The Internet of Things is evolving as a value driver across the board from manufacturing to health care. But as digital transformation unlocks value, it also exposes cybersecurity vulnerabilities amid a growing threat landscape. Now there is the digital twin, which is both a growing way to harness the power of the Internet of Things (IoT) and a source of cyber risk.

IoT and Data Security

The Digital Twin Concept is Growing, Bringing More Security Challenges

A digital twin is a virtual model of a physical device or system, designed for running simulations. Developers can test and build new products and systems without creating risk associated with real-world trial and error. On systems already in place, digital twins serve as proxies so managers can monitor, respond to changes, and make improvements without having to shut down operations or create unnecessary risk.

In manufacturing, for example, digital twins can be used to monitor equipment, proactively planning for repairs and service. Operations managers are able to see the impact of making changes across a wide variety of factors, all without having to implement any changes at all in the physical world.

Of course, all of this runs on data. Digital twins are often created by using a static 3D CAD model of the object or system, and turning that into a dynamic model by adding data collected during the lifecycle of the product (or system).

In the last few years, thanks to advances in data analytics, the Internet of Things (IoT), cloud technology, and artificial intelligence (AI), the use of digital twins has taken off. By 2025, the market for this technology is expected to reach USD 3.8 billion. Having taken root in the engineering environment, it is now increasingly popular in a wide range of use cases across the business landscape.

As such, the security implications are enormous. Much of this is due to the exploding use of IoT devices and the data they transmit.

Digital Twin Technology is Fueled by the IoT

In digital simulations, especially when applied to equipment and systems monitoring, the IoT is essential.

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Using real-world data from sensors placed on the actual equipment, digital twin technology uses simulation to produce predictions, thereby foreseeing problems before they arise. Sensors are what drive the data collection that powers a digital twin’s functionality – without them, there is no real-time data and without rea-time data, a simulation is a weak approximation of the real thing.

As was mentioned above, the IoT is what gives life (i.e. critical real-time data on a range of factors) to digital twins.

The IoT sensors are, unfortunately, where cybersecurity vulnerabilities can crop up. Now that digital twinning is growing, adding yet another avenue of risk for IoT connections, security professionals will have to think even more deeply about how to secure their IoT ecosystems.

Public and Private Data- Siloed No More?

Digital twins rely on data – as much as possible and from as many varied sources as possible. But what happens when previously siloed public and private data are swirled together in the mix of a digital twin model? Data silos are meant to keep private information private, from falling into the hands of unauthorized users or, in the case of a breach, the public. Data hackers will certainly be looking for ways to exploit any possible security vulnerabilities associated with digital twins.

But from the perspective of someone who’s looking to glean insights or set up a digital twin, data siloing is an obstacle. For them, it represents a lack of transparency and a huge obstacle to efficiency. The answer is to enable interactions between data sources from a security-minded framework. These interactions must be brokered, secured, and continually monitored so that your digital twins have lifetime access to all data. Even if an organization does not use digital twins, unlocking data silos is considered important.

In fact, data transparency is commonly viewed as a critical step in the journey of digital transformation – as important as updating/replacing legacy systems or securing the infrastructure with advanced threat protection.

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Another issue with digital twins (and, not coincidentally, with digital transformation) is data interoperability.

Interoperability Challenges with Digital Twins

As data moves from devices and equipment in the field (or on the manufacturing floor), to the software used in data twin modeling systems, there is the risk of getting data you cannot trust or use. In large enterprise settings, it’s possible that hundreds of digital twins can be deployed. Some products may even have a presence in several different digital twins, at different levels:

  1. Component
  2. Asset
  3. System
  4. Process

This hierarchy of digital twins produces different perspectives – incredibly valuable insight – yet may also generate different types of data and complex relationships between data sets that results in data interoperability issues. Component-based digital twins might feed asset-based digital twins you must be able to trust your data or the whole concept of producing a twin model of the actual object goes right out the window.

CIOs should ensure that access to data is available from many different sources along the entire product value chain by setting up the proper architecture definitions, identity management, and access controls.

Another data interoperability issue is that, over long periods of time, data formats and data storage can evolve. As the digital twin amasses growing historical data, the risk of having unreadable data runs higher. CIOs should plan for the long term when setting up their digital twin models, keeping data format and storage options consistent over the lifetime of the twin.

As with all data systems, so is the case with digital twins: your insights are only as good as your data.

Digital Twins and Digital Transformation

Digital twin technology sounds futuristic but it is really just one of the newest ways to harness the power if the IoT and the data it generates. Nothing that new, really, as model simulation has been around for decades. The difference is that now, we have the IoT, data analytics, AI, and the cloud to power even better models for even better results and more value.

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Already, organizations are using digital twins to monitor operational performance in manufacturing. Now, we are starting to see the digital twin concept expanded for enterprise-wide benefits across the whole value chain. The security implications are nothing new, either. Data interoperability, IoT security, data siloing, and data privacy are all challenges we have known before. It’s just that now, there’s even more reason to address them.

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