data engineers, plant managers, and workers’ representatives must come together to align innovation with individual rights. Through privacy-by-design principles and a commitment to transparency, manufacturers can use digital twin technologies not only to transform operations but also to lead the way in embedding digital ethics in industry.

In this new industrial paradigm, data has not only economic value but also societal implications. Complying with GDPR is not merely a compliance checkbox—it is a testament to a company’s integrity in a data-driven future.