What Is a Digital Twin?
A digital twin is a continuously updated virtual representation of a physical system, process, or environment that uses real-time data feeds, physics-based simulation, and AI/ML models to mirror, predict, and optimise the behaviour of its real-world counterpart. Digital twins are used in manufacturing (modelling production lines), infrastructure (simulating buildings and utility networks), healthcare (replicating patient physiology), energy (modelling power grids), and urban planning (simulating cities). Unlike a static model, a digital twin evolves with its physical counterpart — updating continuously as new data arrives.
Why it matters for governance
Digital twins create governance obligations when their outputs inform real-world decisions. Model validation must be ongoing (not just at deployment) because digital twins can diverge from reality as physical conditions change. Data governance is critical because digital twins consume large volumes of real-time operational data that may include personal data. Decision accountability must be clear — when a digital twin recommends reducing maintenance intervals or changing production parameters, who is responsible for that decision? The EU AI Act may classify digital twins used as safety components of products as high-risk AI.