So, looking to this specific industries future, we have more and more computers that are being created, we have more and more computers that are being sort of inserted into various different parts of our lives, from originally computers to laptops, to mobile phones to smartwatches, to tablets to now, security cameras, that's, you know, computers or even in fridges, or you've got the routers, televisions, they're, you know, starting to really actually not just get into every particular, you know, niche and part of your life, but they're actually disappearing as well and becoming much cheaper, much more prevalent in everything we do. So we've got a hugely expanding number of computers. And we've already, you know, got this system and this set of software and hardware systems that are already severely broken. So adding more computers and more complexity to it is obviously, most likely not going to help that broken state of security and how well things are actually locked down.

So on top of all this, you've also got, you know, your AI and your machine learning, which are again, progressively making those previously old tricks that required either very generic spamming or very high level manual tasks to now be fully automated and fully, you know, perfectly executed as you would every single time. Already, AI has the power to mimic entire news articles. There's currently entire, you know, publishers out there that regularly produce basically news feed and articles and pieces that they present to the public, as you know, assumingly Written by normal, regular article writing people, when in fact they're actually written by machine learning algorithms. And these ones are automatically producing over one and a half billion stories annually. And imagine a program on that sort of level, that type of sophistication. Also having access to something like you know, your personal details, any type of public information that you've posted out there, say on, again, your Facebook or your Twitter or anything like that.

And then trying to basically write one of those phishing emails to try and get you to click on that link. This is the sort of, you know, basically, thing that we're up against and that these cyber security experts are up against trying to fend off against these highly personalized emails. You know, it could mimic your mother, it could say that it was her it could come from her email address, and it could be talking about something that she regularly talks about, because you know, you've been talking about it in a public place, such as on Facebook or Twitter. And you would click that link every single time and security experts have to see that plan. Have that and try and mitigate those sorts of brand new risks that are coming into the fold. Another thing that AI machine learning can do is actually automate the process of security auditing.

So usually when a program comes out and wants to be independently reviewed, you'll have security software security experts sort of go over the actual program with a fine tooth comb. They know what to look for in terms of potential, you know, bugs or exploits, and they can identify for good or bad, whether that code is secure or whether it can be exploited. Now you've got huge amounts of conferences that do this every year, they try and hack into, you know, browsers or operating systems and companies can even pay out multi thousand dollar awards for these people, demonstrating how they've hacked into their systems so that they can gain they can then go and fix it and they don't actually suffer those breaches. So it is quite a big industry already. But this is something that machine learning can actually do even faster. Then humans, so it can take an input of a programs code and sort of output all the potential exploits or problems or issues that that program might have.

Now, this is obviously, both as you know, it's kind of like a double edged sword, it's good and bad, it can be used for good, it can be used to very quickly, you know, review software and go, Oh, there's all these bugs and issues and problems that we need to fix with it and make it more secure. But at the same time, it can also be used by people with, you know, bad intentions, to analyze code and go, there's a really quick way I can get into that system, and often go. So these are the types of things that AI is bringing to the table and machine learning is certainly a very, very big part of software security in cybersecurity at the moment. It's also being used to detect viruses as well. Traditionally, there were you know, lists of this is a virus search your files for this virus if it's there you have the virus, very simple list by antivirus systems.

Nowadays, a lot of AI, antivirus programs, they don't like also continue to look at those sites, types of things, but they'll also look at your computer as a whole. And if they see a certain pattern of any file, even if it's not identified as a virus, if they see any pattern of you know, starting to delete lots of files, you know, copying files into critical windows or system files or something like that behavior that looks like it is a virus or malware that will be recognized and alerted. So it is both a good and bad thing having AI and obviously both sides are going to use it. There's always been, you know, the black hats and the white hats when it's come when it comes to cybersecurity. But AI is just taking this to the next level. Now, while no one can, you know, 100% accurately predict the future.

What is very clear is that, as I said, ai machine learning will continue to be very big public To this industry, but beyond that, we'll be having a lot more devices connected, which means we'll be having a lot more sources and potential destinations of where these hacks and security issues will take place. You know, previously, it might have just been your computer that might have been hacked. Now, it could be your router. Now, it could be your camera. Now, it could be your television, or maybe even your wristwatch, you know, many, many more vectors and places that they can attack and cause issues and damage to. On top of that, with this increasing number of devices and increasing number of tools, the permutations about how they can make these attacks is increasing exponentially as well.

So it's not just the fact that you had previously one type of attack site phishing emails as well describing before. But now with these new tools, you can get multiple different ways of using that you can either descend it as a generic email or you can use AI and machine learning to send it as a really personal lysed email that is much more successful. So you have multiple different ways to use what is essentially the same original tactic. So more devices, more ways of attacking more permutations, it's all leading to a huge increase about cybersecurity and, you know, having to deal with this issue as a whole. So 10 years ago, if your company was hacked, or you know, your systems were compromised, the data or the access level that they got, while it was obviously a bad thing, it sort of pales in comparison to what the access and the critical data that I get today, you know, 10 years ago, someone hacked into a company maybe they'd put malware or spyware all over their computer and a try and sell a Viagra pills or something can get me to call someone into go and, you know, clean out all the computers and, you know, obviously a bad thing, but it's fixable.

Today, you know, in 2017, we had the breach on Equifax where essentially half of All Americans, you know, personal details and financial details were stolen things, they can't change things like social security numbers or their name and date of birth, those sorts of things that are critical pieces of, you know, secure information that they can't change and is now in the hands of criminals, one machine. So there's this obvious sort of increase in ramp up of the level of detail and the data that computers are storing, and then becoming more and more serious, and I can only imagine what it will be like in another 10 years, you know, what type of data will be out there for people to steal essentially via cyber attacks? You know, will it be just your you know, financial details or your personal details? Will it be your current, you know, live health status, or will it be all your previous addresses or, you know, I can't even imagine what that information might be, but it's clear that it is going to be continuing to escalate Important.

So the importance of keeping that, you know, secure is obviously going to be more important to. Now, I know a lot of this talk again, might sound very ominous and worrying and, you know, disappointing and scary that you know, in another 10 years, who knows what data they'll be able to steal and it's all birkins I, you know, you won't be able to do anything. But this is actually why I wanted to start with this particular industry because whilst there are a lot of things to worry about, it represents a huge opportunity and people aren't just gonna ignore security or you know, not pay for it or anything like that is a huge ongoing issue, and it will continue to be as such, I see it as one of the most employable sectors in the future. There's going to be a huge need for, you know, security analysts, people that know how to write good code that is nice and secure and doesn't have bugs in it or people that need to review those set those types.

Have code for bugs or security breaches, there is going to be a lot of work that is done by machine learning or AI. But there's always going to be a need for those people there to work with AI and Windows systems, whether it's anything from writing the actual code itself to monitoring the programs as they're rolled out. So join me in the next part where we'll go over a number of potential jobs that might be you know, there currently are there and will be in the future for cyber security. And we'll also go over the critical live examples that have been talking about where we'll get to see the actual strategy in use. So I'll talk to you then