Martin Kuppinger: Where AI, Industrial IoT, Consumer IoT, Blockchain, Decentralized Identity, and Edge Computing Meet

And let's get started. So as I, as I said, the title is some of the password bingo, where I AI industrial IOT. So in a lot of things, consumer IOT, blockchain, decentralized identity, and edge computing, media cybers is really what, what I believe makes things move enables a lot of things in digital transformation, from production to the impact of modern 5g based infrastructure. So that is what I'd like to talk about in the next couple of minutes. So to get started, I, well, I quickly started with a little bit of a definition of cybernetics and make clear.

This is not something really entirely new, it's long available. It's widely used, but it's still trending and it's probably maybe more trending than ever before. So these control circuits, which are behind cybernetics and the, that concept that has been established back in the 1940s are the foundation for a wide range of things.

And so I just picked a picture for the Wikipedia, which gives a little bit of view on such a controlled circuit, which means you have input data, you get data back and then it is tracked and it might be adjusted and is given out to a system or again, going into a feedback loop. So it's really this feedback loop thing, which helps the system to continuously adjust and approve, improve for, for what it needs to do. And this is basically the idea.

And I think already that makes, makes clear why things like IOT and AI, cetera are related because it's about having a lot of input data, syncs sensors deliver this data. And it's about understanding how to, to deal with the data, how to process the data, how to learn from the data. And that is where obviously AI can bring in bring a well, so this is long away.

So the term is that to be first used first being used by plateau, but sort of the firmly establishment of the term happened in 90, 47 by no, a scientist was very much associated with the term of cybernetics, but again, concepts go back quite a while with James, what did things which could be mapped into the cybernetics scene, etcetera. It is widely used. And when you look at the, the areas of, of application of cybernetics, then this is in the computer science such as an AI, such as in computer vision control systems and manufacturing. That is where factually I learned first about it.

I also learned about it for instance, in certain management concepts, but you also find in robotics, bio biology, mathematics, cetera. It's really a widely used concept at the end of the day. And I think from the still logic behind that, and when you dive a little deeper, for instance, in the theory of cybernetics, you'll find there are so many things unless you're anyway had in one of your studies, which is pretty likely, and it's trending.

And I think that's the point where really, as I've said, AI comes into play where we see also more autonomous systems, autonomous systems at the end of the day, rely on that. They rely on this feedback loop on the, how can I get better? How can I control things? If you look at smart manufacturing, it's really ubiquitous where, where you have immense data amounts of data for sensors per second, hundreds of signals, you receive, you need to process, you need to, to use to improve what you're doing. And so this is also where a AAC comes in or connected vehicles.

So it's, it's even more relevant than it ever has been before. And it is tightly related with a number of these technologies. We see that, so to speak, make the world move. So technologies that are of a specific relevance, this in digital transformation. And some of these technologies are AI very clearly. So whatever we finally call AI, and that is subject to separate discussions, but technologies help that help in augmenting users and automation in, in the systems. These are essential for, for all, for many of the, the improvements we see in digital transformation.

And that is about dealing with vast amounts of data, as I've already said at high speed. And one of the areas where data comes from is from IOT devices, speed, industrial IOT, such as in smart manufacturing or we consumer IOT. So all the devices you are using in your, your personal life, and we have billions of sensors and other things delivering data. So we are talking about huge numbers of things, also huge numbers of devices. And we are talking about incredible amounts of data that we can use for our control circuit cycles for our control circuits and things must that go wrong here.

So think about autonomous driving. That is something where this control are must work well, where we rely on that to, to really be successful. Then we have blockchain, which is not that much hype maybe anymore as it has been, but it is important for certain privacy use cases for a secure and, and very viable taker storage of data and complex scenarios. So look at, for instance, Iotta, which is a concept in the IOT space, which is some sort of distributed lecture, not exactly a blockchain, but these concepts are very hyped for a good reason when it comes to digital transformation use cases.

And again, they play, play a role in that, not the only one for sorting data, for managing data, but an essential role because they enable us to do certain things better than without that. We have to seem of decentralized identity. We at cook and Kohl are following for, since it exists and where we also have our separately events on. But it's very obvious that when we have a world of things and devices and everyone owns many of these, we need identity concepts that work with all of these things seamlessly, not having the need to create one new identity per device or thing we are using.

We need to make it better. So we need an autonomous identity in some way, for our ubiquitous use across everything seen, then there's edge computing. It's a little bit. So this pendulums moving back in cloud computing saying, we need to shift some workloads closer to the premises, for instance, around manufacturing, or we need to move certain things closer to the device and the thing when it's remotely. So when we deal with Wes are things, certain workloads must be processed very close to them. When we look at manufacturing, certain workloads must be very close to that.

And so this is also a concept which helps us in, in dealing with certain of these changes and which plays into that. Because again, not only, it's not only relevant Fort use cases, but for these it's of specific relevance.

And again, so to make the work move, we need to look at edge computing and we need to look at 5g. Finally, be it in, in private 5g networks, in, in certain manufacturing environments and fabrics, or it beat 5g in the public, which enables to transport the data at the speed. We need to have it transported because as of again, that these control circuits need a lot of data. They need to process it very fast. And that also means the transport of data needs to be extremely fast. So these are the things which come together.

And when we done the look at so to speak our, our cybernetics control circuit at the center, how does this fit into all this? How does this play with all the other things? And then we have industrial IOT, for instance, a huge number of sensors. And we need to transport the data, which means we need 5g. We have blockchain or other types of lectures, which might be used and to store certain certain of the data to deliver data.

Again, this is in the plane it's required. And again, for input data for, for, for information to control circuit or to the AI related to this entire thing. So this AI, then again, maps to 5g maps, to our distributed lecturers, to our blockchains, whatever we haven't used. Clearly it is the thing which helps in, in, in processing all the data for understanding what to change, what to, so are we good or not? What do we need to adjust the center? This is really what comes in and the same is true for consumer IOT.

So data has processed and we might have some edge computing in beware in between trust for the sake of, of being fast enough in processing in workloads then. So this comes very frequently in that concept.

Again, again, blockchains AI play a role and specifically for consumer IOT, decentralized identities are super essential. So it is a complex scenario. And we need to understand that all these things are, are really neatly tightly intertwined. So given that we have a focus on security, you probably expect me also to talk a little bit about security. I want to do that relatively shortly, but I want touch this topic. So what about security? And it is key to error solution because we can't get, let the circuit get out of control.

It's, it's nothing which it must not happen. And very clearly the more elements we have, the more connected they are, the more they are at risk. And one thing I I tell for years right now is there's a simple rule. Once you're connected, you are under attack. You must assume that once you make a, put a system online, put something online, there will be automated attacks in a relatively short period of time starting. So we need to understand that these are under attack.

And we also know, I think from all the incidents we've seen over the past couple of years, that many of these systems are of specific interest for attackers, they of interest because industrial ESP, because of in, in some way more, more really conflicts between different states and, and other things. So there are so many scenarios where these, these models are at risk that we clearly need to look very. So roughly on that. And there are, there are two parts of the story. One is security. We need to understand security.

We need to under ensure that results are well at unbiased, reliable, and we need to implement a DEC web security and governance mechanisms for this entire environment. We can't do it for just a single piece. We need to do it more in a holistic manner. We need to understand how these things are related and what we need to do in security and governance across the entire solution. So everything which is in some way related to this cybernetics approach to the control circuit must be in, in focus. And the other element is safety.

So safety is the other part, which means we must ensure that a car doesn't crash, that a factory, that machines are factory doesn't don't crash, that they don't go out of business. All these things related to safety are essential as well, and security and safety. The more you have connected systems to closer, they are related to each other. So we need to work hand in hand between operational technology, between safety and between security here. So it is something we can't isolate anymore. It is it's, it is a integrated play. And also there are some things to mention about identity.

So there are so many identities right now, and we need to understand everything has an identity. We are talking not only about the human identities, we're talking about billions of identities, of things, probably billions of devices already. And we talk about complex relationships. So things are part of devices. If you look at a, we pretty complex, you could say, this is a big thing, which consists of many small things. And then you have identities and you have very complex relationships between that.

So we need to understand that there's an identity of things we need to deal with bigger numbers and these complex relationships. So for a vehicle, there's the owner, there's the garage. There's the, the driver, there are the people sitting in the car. There's the police. There are so many different organizations and, and, and, and individuals dealing with. So with the complex set of things and devices in the car, you have to whatever, the entertainment, multimedia system and other things you have to the entry and so many other elements, and all this is related. We need to understand this.

We need to manage this. And then we go back to our picture from a few minutes ahead that it means there are a lot of other things coming in. We have sensors, we have sensors in the consumer IOT in 5g, in edge computing, blockchain, etc. All they can pro they all can provide information about what is happening here. So we have information we can collect for security. We can use for security to understand how these things relate and how they work with each other. And we must do it. We must move to integrated security approaches, and we have the identity with sensors as well.

So when is Martin authenticating or whatever, another element here. So security and identity are related and identity again, is relevant for everything for accessing the 5g network. There's the identity of those things. There's the identity of consumer IOT, things and devices, et cetera.

And last, not least there's governance and governance then builds on security and on identity to, to really give us the, the insight about the risks, cetera, this is what we need to do and depth then directly. So governance goes then back in some way to control, to adjust our systems. That brings me to our summary finally, and there's three things I'd like to give to, to rephrase. And then reemphasize AI is essential for today's system, but it's not alone.

We need to understand how AI maps, the digital transformation, how it maps to cyber nets and how it maps to a wide range of other technologies to make things work. These concepts are tightly intertwined. So without add computing without 5g or innovative lecturers, a lot of use cases will network.

And we need to do that then in a way where we add security, identity governance across all of these systems, because the more we have, the more complex our network is, the more it is at risk, the more risk of failure and the world we are talking about failures can be very, very, so we need to really work on that beyond governance to a holistic concept. That is what we need to do. So rethink the way your solutions work, cybernetics control, service, AI, and more. That is what I'd like to give you today. Thank you for listening to me.