This is our, third and final webinar in the series. Hi, Jessica. How are you doing?
Hey, Phil, I'm great. How are you?
I am doing well. I'm getting over a little bit of a sickness, and if you have kids, you know what that's like, when your kids get sick, you know, it's just you're on borrowed time.
That's right. That's true.
Had a couple of days that was, you know. Anyway, it was interesting.
So when we decided we were going to do this webinar series with Check Point and with you, this was the one that I was the most excited about because this is the one that everyone wants to talk about.
And before we jump in, I want to just introduce you. So talk a bit about your background just in case this is the first webinar people have joined.
So, just talk about your background, I'll introduce myself and then we'll get in and get going.
Yeah, absolutely. So I head up Check Point Strategic Advantage service provider organization now, but I spent the last, over a decade in the security space working with and talking to customers and partners about securing their data or their customer's data and how to do that in a streamlined way we'll say.
Okay, well, and then I'm Philip, so I'm CEO of SecurEdge. We were a VAR and MSP for 15 years, and in the last several years of that we started building software, interesting enough to do automation. And we ended up selling off that IT practice or the MSP business, and then just being a software company.
And so again, the software created was for automation, so it's really fun because we've been working on this for several years, but now kind of the whole world is talking about automation, all that stuff so we're just kind of showing up and going hey yeah that's what we've been doing for five years.
It's a buzzword now.
Today what we're going to do is because it was such a topic that I felt like was in high demand, what I want to do is in the very beginning, I just want to go really broad and talk about generative AI and do all like the industry stuff and talk about that. And then we're going to head into the how we apply that into automation around Network as a Service and specifically about running a services practice.
And so let's jump in.
What is Generative AI?
So, the first thing that people want to know, because it's a it's kind of an industry buzzword, is well, what the hell is generative AI?
And it's something that it's everybody talks about it, you know, it's kind of the buzzwords now, but I wanted to define it. So how do you think about it? How do you think about generative AI?
Yeah, generative AI is really a tool that we can use, right? So think about I think people are probably most familiar with Chat GPT.
A million users in the first five days that they launched that platform. Yeah, but it's a tool that, that takes the data that we give it and it actually produces out new content for us.
Right. Which is amazing. So, so I don't know where, where you were the first time you played around with that Chat GPT thing, but I was like, holy crap.
AI icons graphic source: askviable.com
Like this is this is different. This isn't like Google search. Like, this is a whole new world. And the definition of generative AI is that it can produce, you know, content on our behalf, essentially.
But the second part of the kind of the story I believe is really the AI model itself, and this is where I think it comes back to kind of our business and the VAR business, because what A.I. does is it learns based on the data that it was given.
Data is the Differentiator
So if you're looking at even the evolution of Chat GPT going from 3.5 to 4 and these new versions, they get infinitely more intelligent as they go along. And so what I think is really important to understand is it's not just this concept of they can do things, it's what are we training it with?
And I think more and more the models themselves are going to be even open source now where you can you can go and find these types of models.
It's really the things that are inside the model, which is the data that you're feeding it with, where it's helping it make these types of decisions.
What your what your thought process on that.
Yeah, absolutely. You're going to be more effective if you got that good data. When we think about like even with Check Point right, our secret sauce to our Threatcloud AI and what we've been doing for decades is that experienced data that we're able to take from all of the customers that we have across the globe and put it into these AI engines that we've got now to kick out data faster, better and in a more robust way to protect everyone around the globe.
Well, what I think is interesting is when we were talking about this, you know, we were talking to several folks, including you and others at Check Point, and you guys were like, yeah, we've been doing this for a decade.
You know, manufacturers like Check Point have always had these types of models where they're learning from the other users on the platform and then they're sharing that information across everybody to minimize threats.
But what I think is interesting is we're now applying the same type of methodology you guys have been doing for a long time. And we're starting to think about how can we apply this to the business side of networking.
The Impact of AI
And so I pulled some of these kind of general slides because they're so interesting. So McKinsey, obviously everybody knows McKinsey, the big research firm, is saying that there's going to be a $2.6 trillion annual impact in and using generative AI in business.
Data source: McKinsey
And this is really, really crazy.
But the really interesting part is, is when is that it's going to be applied towards 63 different use cases. And this is where I think we can learn a lot from it. And if you look at the highest value, it's what we're doing as service providers every day. Sales, they talk about internal development and that's more team development, product development and marketing, and then also customer operations, which is just a huge opportunity.
And we're we're talking about Network as a Service, the goal is to be able to sell a customer essentially a subscription. And so the more we can automate of that process, the in theory, the more value that we're creating for our company
That's right. And your customers benefit from that.
Well, that's exactly right. I think in our first call we were talking about the digital experience that we offer our customers.
Why Should You Adopt AI?
So I'm going to tell some stories here just for a second. It triggered us. So we're implementing this type of software and this type of functionality for several big companies, their VARs and MSPs. And what's interesting is you go into these companies that been around for 30 years, have existing processes, and when you go into these meetings, you automatically have people that are excited at one of the spectrum and then the other end of the spectrum you actually have people that are like detractors.
Exactly. Like, why do we need to do this? We already have a process. We're already doing something.
And I think the answer is you implement it for your customer.
If you think about like I'm sure that Christmas season is around the corner, so we're all ordering packages on Amazon and we've got this great digital experience with Amazon and our packages get delivered to us. We know exactly where they are and they're using AI, obviously, and they're using all these this automation even in the warehousing. And what we're talking about is we need to not just receive that from other companies we need to provide that as a service provider.
That's right. Can you imagine if Amazon was still taking orders with paper and pen? Right. Looking at human history like that's where it all started. Everybody use paper, pen, you know, grids. We moved in to just kind of compute, right, with the mainframes and the processing of tons of data and pulling all that data into one spot at least.
Right. And now we've got A.I. that can address that massive data subset and then take it to correlate and build relationships and find patterns, which is why we ordered packages last night, and I've already got them today.
Well, what I also think is funny is there are still like you're talking about green screens and paper and stuff. There's actually is still a lot of that in the VAR channel and there's a lot of people that are that are still kind of operating in that mindset. And so yeah, we've got to kind of move into this century.
It can't be looked at as this new technology, something brand spanking new. It's it's an extension of the interaction we've had with technology for a really long time.
Will AI Take My Job?
All right. So one of the questions that we get from a lot of folks and this is natural for you to start thinking about this is the question will AI take my job? So what's your take on that question for companies that are in kind of the VAR and MSP space.
Yeah, absolutely. That's probably top of mind for a lot of people. And so we have to think about it as a no, but yes. Right. And when we say yes in terms of augmenting and improving what you're doing.
Well, so I actually think the answer is absolutely it will, but I don't mean in that we don't need any more network engineers anymore. Hey, we don't need any, you know, salespeople.
I just believe that it's going to change your job.
So in a way that other you know, we've been having this—what's really funny is, if you like, study history, there's so many like periods in history where there's been new technology been invented, like, you know, the printing press.
You know, it changed the way we did business. Manufacturing lines changed the way that we created things. And there's just example after example over history.
And I think what's interesting now is that we're at this inflection point where AI is going to really change how we function as humans. But the way I think about this is I do think of it kind of like Iron Man, if you've seen that.
So in Iron Man, he's got Jarvis and then Jarvis is a robot and he's got A.I. and he's helping him build things. That's the way I view the way it's going to evolve. You have agents, if you will and they're helping you do different things. Is that your perspective as well?
Yeah, absolutely. And just to build on that analogy, right, Jarvis may be the brains behind what he's doing it or what he's doing, but in the end he Iron Man is still the one doing those things.
He's a creator, which makes it easier to do those things.
How Do We Apply AI to Network as a Service?
So I think when we're looking at the the MSP business and we're now we're back to the actual topic for today after that little sidebar, is you really think about the things you're doing on a day to day basis. You start thinking about what are the things that I need to do and what are the things that I wish I could get somebody else to do for me.
You know, support renewals as an example, suck. Could I outsource that? Could I have an agent that does that and automates those types of things for me?
And when you start looking at your job on a day to day basis, there's all sorts of opportunities for this. So we're we're looking at the network as a service business, specifically the way that we're thinking about it and when we were planning out the content was a kind of to think about how can I use A.I. to essentially help me manage the network itself?
And that's obviously your expertise. And then there's the other side of it, which is, is how can you use the AI to manage the business of providing the network to my customer, which I think we can, we can unpack in detail.
So first let's talk about managing the network.
Yeah, absolutely. So you mentioned it earlier, right? Check Point's been working on AI specifically for over ten years and we've been building technologies based around AI engines.
ThreatCloud has been kind of the term that Check Point's used as far as like the data that we have. In the last year or so, we've rebranded that a little bit and we now call it Threatcloud AI, and that's because of those technologies that we've created and adopted that again, building over years and years and years of data collection.
But with that AI engine, what we're able to do is we're able to make over 2 billion with a B, security decisions around prevention every single day.
Okay, that's a day. Now go back to the paper and pencil. Right. Write 2 billion decisions via paper and pencil. That wouldn't be a day. That would not be a human.
And the other thing you think about it's across all those different vectors as well. So, you know, device security and network security and cloud, all that kind of stuff is, you have to make these calculations across several different threat vectors I would say.
We do. We've got, you know, the ability to take the data that we're getting from all of those data sets, take the AI, you know, with the deep learning now, it's smart and it's effective and it has to be trained on that data.
But that's one of our strengths. And so we take that experience data, we put it in, and then we use APIs to push out that protection across the network gateways or mobile devices or endpoints.
And we have over 100,000 customers globally. So every single device that they're using from our that our A.I. engine detects is through APIs being pushed out to secure their devices as well around the world.
And if on the slide real quick, right, like the amount of information that we're able to process in a single day again without AI would absolutely not be possible.
We have overall over 75 threat prevention engines that we work with and 42 of them today are actually powered by AI. So we're seeing a shift. Yeah, over half, over half of our engines are AI driven, which really is taken place, you know, ten years, but in the last year alone, I think the number is 13 of those had moved to being powered by AI.
And you know, as we think about what we're doing, we're going to continue to benefit as a company Check Point, as a consumer or customer working in the network or managed services space. We're going to continue to be able to benefit from the sophisticated advancements of AI
Check Point, you know, we want to secure your everything. That's our goal.
We we have three C's and we focus on being comprehensive, which is across all those attack vectors you mentioned, right. The network, the endpoint, into the cloud. Right. And then collaborative making sure everything works together. How does it do that through AI and APIs, right?
So all of that data is shared.
And then the last part of that is really being able to have a unified management and a consolidated security operations efficacy, right?
And so Check Point got a single consolidated management portal that we leverage. And this is really behind our ability to lead the industry in vulnerability response times as well, right? The more streamlined you make things, the more you integrate AI into it.
You're not relying on a human for the speed and the kind of looking into what it is that you're that you're trying to analyze analysis. That's the word I was looking for. A consolidated management is really what allows for streamlined security operations.
Well, I think speaking from a VAR or an MSP from that perspective, having a consolidated view of all that stuff is absolutely critical.
It's one thing to have the processing power, but to have it spread across five different interfaces have to log into is just a mess.
So the unified kind of strategy there makes a lot of sense as well as the AI strategy.
Yeah, you know, if we've we've got data across a whole bunch of different engines, it's just machine learning.
Like they all have their own data that they're working on. If we collect all of that data into a single place, then we can have it powered by deep learning and the ability to take and correlate all the data together instead of these disparate systems that are doing it on their own.
Yeah, so you guys have been doing that for a really, really long time, it just happens to be the buzzword now to start calling it or talking about it again.
How Do You Apply AI to Managing Your Business?
But I think what is kind of new at least. Well, actually, this is an interesting topic as well on the business side of, you know, running the network. We've been talking about automation for 20 years, using services or software like professional services, automation, software, as an example or PSA.
But the ability to automate has changed dramatically. So I kind of look at it like you have this new version or 2.0 of automation that's going to look way different than kind of the version one that's from your your traditional PSA software. And so what what I wanted to cover quickly is just how can I adopt AI in my business, not just in the network like what you're doing with Check Point?
Well, to adopt it in the network you just buy Check Point. Right. But to adopt it in your business, that requires that just a little bit different functionality.
And we really broke it down into three steps.
The first one is, we have to connect all your your fragmented systems together. And you and I were together last week at a trade show and it was the CEO of a VAR talking about how they were, you know, using pen and paper to generate quotes.
And really what we have to do is we have to figure out how do we start connecting the PSA with the CRM, with the ITSM. And really, if there's no digital connectivity, you really can't build a AI on top of that. There's no AI for pen and paper yet, no room to probably do that. But it doesn't exist at this point.
So that the step one kind of is this connectivity problem.
And then the second thing that you want to think about how to do is how can I start to take my processes and build an all digital workflow with it? And what you're seeing here is you're actually seeing a digital workflow for a sales process at a VAR.
So if you think about what you do as the human is, you have essentially a discovery process with your customer where you go and collect information about how am I going to be providing security or what's the network and the size of the building where I'm providing it.
Then you go away and you build a billion materials by hand typically, and you might even build it in a spreadsheet or in somebody else's tool.
And then you go through the pricing in that costing process all the way to get to the point where I'm spitting out essentially a proposal to my customer. And so what you have to do is get that process essentially digitized. And then once you can digitize that process, now you can look for places to plug in automation.
And this is where stuff gets really exciting and I'm actually going to demo for example, the bill of materials creation is one of the things that take a lot of time for an engineer. And actually what I'm going to demo today because I think that's a cool thing that I think is going to change dramatically.
How then maybe I'm opening a can of worms now, but when we think about specifically even just the bill of materials process for our for our partners and what they're doing like today, if we think about the pen and paper, like how long of a of a time is it take to go from start to finish with what they're dealing with?
Well, so so we think about this as the answer is it depends on the engineer and the customer and the complexity of the project. Obviously, the more complex, the longer it takes. But it can take several days, two weeks. If you think about all the things that you do with the customer, what's interesting about this topic is that we literally had this debate internally when we were a VAR.
We had this debate for five years internally and the debate was, are we really building bills of materials that are different every time or are they really close to each other? And if they're close to each other, could we build some automation so that this could be auto-generated at least to like 80 or 90%?
And if you played around with ChatGPT and you thought about if I were going to give if I could learn from all the previous designs that we created as a company and that I could prompt my database, if you will, or my system of learning and with a few inputs like you would see on a ChatGPT type functionality, could it spit out a bill of materials for me?
And that's actually what I'm going to demo.
But we had a camp internally that said you can't do that. And then we had the camp internally that said, no, I think we I think we can and we ultimately did.
But you think about it. So there's a lot of these types of processes that when you talk to someone, they're going to say, we're unique, you know, it's impossible to do that.
And my response is, well, we're building self-driving cars. So that's a way more complicated problem to solve than I'm building an equipment list to put in a building. So we probably can't solve that.
Where is AI Heading?
But I think there's a lot of other opportunities as well, like automating prices on quotes and, you know, just looking at the history and how you price something in the past and then spitting that out.
But I think what's important to understand from a from an operational perspective is just where AI is heading if we don't do this.
And so what I mean is, if you don't do this as a VAR or MSP you're going to be competing against another one who is. And that's the actual danger. And so you got to understand kind of where things are headed. And in the industry, what's happening is we've gone from kind of like static inputs already to very dynamic inputs, conversational type of inputs.
And I no longer have to just write in text. I'm already able to consume data through different sources, whether it's images or whatever else. And I think that's interesting. And the other thing is that the the idea behind where AI is heading is, is it's this concept of an agent that's very that's directionally focused. Hey, go give me five new prospects per day in this specific industry and add them into my CRM. Like that's where we're headed with the whole kind of agent-based concept of AI.
So the way that this looks from a process perspective, and if you think about this, the way that computers have worked for like 30 years, it's software and so it's somewhat automated, but it's really kind of dumb. Like I have to tell it on a page to like, do this, do that.
And it's really me interacting with the computer or pressing a button. And the kind of huge shift, if you seen Copilot and some of these things that Microsoft has come out with, is you're literally going to be able to say, hey, go do these things, and then the software will actually run through that process for you and then you're essentially just dealing with the result.
Based on the data, it's you're able to remove some of that human interaction because it's predicting what you would do anyways and cutting out that need in the middle.
100%. And it's actually it's a little bit scary, but there's actually a tremendous amount of opportunity.
AI Tools for IT Service Providers
So I'm going to talk about specific tools that MSPs can use today and then I'm actually going to do a demo just to show you.
So there are tools that are already out there that you can use to automate specific things like prospecting, ChatSpot, proposal writing obviously ChatGPT is an option for there and Jasper is another, bill of materials automation. And I'm going to show you that that's our software is Central Office that I'll demo. There's also a lot of really cool software out there that's able to do things like read service tickets from your PSA, organize those based on what the customer is saying.
And then also even predict the outcome. So we actually had another CEO that's supposed to join us today from Nine Minds, and they're there in AI Startup that just launched a product two weeks ago that you and I both saw a demo of recently, and they're able to do all sorts of things around automating what's happening in your service desk.
And so Pia is one, Thread is another one, and then Nine Minds are systems that are literally already there. Nine Minds. I'll highlight has a free version of their software. And so you can literally connect it to your service desk, create a free account, and then start using it right away.
And so there's a lot of really cool things that are happening right now. But what I would forecast is that 2024 is going to be a year where you're going to see all sorts of new tools get launched that are doing everything from customer experience to threat management to all sorts of stuff.
Yeah, absolutely. You thought I was a big buzzword in 2023. Just wait for 2024.
So what we created and what I'm going to demo because I wanted to have two demos today that only turned out one because Robert got sick. But I'll demo this for you so you can see kind of in action. It's way more fun to see it live and what we created just as conceptually is we created a software we call Control Office and we describe it as a business automation platform for IT service providers.
And so the way that we think about it is you really need digital rails, if you will, or a framework in order to be able to add all this automation. And so we look at the sale process, the fulfill process that you operate is inside your company, and we've essentially created workflow software again so you could layer on AI into Central Office.
So with that, let me let me jump into it and and I will show you some stuff.
Can you see my screen?
Okay. All right. So the first thing that we talked about, if you want to automate, is we talked about how you have to you have to first connect your systems together. And so what we created with Central Office first is the ability to connect essentially all your apps together inside of our software.
So we have a marketplace. Where you literally can connect your CRM, your PSA, essentially the software tools, ITSM, the things that you're using today. And the reason why you want to do that is because you want to be able to automate on top of it. Obviously, that's the topic and see what to be able to connect your apps, but you also want to be able to connect to your partners.
And so if you're if you're working with companies like Ingram Micro as an example or a TD Synnex, we built direct integration with these companies so that you can now integrate workflow processes with your distributors and companies you work with on a digital basis.
And of course Check Point is here as well. And we built integration with Ingram on behalf of Check Point, but we also built a lot of the things that you're selling from Check Point into into the platform as well.
You were talking about that or is this we cover this I think in the last session.
We did but I'll definitely talk to it again. You know, as part of the platform, we've kind of harped on the fact that the data has to all be there and and you're you have all these connectors to pulling the data from all the data points.
What do you do with that data? You take that data and you understand the customer, what they're trying to do, what they're trying to secure, the network and what it looks like. And we spent a lot of time building these bundles around the Check Point offering and based on all of those metrics that the data that Central Office can take and then predictively come up with and offer out packages, these bundles that we've created from Check Point that you can leverage within your bill of materials.
Yeah. So I think that that is really important is that you're standardizing essentially your offering for your customer. And if you're if you're taking those inputs as hey I want to build this with Check Point, what Check Point solution do you sell?
You were talking earlier about the bill of materials creation and one of the things that used to drive me crazy when we had several engineers and things like that is when I would give one engineer a set of work, hey we're going to build this building, it'd be like one bill of materials.
And then I give another engineer the exact same thing, and they designed something different. And the problem with that is we're providing Network as a Service to the customer.
And so really that solution should be the same across all those instances. So, building integration directly with Check Point building those packages out is just an opportunity to standardize is the way that I looked at it.
Okay. All right. So now let's jump into some of some workflows and some automation. So I'm going to click Create quote, which is the beginning of the sales workflow if you're going to sell a network. And then I'm going to select the account that I want to generate a quote for.
Now, I'm generating a quote linking it to an existing opportunity and we're syncd with the CRM and the reason why that's really important is part of the automation you need as a service provider is forecasting.
And part of the problem with forecasting is your team's quoting system may not be connected to your CRM. So tying those two things together is super valuable. And so once I link a deal, now we're asking who do you want to send it to?
And then what we're offering up here are predefined workflows.
So do you want to sell services? Do you want to build your own? So it's just all built from scratch, or do you want to actually sell a solution where we're going to automate some of these types of functions for you? So I'm going to click Solutions and then I'm going to say I want to build a network.
And when I select network, the inputs here, this is really important kind of like in the generative AI world is the inputs here are what's the service address? And so I'm going to put it in at 2459, which is the building I'm sitting in. And then what type of site is this?
This is also important for bill of materials creation. I'm going to click single building. And then how many people are allowed in the building by law, and I'm going to say 500 to 1000. And then the next question is how big is the facility where you are putting in this network? Now, a lot of people don't actually know that.
And so we we build integration with satellites, obviously, and we're measuring the rooftop of the building at 26,000 square feet. So now we just need the user to say how many floors are in the building and there's three. And so we're getting ready to design a network for an 80,000 square foot building. Now we just need to know how is it being used?
It's being used as an office environment and then what apps does the customer want to use. And that's really the set of inputs need about the actual physical space itself.
And now when you continue, what we're asking is of the solutions that you sell? How long do you want the solution to be active for your customer? I'm going to say 60 months.
And then what do you want to build it with? And obviously I'm going to say Check Point and I'm going to say Hewlett Packard and when I hit build what we're doing here is we're generating a bill of materials.
Now what we're also doing is we're automating a lot of the pricing functions that go on behind the scenes. But what we've done there in just a I don't know, a few minutes, seconds, okay seconds.
Is we've created a bill of materials for the MSP. And to answer your question earlier is sometimes that used to take us several days.
So we go back and forth with the customer you know whatever and then the other thing I'll point out is this is not a, you know, completely self driving type thing where, I'm just replacing the engineer.
No, that would be dumb. What we're doing is we're just automating the manual process that the engineer goes through, but then they can add anything they want. So if I could just generate a bill of materials that's 80 or 90% accurate, well then the the engineer can come in and add whatever they want to this bill of materials. So it still has access to the digital catalog, all the pther kind of stuff.
Any questions there?
You know, when we think about the, you know, you talked about it, you give to engineers the same bill of materials requests you come back with two answers. You've taken all the guesswork out, right? Engineers may look for specific things differently when they're scoping devices, network requirements. The fact that you can pick the type of use, the, you know, the size of the space, calculate on its own, all of this is really streamlining it.
It takes all that guesswork out.
Yeah. So obviously where we're headed here is that in the future, your system could literally create the entire proposal for you. We're not to that point yet. And I'll and I'll point out that the bill materials creation you just saw, we're running it in two different models.
We're running it with essentially a ChatGPT style model where we're using the model to produce it and we're also running it with a set of calculators that we created and we're running those side by side.
Right now, our calculators are more accurate than the AI models today, but obviously our models are feeding the AI models, so it's not hard to know who is going to be more accurate in the future.
And so as your data feeds the model, it'll be more and more accurate, obviously, over time. And so where this could be headed is your customer could get a quote in a few seconds and and they could actually check out with that type solution and I think would really kind of revolutionize the way that your customers are buying.
And now they're buying a network in the same way that they're buying cloud, which is effectively just a web interface where I can check out with my network.
I'm going to go back to the deck and then and then see if we have anything else to cover for today. And then and then we'll see if we have any questions that I think we'll we'll wrap up for today.
So that was a demo. We had a couple of follow up things here. Danny, do we have any questions that we need to tackle before we before we try to wrap up? I have two of the first ones about the the BOM automation that you just showed. So when you get to the bill materials, the question came in around how does it know which solution stack to present?
Okay. Well, a couple of things. There. The first one is that the decision criteria in the in the BOM creation were asking, hey, which manufacturer do you want to build with? So typically an MSP is going to say, Hey, I'm standardized on Check Point, and that's my standard offering when I go to my customer. And then what we're doing is we're using the bundles that we created with Check Point, and that's essentially what the system is choosing from.
And then the inputs on that on the original page, which are the the business, the address, the type of use of the building is dictating which bundle that you choose. So if you imagine if your an IT company and I just I'll tell you this, when we were having this internal debate, the way that we answered the debate is we took ten years worth of our engineering data and we organized that based on inputs and outputs.
And we said, what are the things that we're collecting on the building every time that we can use as essentially the inputs and if we take the same inputs, are we getting basically a similar output? And the answer was within 5 to 7%, the output was the same. If we had this set of essentially inputs. And that's actually what drove the whole kind of calculator and automation as part of the process.
But did I even answer the question or did I just ramble. I think you did. The second one just wanted to get your thoughts on securing proprietary data with AI since a lot of these models are predicated on large datasets.
What is the current thinking around proprietary company data and best practices around using AI that doesn't necessarily compromise data that you don't necessarily want it to be able to use.
I think the first thing to remember is that your deep learning capability is based on the data that you allow it to have access to, to learn from.
So, you know, proprietary information you need to ensure that you're just taking appropriate compliance requirements into consideration when you're maybe pulling that data in.
And, you know, I was reading something earlier today that talks about the different types of AI models that you can use. Right. Sticking to, you know, first party tools versus a tool that's built on a tool that's built on a tool, you're going to have the better ability to control your data and ensure that you keep its integrity.
I think there's the the scariest question of the whole thing. And when ChatGPT came out, you had a bunch of people that were just like throwing everything at it. And there was a scenario where a big company that everybody knows and one of the engineering teams took the data and put it on onto the platform, and then that data became available to other people using the platform.
And so the answer is in the fine print for sure. And so I agree with the way that Jessica talked about it and that whatever software tools that you're working with that have AI you need to read the fine print and understand what they're accessing, what they're using it for. Is it shared? Is it anonymous?
And you're seeing the big vendors like Microsoft as an example, create just a crazy legal structure where as crazy that they were able to pull this off where they are essentially accepting liability if they do anything improper with your data. So it's really kind of amazing. But you have to really look at the terms of service for any software that your that you're accessing to make sure they're not doing something you don't want them to do with your data.
Should companies themselves that are looking to use AI have some sort of company policy?
So yes, yeah, I think at SecurEdge we actually had ChatGPT write a policy on ChatGPT and we put that in the so no, absolutely it's just like all the other policies need to be included.
Anything else Danny? That's it.
Okay, alright well So what follow up items do we have? Jessica Do do you want to go through here?
Yeah, absolutely. If you're not already and you'd like to learn more about the Check Point partner community, feel free to reach out checkpoint.com there's a button at the top that says partners from there it'll give you some information and allow you to fill out a form to reach out to us and get in touch. Of course, you can always reach out to me directly too.
And as far as our stuff goes, you can find more information on our website as well. This is going to go out, I think, after the fact. So you don't have to write down the really long domain name, but you can find more information about how to sell more Check Point using our software and then I think we actually had a giveaway or something like that.
I don't know if you want to talk about that. Yeah, we did. For those of you that have stuck with us over all webinars, you get extra entries, but if you joined us for one of the three parts of this series, you've been entered into a drawing. We'll go ahead post webinar today, pick a winner and we'll reach out to you via email and let you know what you want and we'll get it over to you.
So thanks for joining us. Well, thank you, Jessica for joining me. I enjoyed it. Absolutely. Always a pleasure. Okay, you guys have a great day. Thanks, bye Phil!