Generative AI’s Impact on Cybersecurity Marketers

dan verton (1)

In this episode of Cybersecurity Marketing Unplugged, Dan also discusses:

  • Generative AI’s role: How it’s reshaping cybersecurity marketing careers;
  • Tips for entering the world of generative AI in marketing;
  • DIY language models: How to train your own AI for specific tasks;
  • Alternative AI tools beyond ChatGPT;
  • And much more!

With a career spanning over two decades in content marketing and journalism, Dan Verton currently serves as the Director of Content Marketing at Uptycs, a certified cybersecurity CNAPP and XDR vendor. Before that, he worked in similar roles at Cybereason and ThreatConnect. Prior to that, Dan was at LiveSafe where he spearheaded significant growth through thought leadership content and marketing campaign strategies.

Earlier, he founded Homeland Security Television (Verton Multimedia), serving as its CEO from 2005-2018, where he focused on cross-platform digital content strategies.

Given Dan’s Director-level experience in content marketing in cybersecurity, he’s witnessed a significant change in the world of content marketing, specifically with Generative AI and how it’s impacting cybersecurity marketers.

Cybersecurity marketers are already leveraging this current generation of generative AI tools, and to incredible success, I might add, but I think we are at - I like to refer to this as sort of like the Big Bang Theory. We're at the first spark of light before a very incredible expansion event in artificial intelligence. And we need to come to terms with that as marketers.

Full Transcript

This episode has been automatically transcribed by AI, please excuse any typos or grammatical errors. 

Mike D’Agostino: [00:33]

Welcome, everyone to another episode of Cybersecurity Marketing Unplugged. I’m Mike D’Agostino, general manager with CyberTheory and your host for today’s program. Thank you for joining us. Everybody’s doing it, are you? I personally can’t go through three posts on my LinkedIn feed these days without seeing some reference to generative AI, whether it be the latest cheat sheet, prompts secrets, a laundry list of AI tools and their use cases, or the latest marketer boasting how they just generated a billion dollars in pipeline revenue in just over one hour using generative AI. It’s everywhere. For me, this reminds me of the late 1990s, dating myself a bit, when every business was being convinced it needed a website. Lots of web designers came to the rescue, myself included, but nearly everyone had to self-teach themselves. Of course, the internet was just starting to come to life. So there weren’t millions of pages and YouTube videos walking you through on how to develop a website, you needed to get out of the house, go to a physical bookstore, and buy a starter’s guide, and largely experiment on your own. It feels the same way right now with the introduction of generative AI and its use cases for marketers. To my knowledge there are no four year college degrees for generative AI and marketing. At least not yet. But this is not just a fad, and it’s obvious that generative AI can be useful for cybersecurity marketers even if simply serving as a shortcut for copy creation. And I’d argue those cybersecurity marketers that do embrace generative AI and do experiment to create different use cases, those are the ones that will be at the forefront for the next wave in digital marketing. So what are the impacts of generative AI for cybersecurity marketers? How can they embrace this technology to assist in furthering their careers? What are the pitfalls? What are some of the definitive use cases? And how can cybersecurity marketers be sure not to miss this fast moving train? So to help us unpack it all, we’ve asked Dan Verton to join us – the director of content marketing for Uptycs, a unified CNAPP and XDR vendor. Dan, welcome to the show.

Dan Verton: [03:20]

Hey, thanks, Mike.

Mike D’Agostino: [03:21]

Great to have you on. So for everyone in our audience who is not familiar, please spend just a couple of minutes here, walk us through on a quick background of yourself and Uptycs, and provide a glimpse into your experience as a cybersecurity marketer at the forefront of generative AI utilization.

Dan Verton: [03:40]

Sure. So, as you said, I’m the director of content marketing for Uptycs. Uptycs is a cybersecurity firm that does unified CNAP and XDR. So we’re out there with the CrowdStrikes of the world, with the Cybereasons of the world. I’ve been a director-level content marketing professional now for about 10 years. But I cut my teeth and started my career as a journalist, after serving as an intelligence officer way back in the day. But I spent the better part of 20-21 years as a working journalist here in Washington, DC, covering cybersecurity and national security. So I brought my journalism skills to the content marketing World. And it’s only been in the last, I’d say six to 12 months that things have really begun to change in the world of content marketing. And we can get more into that as we as we discussed, AI, but that’s where I come from.

Mike D’Agostino: [04:50]

Very interesting background and kudos to you for spending so much time in content development, and then parlaying that into a marketing career. Really cool path there. Let’s jump into some of our discussion points as we have a number to get through in the next 12-15 minutes here. So let’s start off with probably the most hypothetical talking point of our discussion, just to kind of get it out of the way. And then we’ll get into some more specifics. You had mentioned, you know, in the lead up to this, that you’ve been experimenting quite a bit with AI and have a number of different use cases. But how do you foresee generative AI impacting the careers and day to day lives of specifically cybersecurity marketers?

Dan Verton: [05:35]

Yeah, so I think, is it hypothetical – yes and no. No, because it’s happening already. Cybersecurity marketers are already leveraging this current generation of generative AI tools, and to incredible success, I might add, but I think we are at – I like to refer to this as sort of like the Big Bang Theory. We’re at the first spark of light before a very incredible expansion event in artificial intelligence. And we need to come to terms with that as marketers. The pace of development of generative AI tools far outpaces what we saw when we watched the mobile revolution, when we watch Web 2.0, back in the early days, those things happen quickly, and everyone was amazed. But this is happening at a pace that is probably three to five times of those events. So what is to come is going to be incredibly exciting and incredibly powerful. And I foresee a major change in the way marketing organizations look, and the way they operate. And a lot of it has to do with just the power and scale of the tools that will be available, and the economics behind marketing. So I foresee many of the traditional roles either being rolled up into different roles or eliminated completely. And this is a reality that I think marketing professionals need to come to terms with.

Mike D’Agostino: [07:33]

Absolutely! Right on the mark there and perfect segue into our next talking point, and I love the Big Bang reference there. And that’s sort of like what I was pointing to before. And you’re right on the mark. I remember when companies were getting over to the internet that probably took a good 10 years before we got into the small- and mid-sized businesses getting a website and then your right – Web 2.0 and the mobile revolution, that was a similar event, but condensed even into a shorter amount of time. This is like taking all of those things wrapped in one and condensing it down into like a 12-month time period that is definitely going to see an explosion. But let’s talk a little bit about the consolidation that you talk about. And when I say consolidation, what I mean is fortunately, or unfortunately, within marketing departments, human resources consolidation. So talk a little bit more about that, and how do you think cybersecurity marketers can come to terms with this? And is there anything they can or should be doing to stay ahead of the curve?

Dan Verton: [08:42]

Yeah. So you’ll recall, a couple of weeks ago, I wrote a piece on LinkedIn called The Rise of the AI Content Director. And I think that my role as a director of content marketing is going to quickly evolve into this new form of AI content director, and I say that, and we’re going to get into a use case later on in our talk. But let’s look at what is possible right now with the current generation of AI tools. Now, this is not to say that human beings are going to be replaced totally. AI, as many scientists have already come out and said, is good at doing certain things. But there’s a lot of things that AI can’t do that humans are still masters of, one of which is common sense, believe it or not. You ask an AI tool to take two different size buckets full of water and combine them into one particular size bucket and it’ll give you a list of 20 steps to go through. So it’s not very good at some very basic things. It can be incredibly smart and incredibly dumb at this same time. So there will always be a role for humans, especially in cybersecurity marketing, the concept of domain expertise someone took to look out and monitor the output of these AI tools to ensure that the content is on message is appropriate for a particular persona is in the voice of a company, all of these things are going to remain however, you’re going to see and I can do this today. One person now has the capability to do campaign style production of content that would normally take 2, 3, 4 people. And when you look at, the economics of a marketing department right now, boards of directors are already catching on. I have folks that I consult for on the side from a content marketing perspective, who are saying that they’re cheap, they’re CMO and that their boards of directors are just telling them to go put it through ChatGPT and pump out the content list, juice up our SEO, so the boards are catching on. But today with various tools, I can take a single piece of content, I can create blogs, blog posts, I can create case studies, landing pages, FAQs. I can create nurture email sequences, press releases, all from a single piece of content. That is frightening from the perspective of what the future job market will look like for marketing organizations. But also it’s a alarm bell for folks to get with the program and start to train yourself and learn about this brave new future we’re facing.

Mike D’Agostino: [12:06]

Well, that’s it. I mean, I think if you’re not out there at least minimally experimenting with it right now, you have the potential of falling behind the curve. And that’s an interesting point that you made. Board of directors have caught on, you know, from a business point of view, there could be a viewpoint that AI is going to help save costs through optimization and automation. You’re still going to need, like you said humans to intervene. But from a business point of view, it seems a tad bit scary for the marketers out there right now, but also a lot of opportunity. And as we dive into the utilization of generative AI, we need to accept we are in the very beginning stages, like you said, of implementation. And the disparity between the haves are those that are experimenting with generative AI and the have nots are those who have not yet even created an OpenAI account is massive.

Dan Verton: [13:11]

At a minimum, right? I know, there’s a lot of folks out there in marketing who will say this is never going to replace the human and to a certain extent, I agree with that. There’s always going to be a role for human creativity and oversight of all this. But anything can happen from an economic standpoint, where today, even today that the CMO I’m referring to went to the board and wanted to expand headcount. And part of it was generating content. And the board literally said, just pump it through ChatGPT.

Mike D’Agostino: [13:52]

Yeah, that’s a little scary, but exciting at the same time. And it seems like, again, this is in its infancy, I’m quite sure, give it a couple of years, you’re going to have lots of major universities and everything offering AI courses, and I’m sure it’s going to trickle down into high schools and lower level education. But right now, it seems like there are a ton of different like certifications popping up though. And you mentioned that you yourself have already gone through a number of these courses. Do you have any recommendations for those just starting their journey, exploring generative AI for marketing? Are there any certifications or courses that you found particularly enlightening or useful?

Dan Verton: [14:41]

Yeah, absolutely. Anybody can find today, Google, for example, offers an entire course in generative AI for free. All you got to do is Google it and you’ll find it and it goes through everything from introduction to AI to generative AI, all the different forms of AI, to ethical use of AI and you name it. And so Google is at the forefront right now of offering just free training in this stuff and no better source than Google, which operates probably one of the largest language models in the world, several language models. So Google is a perfect example. I’m certified in Jasper, which is an AI tool that’s designed specifically for marketers. And the certification itself is focused on getting the most out of the particular tool that you’re looking for. I’m currently enrolled in Generative AI for Business Professionals at the University of Pennsylvania. So that’s another certification course. Most universities offer these types of things online, through Coursera, and other forms of training. So, my sort of philosophy on this is, I need to get out as a marketer, I need to get out ahead of what is clearly going to change my profession. And so that’s what I would recommend to all of my colleagues.

Mike D’Agostino: [16:29]

That’s great. Maybe you’ll be developing one of those four-year college degree courses at some point. But good for you for taking the initiative there. I’ll have to look into the Jasper one definitely. And again, the first use case, like I mentioned before, for generative AI, you get to ChatGPT, the first time you see the prompt there, or Bard or whatever your flavor is, it’s creating text or copy. You put in that first question, write me a cold email to a prospect about X, Y, or Z. And it’s almost magical when you hit enter, and it starts typing out the response. But generative AI goes well beyond just creating text and coming up with fancy prompts. Where is it going? Like you mentioned, training, put even some of the use cases on the side, but even like, training language models, like what does that mean? Where do you think this is going?

Dan Verton: [17:33]

Yeah, so let me just take your last sort of question there first, which is training in the model. So that’s the easy one. Currently, today, I can take a company’s public information, or if the right security controls are in place, you can take proprietary information. And you can create a knowledge base in many of these tools, where you can train the AI on your enterprise data. So in other words, you’re not just training it on the large language model that you’re using to generate content, but you’re also training it on your data, which means you no longer have to define certain terms that you use in your marketing programs, you no longer have to ensure that your writers are writing on message or in the proper voice, all of these things and branding are now taken care of by the AI tool. So consistency and scale are just, you know, they’re taken care of right off the bat. Now, the other question is, where’s this going? Just 24 hours before we take this interview, let me give you an idea of what I was doing experimenting with some of the new tools. About two months ago, I went and gave an AI company about one hour’s worth of my voice samples. And you might have known years ago, I did a podcast for a company I was employed by and so I had all this data recording. And I gave them an hour’s worth of my voice sample and I now have an AI-generated clone of my voice that is nearly indistinguishable from me being live. And I can now generate voiceovers by typing in text. So I no longer have to sit like you’re doing right now with this podcast, and talk into the podcast and record it and then go to post-production and cut it up all this stuff. All I have to do is write a script. And now that’s final. And my AI-generated voice will create the voiceover – that takes the process from three to four hours, maybe a full day, down to about 30 minutes. Another thing that’s happening is generative AI is going well beyond text and still images. Still images are getting better, but they’re still kind of clunky, especially if your image has text in it. A lot of times with AI tools, you’ll tell it to put a certain word in and the word will be misspelled. But that’s getting a lot better. But now there are AI tools available, where I can take this script I mentioned earlier, that I’ve already had my AI-generated voice, do the voiceover for, I can take that script, and I can plug it into an AI video tool. And it will go out to iStock and all the stock services, and it will actually create a full-length video with appropriate B roll. Like when you change subjects, and you’re talking about Coca Cola, it’ll have B roll of Coca Cola and it will lay out the video that takes a process that would normally, and I’ve done video production that takes a process that might take you a week, down to an hour. So, again, I say we need to prepare for this as marketers.

Mike D’Agostino: [21:29]

I’ve got ask is this really you right now?

Dan Verton: [21:35]

This is really me.

Mike D’Agostino: [21:36]

This is really awesome. I just want to double check. That’s pretty amazing. And we’ve done some interesting things here as well. We have a brand, which is our education channel and our principals there have been doing some phenomenal work. They can go from generating a script for like a simple course on like, what is PCI Compliance, for example. We generate a script using AI avatar with super high quality voiceover, includes slides from a pseudo PowerPoint presentation, and have an entire 15-minute course developed in just a few hours. It’s very, very amazing. It is. Anybody that hasn’t been looking into it or fooling around with these things needs to do so. Very, very interesting. ChatGPT has become somewhat synonymous with generative AI, but there are alternatives and lots of other applications and platforms available. You mentioned this one Jasper, which you said is a little bit more targeted, I guess for marketers, any others you can recommend? You even just gave us a couple of use cases here. But any other specifically or any others that, maybe it’s Jasper that you want to expand upon for specifically marketers?

Dan Verton: [22:59]

Yeah, so, Jasper is great. Every week, there are probably dozens of new startups. And the interesting thing about where this, goes back to your last question, where is this all going is that, all of these AI tools like OpenAI and ChatGPT and Google’s Bard, they’re starting to become embedded in things we don’t associate with AI. So you might be using AI without even realizing or have access to it. So web browsers or search, they’re all becoming API enabled. And so depends on what you need to do you know, what advantage you’re looking to gain and what tools you’re using, maybe you already have everything you need. But if you need an actual platform, there’s Jasper, I use a service called InVideo, which does these videos for you. It goes text to video, or you can actually get in there as a real video editor and do detailed editing. Also, Style Checker. A lot of us in marketing, we subscribe to one style or another. Well, AP now has an AI-driven style checker. So you can pump text into a Style Checker and it’ll find all of the Associated Press style mistakes you’ve made and suggest changes. So copy editors don’t need to check for AP style. It’s not needed anymore.

Mike D’Agostino: [24:42]

Very, very cool. And along those lines, and we do that quite a bit here at ISMG and our parent company, we post tons of articles and writeups every single day. I have to check with our editors if they’re making use of that, but let’s talk specific use cases. As we were preparing for this discussion, and you even mentioned it before, about how cybersecurity marketers can generate, like an entire campaign from one piece of content. And I know that’s all the rage these days, for obvious reasons that there’s so much emphasis put on digital content and using digital content to try to draw people through the buyers journey and the sales funnel, because let’s face it, I don’t know how to term it, fewer and fewer people want to engage with sales reps. They want to do their own research. And given the nature of things, much of that research is done online through digital content. So that has to be at the top of the list for digital marketers these days to get into market. So talk more about that, if you can expand on like, what do you mean, how is that accomplished to degenerate like an entire campaign?

Dan Verton: [26:03]

Yeah. So I’ve done this in a consulting role, as a side gig for some friends, who are CMOs. And to your point about folks not wanting to go right to a sales folk salesperson is absolutely correct. The average consumption is, I think, 12 to 15 pieces of content before they ever talk to a salesperson. So they are way ahead of understanding not only what they want, but what you can provide before they talk to that salesperson. But I had a colleague who came to me and asked me for advice on how they could use a particular whitepaper for a broader campaign that would last more than just, you know, posting it on the website and a week’s worth of promotion. And I’m a big proponent of creating once and using many. And so obviously, the first thing that I thought of was this white paper is several weeks of blog posts that I’ll point back to the to the CTA, which is download the whitepaper. But if you use an AI tool that can create campaigns – this is the important part – you can take that one piece of content, and you can train the AI on that one document. And then you can create a campaign that will not only provide you with a list of blog ideas, by title and description, but can write those blog posts; can pull out case studies; can do your Facebook ads; can do a landing page for that particular topic; can generate Google text ads; can generate FAQs based on it; can do your LinkedIn posts; your nurture email sequences; your newsletter entries; a press release, if you think it’s important enough; it can come up with an entire campaign of social media promotional tweets; can even do SMS messages if that’s how you engage with your customers. So from one piece of content – all of that what I just said, probably today takes a team of three or four different marketing functions to create. And one person can create that and put it through the editing process. I’ll give you an example of how this is happening at some major brands. So look at the soda brand Fanta. They used AI to generate, I think 15,000 versions of a social media ad campaign that ended up increasing their buyers’ intent by about 30%. Coca Cola did the same thing. The New York Times is using AI to automate processes like resizing images that go in a story for different channels, like for different social channels, so they don’t have a person doing that anymore. So this is the world we currently live in today.

Mike D’Agostino: [29:28]

Really, really cool there. And the ability to build out and that goes back to your previous statement on consolidation, to be able to do that with minimizing a bit one click that eliminates a lot of back and forth with a lot of different people. And I’m quite sure some of these things, they still require human oversight right after a piece of content is developed. You still need to make sure you go through it, but just having that shortcut, even if it can get you 30-60-80% of the way there. That’s a huge win. We’ve experimented ourselves, we’re doing a lot, we’re using it quite a bit through CyberTheory in our advisory firm here. But one of the first use cases that we started developing is around SEO, and using it, again, for pure content development to target inbound traffic from the search engines in Google. And we did a lot of research on it before we started even like getting into testing, because we’re very conscious about how search engines are going to treat AI generated content. And I think there’s a little bit of thinking out there that somehow your content is going to be penalized if you are generating mass amounts of landing pages using AI. I don’t think that’s the case, though. In fact, we even found reference on Google, specifically that their internal Wiki basically states that they are not going to penalize you for using AI-generated content. And ultimately, it’s a bit of a conflict of interest. They’ve got Bard, they’re all in on AI, they want people using AI. So to penalize them for using it seems a little contradictive. But they basically stated that so long as they stick by their rules as long as the content is informational, it’s unique, it’s informative, and it’s useful and valuable then whether it’s generated by AI or human or with human oversight, it doesn’t make a difference in their eyes.

Dan Verton: [31:44]

That’s the wildcard. The wildcard is what’s going to happen on the regulatory front going forward. What’s going to happen with the big tech companies, there’s only two or three of them that own this, and we’re at the mercy of what they do with the technology at this point. But to your point about SEO is a great example of how of an area of marketing that’s just being turned on its head by this because SEO optimization is built into all of these tools. So it takes the science of 2-3-4 people, and automates it. And that’s a very powerful thing. And SEO professionals need to need to start looking at it as well. But on the on the regulatory front, whatever happens, I think it may not hurt the producers of content as much as it will ensure that we’re not doing things like misinformation. Today I can generate an image of tuples political figures shaking hands that from two completely opposite sides of the aisle that would rival a lot of people. But it’s not true, never happened. So the thing about what’s going to happen on the regulatory front is that you need to be certain if you want to avoid these labels, like AI-generated content that you get a plagiarism checker. Because the large language models are out there. And you’re basically scraping data from the large language model when you create one of these things. And you might also be leveraging data that your competitors created. That’s another reason why a human needs to be in the loop. And if you have a plagiarism checker with a click of a button, you can be assured that even though an AI tool will help you create it with your editing, that this is a genuinely unique piece of content. And so, you know, that’s another important point, I think, to bring forward.

Mike D’Agostino: [34:11]

So many implications. And who knows, maybe in the future, maybe not too long into the future, the whole concept of, you know, Google search results, or whatever your favorite search engine is, could be completely turned on its head. We know there are millions of pages on the web being produced every day, with a lot of that being kind of SEO bait type of content to generate inbound traffic. Now imagine with AI being able to produce 10-20-30 times the volume, at a click of a button that can have massive implications for search engine results, people trying to be in the system so to speak. I wouldn’t be surprised if at some point Google or the other search engines take action, ike I said, who knows completely revamp that search and discovery process.

Dan Verton: [35:06]

That’s what’s happened. Just in the last couple of days, I think OpenAI has been sued by some Hollywood folks. And I think one of them was the creator or the writer of Game of Thrones or something like that. And, their argument was that, they don’t know, but they suspect that ChatGPT is using their intellectual property in their large language model that other people are using. I heard that just the other day. And I said, does that mean that if I’ve been inspired by Hemingway, and influenced by Hemingway, that I need to rethink how I write because it might mimic Hemingway-style, or maybe I’ve used the same word. So there is this real slippery slope that we can go down here. And knowing what I know about how these tools turn out copying and looking at the copy, I have yet to find an example where either ChatGPT or Jasper or any other tool has turned out word for word, a part of a script, as an example. They’ve used it as inspiration, if you will, if you can use that term in relation to a technology. But I’ve yet to come across a completely copied copyrighted piece of material. So these are questions that these companies are going to be dealing with from Congress and from lawsuits. But hopefully, they can explain how the technology works in a way that it won’t hurt creativity, whatever happens.

Mike D’Agostino: [37:08]

Some of these things have been going on for a long time, especially in the music space, as far as copyrights go and plagiarizing other people’s songs. And I don’t know what the limitations are, if you use four, or five or six notes in the same sequence that can be considered plagiarism or something. But that’s been going on for a long time. This is just a completely new twist on that. So just one more talking point, I remember when we were discussing this that you had kind of talked about how, perhaps a bit of a way around that or a different way of thinking is providing like a large language model to individual organizations, so that they are training their own AI on their own content which kind of to a certain degree, circumvents a bit that you might be utilizing others’ content to develop your AI. So I know, we’re being a little speculative here and predictive. But where do you see this going, in the next – I don’t want to say five or 10 years – that’s like sci-fi, but let’s just say in the next two to three years?

Dan Verton: [38:28]

I think even that’s a little bit far out. I think it’s happening now. Large enterprises, like financial institutions, they already have AI tools in place like chatbots, and stuff, but they are now looking at, how do we take all of the customer data that we have, the interactions we have on an hourly basis with customers, and how do we corral that so that we can develop new tools based on AI? And so that’s all of their enterprise data. That’s exactly where this is heading. And like I said at the beginning about discussion, we can do that today. But where the general large language models come in, that are owned by open AI, Google and Microsoft or whatever, where they come in is where is the traditional part of content marketing, which is research. We’re not going to stop doing Google searches to create content. So all the better to have an AI tool that will help us create research and first drafts of parts of pieces and then go through it with a plagiarism checker and then go through it as a domain expert, and ensure what you need. But definitely this isn’t ability to train AI models on corporate data is happening today. And that part of this equation is going to have a profound impact on marketing organizations because it is what powers your ability to scale at unprecedented levels just for your organization. So marketing professionals need to understand how these tools work in society in general. I think we do need more openness into the language models and into the algorithms, because right now, just a handful of companies own this capability, because they’re the only ones that can afford the tens of thousands of processors. The last estimate was that OpenAI has trained on over a trillion words. So yeah, we need more openness. And it’s got to be much more democratic. People have to have access to the same capabilities, and then be able to leverage the data they’ve already collected. And like I said, these large enterprises, business enterprises have lots of data that they can leverage.

Mike D’Agostino: [41:21]

So many implications here. And I guess my main takeaway from all of this is, if you’re a cybersecurity marketer, get on the bus. If you haven’t yet, you need to create an account, start your certification journey, start experimenting, because very, very quickly, it’s going to be an expected tool that marketers are going to be using on a daily basis. So that’s the best you could do right now, is start experimenting, start putting some of these use cases to work, see if you can inject utilizing AI into your workflows, start leaning on others, and learning from others that have been experimenting. I was kind of joking about my LinkedIn feed. But in all seriousness, I pick up on quite a few tips and tricks, and once you kind of get into one, it starts to snowball you think of your own ideas and how you can put it to use and eventually we’ll get to those four-year college degrees were, I’m quite sure the education would be a bit more formal. But we have an opportunity. Cybersecurity marketers have an opportunity right now to be at the forefront, stay ahead of the curve, and hopefully, keep a little bit more optimism of your careers being intact in the future.

Dan Verton: [42:46]

I know there’s a lot of unknowns and makes it scary, but I think on par this is a positive thing for everybody.

Mike D’Agostino: [42:55]

Absolutely. Dan, the time is flown by, this was such a great conversation. Hopefully, we’ve gotten some cybersecurity marketers excited out there. Really appreciate you being on the show, and we’ll have to do it again.

Dan Verton: [43:08]

Hey, thanks, Mike. I appreciate it. It was fun.

Mike D’Agostino: [43:12]

We’re wrapping up another episode of Cybersecurity Marketing Unplugged. Again, I’m Mike D’Agostino, the host for today’s episode. Thanks for joining us.