Trends in Expert Witnessing: Digital Twins 

By Noah Bolmer

Digital twin technology, also known as a computer-implemented simulation, virtual prototype, or cyber-physical model, is a real-time virtual replica of a physical object or system. Used in industries from aerospace to municipal infrastructure, these digital models use continuous sensor data to mirror real-world assets, predict maintenance needs, and simulate operational performance.  

For expert witnesses, this technology is growing from an internal engineering dashboard into an element of civil litigation. Because a digital twin changes constantly based on real-world conditions rather than remaining static, it introduces unique challenges to standard evidence rules. When a legal dispute arises, the expert witness must prove the technical reliability of the virtual model, requiring a clear understanding of how courts, regulators, and opposing counsel evaluate the integrity of virtual replicas. 

Note that the topic of “digital persona twins”, where an AI likeness of an individual is created to consult with is outside the scope of this piece.  

Background 

The idea of building a virtual counterpart to a physical system first appeared in aerospace programs, where engineers needed a way to monitor spacecraft remotely. NASA’s early work established the principle: a continuously updated model that could reflect the condition of equipment in orbit. Industry picked up the concept a decade later, applying it to turbines, jet engines, and manufacturing lines. At that stage, the technology was limited by the cost of sensors and the computing power required to process streams of data in real time. 

The landscape shifted as the Internet of Things matured. Cheap, reliable sensors became standard in industrial equipment, and connectivity allowed those sensors to feed data continuously into centralized systems. At the same time, advances in processor capacity and cloud infrastructure made it possible to run complex simulations without prohibitive expense. What had been a specialized tool for aerospace became accessible to other heavy industry, including energy plants, factories, and municipal projects. As the technology has become more commonplace, courts are being asked to decide whether these models can stand as reliable reflections of reality. 

Regulations 

Regulators frame digital twins within broader technology and compliance agendas across multiple international jurisdictions, shifting the focus toward standardization, cross-platform interoperability, and long-term risk management. In the United States, the National Institute of Standards and Technology released NIST IR 8356, which explicitly establishes technical baselines for security, trust, and data integrity in cyber-physical digital twin architectures. By standardizing these core operational parameters, NIST helps define recognized technical practices that experts can rely upon when demonstrating the reliability, security, and validation of a digital twin in legal proceedings. 

Internationally, regulatory frameworks are evolving to treat digital twins as established industrial tools requiring structured governmental oversight. The European Union addresses this technology through the EU AI Act and parallel digital data initiatives, mandating that AI-driven digital twins used as safety components in critical infrastructure meet strict transparency, auditability, and validation requirements. This stringent regulatory stance sets a high bar for European tribunals, where compliance with the Act may provide an important benchmark when evaluating the reliability and governance of AI-driven digital twins. Meanwhile, Singapore’s Smart Nation program takes a governance-first approach by integrating national-scale digital twins into urban planning and issuing targeted guidelines on data streams and privacy protection. Although these international frameworks differ in their primary emphasis—technical standards in the U.S., risk classification in the EU, and public data governance in Singapore—they collectively help define emerging expectations for virtual model reliability, governance, and validation. 

Evidentiary Standards 

Although technical standards and regulatory guidance governing digital twins continue to mature, those frameworks do not directly establish courtroom admissibility rules. Lacking a dedicated legal framework for evaluating digital twins as evidence, courts must instead apply traditional evidentiary principles to these evolving technologies. As a result, expert witnesses must translate technical validation, operational integrity, and regulatory compliance into evidence that satisfies judicial gatekeeping standards. Introducing a digital twin into a legal proceeding as a reliable reflection of reality generally requires demonstrating several core elements of technical and operational reliability, including: 

• Scientific Validity: Grounding the underlying engineering software in accepted scientific theories and peer-reviewed principles. 

• Data Chain of Custody: Verifying that telemetry entering the model remained materially unaltered throughout collection, transmission, and storage. 

• Version Integrity: Documenting the software version and code configurations active at the precise time of the incident. 

• Validation Testing: Demonstrating the model’s error rate by proving how closely virtual outputs match physical test results. 

• Hardware Synchronization: Confirming that the processing hardware did not introduce data lag or computing anomalies. 

Meeting these operational criteria helps convert a dynamic virtual simulation into stable, verifiable evidence. By establishing this clear line of technical validation, an expert witness grounds the software model in reality, moving the courtroom focusaway from the complexity of the technology and onto the core elements of the case. 

Litigation in the Absence of Precedent  

Because digital twins remain an emerging technology, courts have not yet developed a substantial body of precedent addressing their evidentiary status or liability implications. Therefore, disputes involving these systems must be analyzed through existing doctrines governing intellectual property, software, data governance, privacy, and expert testimony. 

Writing in the North Carolina Journal of Law & Technology, Brynn Story argues that digital twins raise significant questions involving privacy, cybersecurity, accountability, and governance that current legal structures do not fully address. In the absence of a dedicated legal framework, courts evaluating disputes involving digital twins must often apply existing legal doctrines to the technology’s constituent components rather than to the digital twin as a distinct legal asset. Trade secret law may protect proprietary simulation methodologies, patent law may protect novel technical implementations, and copyright law may protect the underlying software code. Yet these doctrines were developed before continuously synchronized virtual models became commercially widespread and do not always provide clear answers to questions involving real-time data streams, multi-party system inputs, or continuously evolving simulations. 

Until a more developed body of precedent emerges, expert witnesses remain central to establishing the reliability and significance of digital twin evidence. In many cases, the persuasive value of a digital twin will depend less on specialized legal doctrine than on an expert’s ability to explain how the model was constructed, validated, maintained, and connected to the physical system it was designed to represent. 

Expert Analysis 

The integration of digital twins into complex disputes radically transforms the traditional role of the expert witness. Instead of merely offering a retrospective opinion on physical failures, a modern expert must actively defend the continuous technical lifecycle of a dynamic, virtual system. Cross-examinations might focus heavily on algorithmic integrity, data processing pipelines, and sensor reliability. To survive judicial scrutiny and establish credibility, an expert’s testimony must comprehensively address at least three core technical pillars: 

• Algorithmic Variance: Explaining the mathematical margin of error between the digital twin’s predictive physics formulas and actual physical wear patterns over time. 

• Telemetry Ingestion: Verifying that the real-time sensor streams feeding the model were captured, encrypted, and processed without dropping critical data packets. 

• Boundary Conditions: Proving that the simulation parameters were restricted to realistic operational ranges, preventing biased or mathematically forced outcomes. 

This shift may change how legal teams prepare technical witnesses. Experts can no longer treat a digital twin as a closed-box software program that simply outputs an answer. They must possess the specialized technical fluency required to explain how the software’s underlying math translates to physical reality. Ultimately, the evidentiary weight of a digital twin rests entirely on the expert’s ability to prove that the virtual asset is a highly accurate, fully auditable mirror of the physical reality. 

Conclusion 

When introducing a digital twin into a legal proceeding, an expert witness must demonstrate the reliability of a continuous software loop. Cross-examinations will increasingly target the data ingestion pipeline, the error rates of the physics engine, and any discrepancies between virtual predictions and physical realities. Surviving this judicial scrutiny requires experts to approach cyber-physical modeling with a defensive, audit-first mindset during the initial build phase. Ultimately, courtroom credibility is won or lost on the verifiable integrity of the underlying data. 

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