- In the digital twin domain,
data is at the core of value creation.
Subsequently, how that data is managed
and secured must be a priority.
I've discussed the importance
of data and analytics previously,
so here I'll focus on the security aspects
of data management.
Let's briefly describe the data environment
of a simple digital twin ecosystem.
First, in the Design phrase,
as a virtual product is being designed and simulated
potentially high volumes of data are created,
stored and analyzed.
Next, in the Build or production phase,
data is collected by sensors in real time
and this data travels over a network to a collection point.
This could be a local data center, but it could also be,
and it's increasing likely to be, in the cloud.
Third, during operations and maintenance,
much like in the Build phase,
volumes of valuable data are collected,
transmitted, analyzed and stored.
In both Build and Operate,
data can also be sent to sensors and systems,
so in fact data transmission is increasingly bidirectional.
Finally, access to digital twin data is by nature liberal,
since value is gleaned by having a large number
of stakeholders accessing and collaborating around it.
The data landscape and opportunity
for data management challenges are deep and wide.
In cybersecurity parlance, we would say digital twins
have a high number of attack vectors.
Without a doubt, digital twin data is sensitive.
At a minimum, it contains confidential
and competitive information,
but in the worse case a bad actor can create chaos
and damage if data or systems are breached.
Keep in mind, it's not just about access
and theft of data that must be protected.
It's also about the availability of data.
By this, I mean if a bad actor interrupts the flow
of real time data, it can have consequences
to system functions and drive up costs.
Sadly, this can also be leveraged by the bad guys
to extort a ransom.
So the risks are real.
What should be done?
Let's first recognize that the digital twin
is probably more vulnerable than the physical twin.
In this statement, I'm not including the sensors.
Unfortunately, sensors have not received
the security attention they deserve.
However, positive progress is being made.
The digital twin,
which is simply software on a connected computer,
is the target of choice for threats.
Note though that penetrating and profiling a digital twin
can help hackers later access a physical twin.
Data, when not traveling over a network,
is sitting on a computer either locally,
in a data center or in the cloud.
Cybersecurity controls must be applied
across the environment,
what cybersecurity professionals
refer to as the full attack surface.
This must include all the connected devices
and other software systems.
Digital twins and their data
interface across a multitude of solutions.
Security tools available will include encryption,
firewalls, port blocking,
advanced authentication of both devices and people,
monitoring and intrusion detection systems.
Worth noting is interesting innovation
happening in the distributed ledger technology
to provide security and trust across IoT networks.
In particular, take a look at IOTA
and its distributed ledge called Tangle.
Most importantly, since this is not a cybersecurity course,
the purpose here is to recognize that a digital twin
is a particularly attractive and vulnerable security risk.
The nature of its purpose and value
and also how it is constructed and connected,
means it deserves priority attention.
My advice?
Don't build and operate a digital twin
without also implementing an inclusive
and robust security and data management strategy.