Digital Twins in the Workplace Raise Legal and Ethical Questions

Digital twin technology is increasingly used in workplaces to model employee behavior and support decision-making, but its adoption brings legal, ethical, and technical challenges.

Digital twins are virtual models that represent real-world objects, processes, or individuals and are used to simulate and predict outcomes. In employment settings, these models—sometimes called digital twins of employees—allow organizations to analyze productivity, safety, and performance by processing data from sources such as emails, keystrokes, calendars, and location tracking.

Employers can use digital twins to forecast workforce trends, detect signs of burnout, and plan organizational changes. Some implementations rely on artificial intelligence to enhance these predictive capabilities, while even basic logic-based systems have demonstrated cost-saving potential in manufacturing environments.

The introduction of digital twins in the workplace has prompted concerns about employee privacy, consent, and surveillance. Issues such as algorithmic bias and potential impacts on worker autonomy have also been identified as areas requiring attention.

What the numbers show

• Simple digital twin systems have saved up to €2.5 million per year in manufacturing
• Kyndryl launched an AI-powered workplace digital twin in April 2026
• The EU AI Act classifies workplace digital twins as high-risk systems

Legal uncertainties surround digital twin projects, especially where multiple parties contribute data. Questions about data ownership, liability, confidentiality, and intellectual property rights remain unresolved in many cases, particularly as the technology becomes more complex and widely adopted.

In healthcare, digital twins may be involved in liability lawsuits if recommendations are incorrect or if data protection regulations, such as the General Data Protection Regulation (GDPR), are breached. Intellectual property issues can also arise, for example when freelancers’ work is used to train AI models without explicit permission or when digital twins generate content that is defamatory or plagiarized.

Technical requirements for digital twins include high-performance computing infrastructure, cloud storage, and advanced data processing. These needs can increase operational costs and introduce additional privacy and cybersecurity risks for organizations deploying the technology.

According to the U.S. Government Accountability Office, digital twin technology is less developed in biological and human applications, and regulatory standards for these uses are still evolving. The EU AI Act requires bias mitigation and transparency for workplace monitoring systems, including digital twins, reflecting the growing regulatory focus on these tools.

* This article is based on publicly available information at the time of writing.