Speaker 1: Helping leaders motivate their people to a higher level of
Speaker 1: performance through strong human relations, team building, and goalagiving. This
Speaker 1: is the Seven Minute Leadership Podcast with your host Paul Fellovaliedo.
Speaker 2: Hello everyone, and welcome to the Seven Minute Leadership Podcast.
Speaker 2: It's episode two fifty four. Today we're diving into a
Speaker 2: topic that every C suite leader needs to have on
Speaker 2: their radar, and that's AI data driven leadership in cybersecurity.
Speaker 2: In the digital age, the rapid advancements in artificial intelligence
Speaker 2: and machine learning are no longer optional tools for organizations.
Speaker 2: They are essential for optimizing operations, predicting market trends, and
Speaker 2: delivering personalized customer experiences. But with this digital transformation comes
Speaker 2: a major responsibility safeguarding data and ensuring ethical AI use.
Speaker 2: To help us unpack this, I'm joined today by Josh Gelman,
Speaker 2: a technology and cyber security expert from Gelman Integrative Consulting
Speaker 2: in Pittsburgh. Josh has spent years helping businesses navigate the
Speaker 2: complex world of cybersecurity, digital ethics, and AI driven decision making. Josh,
Speaker 2: Welcome back to the show.
Speaker 3: Thanks for having me.
Speaker 2: Yeah, I think it's been over one hundred episodes. Ago
Speaker 2: was the last time you were here. Yeah, so thanks
Speaker 2: for making the time.
Speaker 3: Yeah, it's been a long time yep.
Speaker 2: So let's start with AI and data driven leadership. We
Speaker 2: hear a lot about AI revolutionizing industries, but from a
Speaker 2: leadership perspective, how should CEOs and executives be thinking about
Speaker 2: AI implementation?
Speaker 3: Well, I think with AI, you need to approach it
Speaker 3: like any other new technology. So a lot of people
Speaker 3: see something that's AI, this new shiny new toy that's AI,
Speaker 3: and they download the app or they want to sign
Speaker 3: up and immediately start using it without thinking about what
Speaker 3: the implications of that might be, not necessarily just from
Speaker 3: a security perspective, but from a data autonomy perspective, privacy perspective,
Speaker 3: all kinds of risk that might be introduced by just
Speaker 3: starting to use a new technology without really giving it
Speaker 3: a lot of forethoughts. So I think having a strong
Speaker 3: strategic plan for how you're going to use AI before
Speaker 3: you just dive in head first and start using it
Speaker 3: is really important and really giving it some thought instead
Speaker 3: of just diving in headfirst and starting to use the
Speaker 3: new and shiny, greatest new toy.
Speaker 2: Yeah that's a good point. So what do you think
Speaker 2: are some of the common mistakes that you see leaders
Speaker 2: make when integrating AI.
Speaker 3: Well, I think again, just going directly into starting to
Speaker 3: use a technology without thinking about how it's going to
Speaker 3: help them meet business objectives. They just see that it's
Speaker 3: a shiny new toy, immediately want to start playing with it,
Speaker 3: and they're not really considering how this can be used
Speaker 3: to increase revenue, how it can be used to drive
Speaker 3: change within the organization. They just start using it without
Speaker 3: giving a whole lot of thought. So I think having
Speaker 3: a better strategic plan on how you're you going to
Speaker 3: use AI is important. I think another thing that leaders
Speaker 3: where people in leadership positions don't really consider much with
Speaker 3: AI is the information they're feeding it. So, for example,
Speaker 3: if you're sending it or if you're prompting it with
Speaker 3: your private corporate information like, for example, things that contain
Speaker 3: personally identifiable information, or if you work in healthcare, like
Speaker 3: protected health information or trade secrets or financial data, all
Speaker 3: of that data that you're submitting to the AI model
Speaker 3: could potentially be used to train that model and could
Speaker 3: end up in the wrong hands. And that's especially true
Speaker 3: if you're using something that maybe isn't as reputable as
Speaker 3: some of the more prominent AI tools that are out there.
Speaker 3: People get concerned about AI tools like Deepseek, which is
Speaker 3: based out of China, and people just feeded all kinds
Speaker 3: of information and you're not really sure where that information's going,
Speaker 3: who's getting their hands on it, and what they can
Speaker 3: do with it. So I think it all goes back
Speaker 3: to having that strategic plan about how you're going to
Speaker 3: use AI. And I think a lot of organizations also
Speaker 3: would benefit with by developing their own internal AI tools
Speaker 3: as opposed to using publicly available AI. That way, they
Speaker 3: have more control over where the data is going and
Speaker 3: how it's being used.
Speaker 2: Yeah, and you touched on a couple important points. Then
Speaker 2: I'm going to circle back on here in a few minutes.
Speaker 2: But for leaders looking to maximize AI's potential, what are
Speaker 2: the key steps in building a true data driven organization
Speaker 2: that actually makes smarter decisions.
Speaker 3: I think the key is the people that you have
Speaker 3: involved in the organization. You need to have buy in
Speaker 3: from all levels of management, from your senior leaders to
Speaker 3: the people that are on the you know, on the
Speaker 3: factory floor, or the people that are just involved in
Speaker 3: the day to day operations of the organization. And then
Speaker 3: I think it helps to form It doesn't have to
Speaker 3: be a formal committee, but some sort of committee or
Speaker 3: working group or cask force where everybody can kind of
Speaker 3: get around and discuss the latest and greatest in AI
Speaker 3: trends and how those AI tools and trends can be
Speaker 3: used to benefit the organization in the best way possible
Speaker 3: as opposed to just having you know, we talk about
Speaker 3: like shadow technology, where people within the organizations start using
Speaker 3: technology that isn't sanctioned by their leadership. Instead of having
Speaker 3: AI become a form of shadow technology, get a step
Speaker 3: ahead of it by involving people that you work with,
Speaker 3: developing these committees, these working groups and talking about the
Speaker 3: latest tools that are out there, how they might benefit
Speaker 3: you in your organization, and what the risks are. And
Speaker 3: it really needs to be part of your overall strategic
Speaker 3: plan but also your risk management plan and your organization.
Speaker 2: Do we need to create the position shadow technologies are?
Speaker 3: I don't think you need to create the position, but
Speaker 3: you certainly need to be cognizant of it. I mean,
Speaker 3: you need to be aware that people are probably using
Speaker 3: technologies that you haven't sanctioned yet. And we saw that
Speaker 3: a lot during COVID you know, we saw, for example,
Speaker 3: I always give the example with contact tracers and a
Speaker 3: lot of municipalities and states contracted with contact tracers and
Speaker 3: they didn't really have the systems in place to store
Speaker 3: the data that they were collecting from patients. They started
Speaker 3: to use public Google Drive and didn't have that secure,
Speaker 3: so then anybody could access that information. And I think
Speaker 3: you run into the same risk with AI if you're
Speaker 3: not careful with how you're using it. You could have
Speaker 3: employees that are essentially turning into insider threats. Right, They're
Speaker 3: ex filtrating data from your organization into an AI model
Speaker 3: that is potentially used by competing companies or maybe even
Speaker 3: foreign governments and could be used in nefarious ways. So
Speaker 3: you just really have to give it a lot of thought,
Speaker 3: and it has to have to involve a lot of strategy,
Speaker 3: a lot of forethought into actually using AI.
Speaker 2: Yeah, so explain the importance of clean data, predictive analytics
Speaker 2: in AI, transparency in decision making.
Speaker 3: So with clean data, I mean garbage in, garbage out
Speaker 3: is the old saying, right, So if you're not providing
Speaker 3: clean data to a model, then you can't expect the
Speaker 3: results that you get to be very accurate or very precise.
Speaker 3: So you need to make sure that whatever data you're
Speaker 3: giving the AI model is accurate. It's it's not tampered with.
Speaker 3: You know, its integrity is intact. Uh and it's uh
Speaker 3: and and that goes with your prompts too. I mean,
Speaker 3: if you anyone who's played with chech ept, if if
Speaker 3: you learn about prompt engineering and how to prompt AI,
Speaker 3: there's good ways to do it and bad ways to
Speaker 3: do it. You can give an AI model, a large
Speaker 3: language model, a bad prompt and it's going to give
Speaker 3: you bad output. But you could give it a good
Speaker 3: prompt and it's going to give you good output. So
Speaker 3: garbage in, garbage out is what I really think. That
Speaker 3: all goes back.
Speaker 2: To, Yeah, good h good point and special thanks to
Speaker 2: the guy in the room that for Christmas gave me
Speaker 2: a masterclass in prompt engineering. It's it's helped me a lot.
Speaker 3: Yeah, and prompt engineering is really one of those things
Speaker 3: that you know, it's always evolving, especially with new models
Speaker 3: that are out there, because you know, every model is
Speaker 3: a little bit different. And you know, even when you
Speaker 3: look at like consider a copilot versus open AI. Co
Speaker 3: pilot uses open AI models on the back end, but
Speaker 3: they have essentially an interface or a wrapper in front
Speaker 3: of the model that intercepts the prompt you give it
Speaker 3: and inserts some of it their own logic before it
Speaker 3: actually gets to the model, whereas with open AI it
Speaker 3: doesn't add that additional seasoning to it. So the way
Speaker 3: that you prompt the open AAI model might be completely
Speaker 3: different than the way you prompt the copilot model. They're
Speaker 3: the same models on the back end, but the results
Speaker 3: you're getting are different. So prompt engineering is really important.
Speaker 2: Yeah, it was really neat to go through that class,
Speaker 2: and I'd highly encourage everybody that's serious about wanting to
Speaker 2: learn AI to take some form of a prompt engineering
Speaker 2: course online. But now we've got to talk about the
Speaker 2: elephant in the room, Gelman in that cyber security in
Speaker 2: digital ethics. So I guess with every advancement in AI
Speaker 2: that we're seeing, all these new vulnerabilities are starting to emerge.
Speaker 2: So what are some of the biggest cybersecurity threats facing
Speaker 2: businesses today?
Speaker 3: So when you think of emerging threats and the current
Speaker 3: threat landscape in let's just say the United States, because
Speaker 3: different countries certainly have different vulnerabilities and different threats that
Speaker 3: might impact them differently. But when you're thinking about the
Speaker 3: emerging threats in the United States, you have to think
Speaker 3: of not just how AI can be used for good,
Speaker 3: but how AI can be used for bad. So there
Speaker 3: are AI models out there that are specifically designed to
Speaker 3: generate malware, and attackers can use those models to manipulate
Speaker 3: people through social engineering, phishing campaigns, things like that in
Speaker 3: ways that you would never think are possible, or ways
Speaker 3: that never were possible before. Like, for example, you can
Speaker 3: essentially create an AI avatar of your CEO and have
Speaker 3: that CEO avatar produce a message, a video message that
Speaker 3: says they want you to send a payment in a
Speaker 3: certain amount to this account that they give you, and
Speaker 3: it looks like it's coming from that particular CEO. To
Speaker 3: the untrained eye, it looks real, has human like features.
Speaker 3: And even if you're not using video audio, I mean,
Speaker 3: that's been a big one too, where you will clone
Speaker 3: of somebody's voice and use AI to make it sound
Speaker 3: like a message came from them. Then you could start
Speaker 3: a teams meeting, say with the AI clone voice model
Speaker 3: that your camera doesn't work and it sounds just like
Speaker 3: the message is coming from that person. There are specific
Speaker 3: AI models even that are just for malware worm GPT
Speaker 3: was one of the original ones. There's AI models that
Speaker 3: are trained on the dark Web. So using AI for evil,
Speaker 3: I think is something that has to be considered as
Speaker 3: one of the top emerging threats right now.
Speaker 2: Yeah, no, because nobody in the world would ever use
Speaker 2: AI for evil, right yeah.
Speaker 3: I mean there's a model called dark Bird and it's
Speaker 3: just trained completely on beta from the dark Web, and
Speaker 3: you can use that model to do a lot of
Speaker 3: a lot of bad things. Of course, we don't really
Speaker 3: I guess want to go down that road because we
Speaker 3: don't want to turn this into a training course for
Speaker 3: for attackers. But you know, AI, you could do some
Speaker 3: scary things with it, and I even think of like
Speaker 3: if you have ever heard of the grandparent scam. No,
Speaker 3: so that's like when somebody gets a text message from
Speaker 3: they claim it's the attacker, will claim it's somebody's grandchild
Speaker 3: or maybe it's a loved one, and they say that
Speaker 3: you know, they got in trouble and they're gonna they're
Speaker 3: gonna need money in order to get that trouble, and
Speaker 3: you've got to send this money to this account or
Speaker 3: you got to send bitcoin to this particular account, and
Speaker 3: now you know, people kind of caught onto that. But
Speaker 3: now with AI, you could go on to somebody's YouTube page,
Speaker 3: a Facebook page where they have videos, clone their voice,
Speaker 3: So clone the voice of the person that you want
Speaker 3: to emulate, and actually send a recording or a voicemail
Speaker 3: to somebody and make it sound like you know that
Speaker 3: person is actually in trouble and need help, and it's
Speaker 3: it's indistinguishable from the actual person.
Speaker 2: Yeah. So so that brings me to AI ethics, right,
Speaker 2: because we've seen major concerns around bias and AI, like
Speaker 2: we've talked about with data privacy and even regulatory crackdown.
Speaker 2: So how can CEOs navigate this landscape responsibly?
Speaker 3: It's it's definitely a challenge. I think training is an
Speaker 3: important element to it. I was just watching a LinkedIn
Speaker 3: learn course actually the other day on ethics and AI,
Speaker 3: and it was really providing a lot of useful insights
Speaker 3: on how to use AI ethically and some of the
Speaker 3: ethical challenges that you may not even consider with AI,
Speaker 3: like how the model was trained and the environmental impacts
Speaker 3: behind it, and the fact that you know you might
Speaker 3: be getting or the AI model might have been using
Speaker 3: copyrighted data, so you might be the output might be
Speaker 3: copyrighted and you're not aware of it. So training and
Speaker 3: awareness is certainly, I think the top thing that leaders
Speaker 3: can do to kind of address that concern. But beyond that,
Speaker 3: I mean, it's just really staying aware of all of
Speaker 3: the emerging threats that are out there.
Speaker 2: Here's what I think scares people or turns them off
Speaker 2: to AI, especially in the business world, because I saw
Speaker 2: this article just this morning that said this, and it
Speaker 2: was Elon Musk, and it was in one of the
Speaker 2: AI news articles that I subscribe to. Or they're talking
Speaker 2: about his new what is it, GROC three? Yeah, okay,
Speaker 2: and he was quoted saying GROCK three is more capable
Speaker 2: than GROC two and is a maximally truth seeking AI
Speaker 2: as it's been trained with ten times more computing power
Speaker 2: using a data set that includes filings from court cases.
Speaker 2: So people that aren't following the AI surge may not
Speaker 2: be aware of what data is actually being put into
Speaker 2: in the AI models are being trained with. And I
Speaker 2: think that when regular people that aren't really plugged into
Speaker 2: AI read an article that says, oh my god, this AI,
Speaker 2: it's been trained with actual court cases, they may not
Speaker 2: be aware of what information is being truly dumped into
Speaker 2: these large language models. So can you talk a little
Speaker 2: bit about that kind of stuff.
Speaker 3: Yeah, So there's two things that really come to mind
Speaker 3: with that. First is bias. AI models all have some
Speaker 3: sort of algorithmic bias based on the data that they've
Speaker 3: been trained with. So if you train a model on
Speaker 3: specific court cases that are about a topic that is
Speaker 3: something you're particularly interested in, something that's in your favor,
Speaker 3: something that is aligned with your political beliefs, then the
Speaker 3: results that you're going to get are also going to
Speaker 3: be aligned with those political beliefs. So that goes back
Speaker 3: to garbage in, garbage out. So biases are are a
Speaker 3: big thing that can happen with with generative AI. The
Speaker 3: other thing is hallucinations.
Speaker 2: AI.
Speaker 3: AI can make shut up, I can say shit right,
Speaker 3: A AI can make shit up. That's that's hallucinations is
Speaker 3: what they is. That what that's referred to in the
Speaker 3: AI world. And it may it may sound correct when
Speaker 3: whenever you get it out and get the output from
Speaker 3: the model, it may sound like, oh, this is legitimate,
Speaker 3: this is real, and it's not. And that's the other
Speaker 3: thing you have to be careful for or careful and
Speaker 3: whenever you're using especially some of these newer AI models,
Speaker 3: when they don't have the training data to really answer
Speaker 3: the prompt that you're providing it, they will make it
Speaker 3: up and it will sound accurate. And we saw that
Speaker 3: a lot with chat GPTU in its infancy.
Speaker 2: Yeah, good points, Josh, Well, I got to tell you
Speaker 2: this has been a great conversation before we wrap up.
Speaker 2: Do you have one more little piece of advice you'd
Speaker 2: give to any CEO or executive out there who's looking
Speaker 2: to plug into AI for their business.
Speaker 3: Yeah, I would say maybe number one, always keep training,
Speaker 3: keep learning, stay on top of things, because the stuff
Speaker 3: that's out there today is not the same stuff that's
Speaker 3: going to be out there tomorrow. I would also say,
Speaker 3: be transparent about your use of AI. If you're using
Speaker 3: AI for something, you should make it clear to the
Speaker 3: people that are the consumers of whatever you're generating that
Speaker 3: you did use AI because there could be other implications
Speaker 3: that come out down the road, especially with copyrights and
Speaker 3: privacy and that type of thing. And then always make
Speaker 3: sure that you're incorporating AI into your overall risk management strategy.
Speaker 3: Of your organization. If you're not, you're leaving yourself open
Speaker 3: and vulnerabilities to lawsuits to possible attacks. So it's really
Speaker 3: important to consider AI in the context of risk management.
Speaker 2: Well again, Josh, thanks for coming back. Do me a favor.
Speaker 2: Let's not let it be another one hundred or so
Speaker 2: episodes until you come back and do another one. Sure
Speaker 2: fair deal. Yeah sounds good to me and for everyone listening.
Speaker 2: This has been the seven Minute Leadership Podcast and we'll
Speaker 2: see you next time.
Speaker 1: For more Paul Fell of Alito Podcasts, visit paulfellowalito dot com.