A digital twin is a virtual representation of a physical entity, kept current with data from it, that people use to monitor, predict and test. It is useful because you can try changes on the copy. It adds risk because it joins sensors, models and often control systems, so the data feeds need protecting.
What does NIST call a digital twin?
NIST's glossary, quoting NIST IR 8356, defines a digital twin as the virtual representation of a physical or perceived real-world entity, concept or notion. NIST's digital twins page describes a particular type of computer model of a physical system, such as a machine or building, with potential for high accuracy, precision and flexibility.
It adds that digital twins primarily function to make predictions or to serve as a status indicator for the system being modelled, and that forecasting is a foundational element. NIST gives manufacturing as a leading use case, for example deciding when to run maintenance or where to place machinery.
How is a twin different from a simulation?
A simulation runs a model with assumptions you provide. A twin is tied to a specific real asset and updated with its actual data, so it reflects that machine's state today. That link is the point and the risk: data flows in from the physical system, and in some designs, decisions or commands flow back.
What are the parts?
Think of five parts: sensors or data sources on the physical asset, a data pipeline that carries readings, a model that represents behaviour, a store for history and an interface where people or systems act on results. Some designs add an automatic feedback path to change settings on the asset.
Where does security risk come in?
Poisoned inputs. If an attacker can alter sensor data, the twin's picture of reality is wrong, and predictions or decisions based on it fail. A twin that says a pump is healthy when it is failing is worse than none.
Exposed models and history. A twin's data shows how a plant or product behaves. That is valuable to competitors and useful for planning an attack.
A path into operational systems. Where the twin writes back to equipment, compromising it can mean compromising the physical process. Treat any write path as a control channel with strict authentication and limits.
Sprawl of connections. Twins pull from many systems, each with its own accounts and APIs.
None of this is specific to twins; it is the same discipline as any connected system, applied to a new integration point.
A constructed example
A manufacturer builds a twin of one production line to predict when a bearing needs replacement. Vibration sensors send readings every second through a gateway to a cloud model.
The team separates read and write. The sensor feed is one way, authenticated per device, with readings checked against plausible ranges, so a wildly impossible value is flagged. The twin has no path to change machine settings; it creates a maintenance ticket and a person decides. After six months they consider letting it adjust speeds automatically, and that decision comes with its own threat review, a hard limit on the adjustment range and a manual override.
How do you secure one?
Start from NIST's wider guidance for the systems involved. For industrial networks, segmentation and zero trust principles apply, as in NIST SP 800-207: no implicit trust from network location, with authentication and authorisation before each session. For the software, apply the secure development practices in NIST SP 800-218 to the twin's code and dependencies, and keep an inventory of its components.
Record where each data feed comes from and who owns it. Validate inputs. Log access to models and history. Plan for the twin being wrong, and make sure a person can tell.
How to check or apply this
- Draw the data flows between the physical asset, the twin and any consumer.
- Mark every path that writes back to equipment and put strict controls on each.
- Authenticate every data source and check readings against plausible ranges.
- Segment the twin's network from operational control networks.
- Inventory the twin's software components and scan their dependencies.
- Restrict and log access to models and historical data.
- Test what happens when the twin is fed bad data.
Quick check
Is a digital twin the same as a 3D model?
No. A 3D model shows shape. A twin is a model kept in step with a real asset's data and used for monitoring or prediction.
Should a twin control equipment automatically?
Only after a separate risk review, with limits on what it can change and a human override. Many deployments start with advice only.
Sources
- NIST, Digital twins
- NIST glossary, digital twin
- NIST SP 800-207, Zero Trust Architecture
- NIST SP 800-218, SSDF
Last reviewed: 18 October 2026.
