Protecting your digital twin
With the help of connected devices, networks and supporting infrastructure, digital twins enable true two-way communication between the physical and the digital world.
This presents a unique challenge for security teams, who have to realise that traditional protective measures won’t be enough to keep systems and data safe.
Instead, security efforts must expand to cover hardware and software – and the information that passes between the two. This means encrypting the connection between the digital twin and the physical asset it replicates, and taking a more holistic approach to ensuring data privacy from the outset of all your projects.
The good news is, there are lots of tools and technologies available to help ensure the security and privacy of your data. The most difficult decision you might have to make is which you choose – a complete stack suite or a mix of customised solutions.
The overall approach you take to the security of your digital twin is vital, too. With that in mind, here are three things you can do to make sure you stay on top of things:
1. Identify a purpose with risks assessed
Your security requirements will initially be dictated by the needs of your digital twin, so it’s important to start with specific use cases in mind and gain an understanding of the information and control your end-users require.
By collaborating closely with your people, you can define what level of digital twin should be developed – whether it’s for an asset, process, or system – and the capabilities it needs.
For instance, is it necessary to have real-time two-way communication? What should the maximal latency of your network be? And what risks, data security and privacy issues could be involved?
Once you have answers to these big questions, it’s easier to define a data governance and management strategy that will keep your twin, your asset and your data secure.
2. Set data profiling parameters
The next step is to identify and categorise your data sources, which will include both your legacy systems and new sources, like connected IoT sensors.
As part of this data profiling exercise, you should assign critical parameters and legal requirements to each dataset.
This requires asking some key questions, like ‘is this dataset publicly or privately owned?’, ‘which license does it fall under?’, ‘which part of the dataset needs to be anonymised?’, ‘if data isn’t available how can we generate it?’, and ‘how do we transfer data between systems in a secure way?’
3. Ensure data governance
These parameters and policies can then be combined with user-specific data governance policies, to ensure the highest possible level of privacy and the lowest level of risk.
To make sure these policies are appropriately implemented, a strong data management strategy needs to be in place. This will dictate who is responsible for data at different parts of its lifecycle, like data engineers, data analysts, data stewards, or business analysts.
For each dataset, you must then ask what identity access management, data reaction, and data residency requirements there are. These requirements must be met throughout the entire lifecycle of the data, while it’s being ingested, while it’s at rest, and during computation.
It sounds like a lot to think about. But as we said, there are numerous products available to help you apply important data governance processes, like masking, redaction, differential privacy, encryption, and lifecycle management.
There are also principles and frameworks under development for ensuring data is shared securely, openly and, with adequate quality to deliver true value and insight.
The important thing is to have a holistic overview of your needs before deciding which technology to opt for and which principles to follow.
