Helping leaders motivate their people to a higher level of performance through strong human relations, team building, and goalagiving. This is the Seven Minute Leadership Podcast with your host Paul Fellovaliedo.
Hello everyone, and welcome to the Seven Minute Leadership Podcast. It's episode two fifty four. Today we're diving into a topic that every C suite leader needs to have on their radar, and that's AI data driven leadership in cybersecurity. In the digital age, the rapid advancements in artificial intelligence and machine learning are no longer optional tools for organizations. They are essential for optimizing operations, predicting market trends, and
delivering personalized customer experiences. But with this digital transformation comes a major responsibility safeguarding data and ensuring ethical AI use. To help us unpack this, I'm joined today by Josh Gelman, a technology and cyber security expert from Gelman Integrative Consulting in Pittsburgh. Josh has spent years helping businesses navigate the complex world of cybersecurity, digital ethics, and AI driven decision making. Josh, Welcome back to the show.
Thanks for having me.
Yeah, I think it's been over one hundred episodes. Ago was the last time you were here. Yeah, so thanks for making the time.
Yeah, it's been a long time yep.
So let's start with AI and data driven leadership. We hear a lot about AI revolutionizing industries, but from a leadership perspective, how should CEOs and executives be thinking about AI implementation?
Well, I think with AI, you need to approach it
like any other new technology. So a lot of people see something that's AI, this new shiny new toy that's AI, and they download the app or they want to sign up and immediately start using it without thinking about what the implications of that might be, not necessarily just from a security perspective, but from a data autonomy perspective, privacy perspective, all kinds of risk that might be introduced by just starting to use a new technology without really giving it
a lot of forethoughts. So I think having a strong strategic plan for how you're going to use AI before you just dive in head first and start using it is really important and really giving it some thought instead of just diving in headfirst and starting to use the new and shiny, greatest new toy.
Yeah that's a good point. So what do you think are some of the common mistakes that you see leaders make when integrating AI.
Well, I think again, just going directly into starting to use a technology without thinking about how it's going to help them meet business objectives. They just see that it's a shiny new toy, immediately want to start playing with it, and they're not really considering how this can be used to increase revenue, how it can be used to drive change within the organization. They just start using it without
giving a whole lot of thought. So I think having a better strategic plan on how you're you going to use AI is important. I think another thing that leaders where people in leadership positions don't really consider much with
AI is the information they're feeding it. So, for example, if you're sending it or if you're prompting it with your private corporate information like, for example, things that contain personally identifiable information, or if you work in healthcare, like protected health information or trade secrets or financial data, all of that data that you're submitting to the AI model could potentially be used to train that model and could
end up in the wrong hands. And that's especially true if you're using something that maybe isn't as reputable as some of the more prominent AI tools that are out there. People get concerned about AI tools like Deepseek, which is based out of China, and people just feeded all kinds of information and you're not really sure where that information's going, who's getting their hands on it, and what they can
do with it. So I think it all goes back to having that strategic plan about how you're going to use AI. And I think a lot of organizations also would benefit with by developing their own internal AI tools as opposed to using publicly available AI. That way, they have more control over where the data is going and how it's being used.
Yeah, and you touched on a couple important points. Then I'm going to circle back on here in a few minutes. But for leaders looking to maximize AI's potential, what are the key steps in building a true data driven organization that actually makes smarter decisions.
I think the key is the people that you have involved in the organization. You need to have buy in from all levels of management, from your senior leaders to the people that are on the you know, on the factory floor, or the people that are just involved in
the day to day operations of the organization. And then I think it helps to form It doesn't have to be a formal committee, but some sort of committee or working group or cask force where everybody can kind of get around and discuss the latest and greatest in AI trends and how those AI tools and trends can be used to benefit the organization in the best way possible as opposed to just having you know, we talk about like shadow technology, where people within the organizations start using
technology that isn't sanctioned by their leadership. Instead of having AI become a form of shadow technology, get a step ahead of it by involving people that you work with, developing these committees, these working groups and talking about the latest tools that are out there, how they might benefit you in your organization, and what the risks are. And it really needs to be part of your overall strategic plan but also your risk management plan and your organization.
Do we need to create the position shadow technologies are?
I don't think you need to create the position, but you certainly need to be cognizant of it. I mean, you need to be aware that people are probably using technologies that you haven't sanctioned yet. And we saw that a lot during COVID you know, we saw, for example, I always give the example with contact tracers and a lot of municipalities and states contracted with contact tracers and they didn't really have the systems in place to store
the data that they were collecting from patients. They started to use public Google Drive and didn't have that secure, so then anybody could access that information. And I think you run into the same risk with AI if you're not careful with how you're using it. You could have employees that are essentially turning into insider threats. Right, They're ex filtrating data from your organization into an AI model that is potentially used by competing companies or maybe even
foreign governments and could be used in nefarious ways. So you just really have to give it a lot of thought, and it has to have to involve a lot of strategy, a lot of forethought into actually using AI.
Yeah, so explain the importance of clean data, predictive analytics in AI, transparency in decision making.
So with clean data, I mean garbage in, garbage out is the old saying, right, So if you're not providing clean data to a model, then you can't expect the results that you get to be very accurate or very precise. So you need to make sure that whatever data you're giving the AI model is accurate. It's it's not tampered with. You know, its integrity is intact. Uh and it's uh
and and that goes with your prompts too. I mean, if you anyone who's played with chech ept, if if you learn about prompt engineering and how to prompt AI, there's good ways to do it and bad ways to do it. You can give an AI model, a large language model, a bad prompt and it's going to give you bad output. But you could give it a good prompt and it's going to give you good output. So garbage in, garbage out is what I really think. That all goes back.
To, Yeah, good h good point and special thanks to the guy in the room that for Christmas gave me a masterclass in prompt engineering. It's it's helped me a lot.
Yeah, and prompt engineering is really one of those things that you know, it's always evolving, especially with new models that are out there, because you know, every model is a little bit different. And you know, even when you
look at like consider a copilot versus open AI. Co pilot uses open AI models on the back end, but they have essentially an interface or a wrapper in front of the model that intercepts the prompt you give it and inserts some of it their own logic before it actually gets to the model, whereas with open AI it doesn't add that additional seasoning to it. So the way that you prompt the open AAI model might be completely
different than the way you prompt the copilot model. They're the same models on the back end, but the results you're getting are different. So prompt engineering is really important.
Yeah, it was really neat to go through that class, and I'd highly encourage everybody that's serious about wanting to learn AI to take some form of a prompt engineering course online. But now we've got to talk about the elephant in the room, Gelman in that cyber security in digital ethics. So I guess with every advancement in AI that we're seeing, all these new vulnerabilities are starting to emerge. So what are some of the biggest cybersecurity threats facing businesses today?
So when you think of emerging threats and the current threat landscape in let's just say the United States, because different countries certainly have different vulnerabilities and different threats that might impact them differently. But when you're thinking about the emerging threats in the United States, you have to think of not just how AI can be used for good,
but how AI can be used for bad. So there are AI models out there that are specifically designed to generate malware, and attackers can use those models to manipulate people through social engineering, phishing campaigns, things like that in ways that you would never think are possible, or ways
that never were possible before. Like, for example, you can essentially create an AI avatar of your CEO and have that CEO avatar produce a message, a video message that says they want you to send a payment in a certain amount to this account that they give you, and it looks like it's coming from that particular CEO. To the untrained eye, it looks real, has human like features.
And even if you're not using video audio, I mean, that's been a big one too, where you will clone of somebody's voice and use AI to make it sound like a message came from them. Then you could start a teams meeting, say with the AI clone voice model that your camera doesn't work and it sounds just like the message is coming from that person. There are specific AI models even that are just for malware worm GPT was one of the original ones. There's AI models that
are trained on the dark Web. So using AI for evil, I think is something that has to be considered as one of the top emerging threats right now.
Yeah, no, because nobody in the world would ever use AI for evil, right yeah.
I mean there's a model called dark Bird and it's just trained completely on beta from the dark Web, and you can use that model to do a lot of a lot of bad things. Of course, we don't really I guess want to go down that road because we don't want to turn this into a training course for for attackers. But you know, AI, you could do some scary things with it, and I even think of like
if you have ever heard of the grandparent scam. No, so that's like when somebody gets a text message from they claim it's the attacker, will claim it's somebody's grandchild or maybe it's a loved one, and they say that you know, they got in trouble and they're gonna they're gonna need money in order to get that trouble, and you've got to send this money to this account or you got to send bitcoin to this particular account, and
now you know, people kind of caught onto that. But now with AI, you could go on to somebody's YouTube page, a Facebook page where they have videos, clone their voice, So clone the voice of the person that you want to emulate, and actually send a recording or a voicemail to somebody and make it sound like you know that person is actually in trouble and need help, and it's it's indistinguishable from the actual person.
Yeah. So so that brings me to AI ethics, right, because we've seen major concerns around bias and AI, like we've talked about with data privacy and even regulatory crackdown. So how can CEOs navigate this landscape responsibly?
It's it's definitely a challenge. I think training is an
important element to it. I was just watching a LinkedIn learn course actually the other day on ethics and AI, and it was really providing a lot of useful insights on how to use AI ethically and some of the ethical challenges that you may not even consider with AI, like how the model was trained and the environmental impacts behind it, and the fact that you know you might be getting or the AI model might have been using copyrighted data, so you might be the output might be
copyrighted and you're not aware of it. So training and awareness is certainly, I think the top thing that leaders can do to kind of address that concern. But beyond that, I mean, it's just really staying aware of all of the emerging threats that are out there.
Here's what I think scares people or turns them off to AI, especially in the business world, because I saw this article just this morning that said this, and it was Elon Musk, and it was in one of the AI news articles that I subscribe to. Or they're talking
about his new what is it, GROC three? Yeah, okay, and he was quoted saying GROCK three is more capable than GROC two and is a maximally truth seeking AI as it's been trained with ten times more computing power using a data set that includes filings from court cases. So people that aren't following the AI surge may not be aware of what data is actually being put into
in the AI models are being trained with. And I think that when regular people that aren't really plugged into AI read an article that says, oh my god, this AI, it's been trained with actual court cases, they may not be aware of what information is being truly dumped into these large language models. So can you talk a little bit about that kind of stuff.
Yeah, So there's two things that really come to mind with that. First is bias. AI models all have some sort of algorithmic bias based on the data that they've been trained with. So if you train a model on specific court cases that are about a topic that is something you're particularly interested in, something that's in your favor, something that is aligned with your political beliefs, then the results that you're going to get are also going to
be aligned with those political beliefs. So that goes back to garbage in, garbage out. So biases are are a big thing that can happen with with generative AI. The other thing is hallucinations.
AI.
AI can make shut up, I can say shit right, A AI can make shit up. That's that's hallucinations is what they is. That what that's referred to in the AI world. And it may it may sound correct when whenever you get it out and get the output from the model, it may sound like, oh, this is legitimate,
this is real, and it's not. And that's the other thing you have to be careful for or careful and whenever you're using especially some of these newer AI models, when they don't have the training data to really answer the prompt that you're providing it, they will make it up and it will sound accurate. And we saw that a lot with chat GPTU in its infancy.
Yeah, good points, Josh, Well, I got to tell you this has been a great conversation before we wrap up. Do you have one more little piece of advice you'd give to any CEO or executive out there who's looking to plug into AI for their business.
Yeah, I would say maybe number one, always keep training, keep learning, stay on top of things, because the stuff that's out there today is not the same stuff that's going to be out there tomorrow. I would also say, be transparent about your use of AI. If you're using AI for something, you should make it clear to the people that are the consumers of whatever you're generating that you did use AI because there could be other implications that come out down the road, especially with copyrights and
privacy and that type of thing. And then always make sure that you're incorporating AI into your overall risk management strategy. Of your organization. If you're not, you're leaving yourself open and vulnerabilities to lawsuits to possible attacks. So it's really important to consider AI in the context of risk management.
Well again, Josh, thanks for coming back. Do me a favor. Let's not let it be another one hundred or so episodes until you come back and do another one. Sure fair deal. Yeah sounds good to me and for everyone listening. This has been the seven Minute Leadership Podcast and we'll see you next time.
For more Paul Fell of Alito Podcasts, visit paulfellowalito dot com.