Tony opens by walking through what it took to become Gilbert's first ever CISO, brought in specifically to look at cybersecurity strategically across the whole organization rather than department by department. He describes his first move as auditing the town's existing security spend and discovering money going toward tools that weren't being used properly, a decision that shrank his budget dramatically but let his team of three operate with the impact of a much larger department through carefully chosen strategic partnerships.
The conversation moves into how Gilbert approached artificial intelligence years before most organizations took it seriously, with Tony's office starting formal discussions about governance, guardrails, and data access roughly four years ago. That early groundwork led to the creation of a chief artificial intelligence officer position, a decision Tony says changed how the organization operates but also introduced new concerns, particularly around agentic AI, which he describes as opening a Pandora's box that cannot be closed again. He is candid that generative AI has not yet earned his full trust for frontline security work, arguing that machine learning products built specifically for behavioral detection remain far more reliable at catching abnormal activity like the kind that led to the MGM breach.
The back half of the episode centers on where Tony draws firm boundaries around AI's role. He explains why he will not let artificial intelligence serve as the sole peer reviewer on any production change, citing liability and the need for a human stakeholder with real accountability, though he is open to AI catching mistakes as one voice among several. He also shares a personal system, inspired by a story about Abraham Lincoln's unsent letters, for using a cooling off period and AI assistance to temper emotionally charged communications before they go out, a practice he says has become part of how he leads through moments of crisis.
Resources mentioned in this episode
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The Age of Human Judgment, Not the Age of AI, With Benny Zhang - Ep 235
Why Patch Management Is No Longer Frontline Defense with Brett Price - Ep 234
Generals in the Command Post: AI Security and Human Identity with Erik Miller - Ep 233
Guest: Tony Bryson
CISO of the Town of Gilbert, Arizona
Matthew Connor: Matthew Connor here, host of the Cyber Business Podcast. Today we're joined by Tony Bryson, CISO of the Town of Gilbert, Arizona. Tony, welcome to the show.
Tony Bryson: Thanks, Matt. Pleasure to be here.
Matthew Connor: Pleasure to have you. Before we get too far in, a quick word from our sponsors.
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Matthew Connor: And now, back to our show. Tony, for those who aren't familiar, can you tell us about your role as CISO of Gilbert?
Tony Bryson: Sure. Let's see, I'm the first CISO of the Town of Gilbert. The Town of Gilbert has been around for, I think we just celebrated our centennial about five or six years ago, so we've been around a while. We've seen explosive growth, we were probably around fifteen thousand residents at the turn of the century, and now we service about three hundred thousand residents. So we've seen explosive growth, and we're now the fourth-largest municipality in the state of Arizona. We service all aspects of our community: public safety, emergency services, critical infrastructure, water, wastewater, traffic operations, those types of things. The organization was growing, and they decided they needed to bring somebody in to really look at cybersecurity seriously and take it on strategically, more so than maybe they had in the past. The town was really good at going out and procuring solutions that could solve problems, but a lot of times those problems were focused on a specific division more than on the organization as a whole. So they wanted somebody who would come in with that organizational point of view, thinking about security challenges holistically rather than strictly at a departmental level. The previous chief technology officer, who I'd worked with previously at Mesa Community College, reached out and said, hey, we've got this position open, I think you might be a really good fit, would you mind going through the process? And I said, sure, let's do it. So I went through the interview process, got through that, and they decided to make me their first chief information security officer. That's been a bit of a challenge, been grinding away at it for six years, but we've got a lot of foundational pieces in play, all of our table-stakes documents in play, a really good foundation as far as our cybersecurity strategy, a lot of our technical solutions in play. Now it's a matter of maturing things and getting to the point where we can take the next logical step with our cybersecurity program.
Matthew Connor: Well, it's always challenging to be the first, right? It's interesting, because when you look at a town like Gilbert, in a lot of ways it's similar to that smaller organization or enterprise that's just as large a target as the bigger municipalities and larger organizations, in terms of how much threat they attract, and yet they tend not to have quite the budget and resources of their larger counterparts. So it creates a real challenge, doing more with less. Is it a matter of getting the basics right? Is that where you start? I mean, you've had six years now, but is that the strategy, you start with the basics, get that laid out, and build from there? How do you even go about it?
Tony Bryson: That was my approach when I came in. I said, okay, let's build a good, solid foundation that everything else will build upon. You can't build a house until you have the concrete poured, you can't erect a stick frame until that concrete has cured and is ready to support the weight of the structure you're about to put on it. So that was my approach when I came into the Town of Gilbert. Let's get our policies in place, let's make sure we look at our technology stack and understand it completely, look at our security stack, make sure we have the right tools. And it's ironic that you should mention budget, because one of the things I did when I came in was look at the budget and make the determination that we were spending a lot of money on tools that weren't being used properly. So I went in and, with purpose, reduced that spend dramatically. And I was so successful at that that I winnowed down our spending to the point where my budget is extremely small, probably not large enough for some of the things I'm challenged to do. But we do more with less, and we've made sure we've engaged with strategic partners who act as force multipliers for our limited staff, really leaning into managing our budget so that wherever we spend money, we're going to get a very big bang for the buck. We're going to get that force multiplier for my staff, so that even though I have a department of three, we can punch at the weight of a department of fifteen or twenty. We make sure our investments are done wisely, that we engage with the right business partners, ones that share the same goals as us, that are equally invested in our success, that are on their own trying to help push the town to become truly the city of the future.
Matthew Connor: Well, I think that's really where you've got to start, right? And I think that's a really great way of going about it, you reduce as much waste as possible and focus on the fundamentals. I can't help but wonder, when you're an army of three and you're now leveraging strategic partners, that makes perfect sense, that's great. Now you're punching like you're an army of, did we say fifteen to twenty?
Tony Bryson: Fifteen to twenty.
Matthew Connor: So I'm curious, where do you stand on AI, in terms of how it can help you? Because I think that's where the world is going, there's a lot to consider, there's so much to discuss and think about when you start talking about leveraging AI. So I'll leave this very broad and open for you, we can go whichever direction you want. But where do you stand on this? What's the approach you take, or the perspective you have, being an army of three? Where do you stand on this?
Tony Bryson: Artificial intelligence is the new double-edged sword. It can be very beneficial to you, but it can also do damage to you if you're not wielding that sword appropriately, if you're not taking care of what that second edge looks like. Our artificial intelligence strategy was actually born out of a discussion started by my office. We started talking about artificial intelligence about four years ago, so we were way ahead of the curve. We've seen a lot of hype come out about what artificial intelligence is going to do to the industry, how it's going to change everything we do, and we started looking at that and having discussions from the IT leadership perspective, wanting to make sure we understood everything that could potentially happen to us with the introduction of artificial intelligence, recognizing that artificial intelligence has been around for decades and has already been baked into a lot of the solutions we use. So it was this new approach to artificial intelligence that we had to try to get ahead of, and I think we did a really good job. We leaned heavily into building out appropriate governance structures, getting guardrails set up around artificial intelligence: how we would use it, what data we would give it access to. We were very thoughtful as we approached the topic, and out of those discussions we got to the point where we decided we needed a chief artificial intelligence officer. So we went out and brought in an individual who was responsible for artificial intelligence adoption and innovation, and that has dramatically changed how the organization does a lot of things. From the security perspective, it keeps me awake at night, especially with the adoption of agentic AI. It's opened up, I think, Pandora's box, and once that's open, we're not closing it again. So now we're kind of scrambling, trying to get ahead of some of the things agentic AI brings to the table that we hadn't originally thought of when we posed the question of what AI looks like in the future. Agentic AI is a bit of a game changer, it's kind of changed how we do a lot of things. It's great, we use it as an augmentation to our staff more than something to replace staff, and we try to make sure all of our staff recognize that artificial intelligence is not here to take your job, it's here to help augment your job and make you perform better. Some of those tasks that may have been repetitive and boring to do, we can now task artificial intelligence to do, and it can take that on while allowing you, the human being, to provide greater value to the larger organization by using the massive brain cells between your ears. So that's where we think artificial intelligence is really helping our organization, making our staff, our people, better and more capable of doing their jobs.
Matthew Connor: I couldn't agree more, I think it's incredible how it really does kind of supercharge the worker when it's utilized properly. And we've come so far in under four years since ChatGPT came out, generative AI's growth has been so rapid, its improvement has been so rapid. Where we are today versus where we were even six months ago is dramatic. And I think groups, companies like, surprisingly, Grok from xAI, they're doing an incredible job. I don't know if you've had a chance to look at Grok, but interestingly enough, they're doing this incredible job of structuring the security into it and limiting its scope and access, it's baked right in, brilliantly done. Where it all runs, your data stays on one small Linux box in Silicon Valley, and you have complete access to it, and it's really interesting how they're now putting in guardrails to make it a lot easier, and you can see the evolution has come a long way. It's evolved greatly since the early days, especially compared to some of these more open-ended agentic tools we've seen, where it's really hard for people to consider all the ramifications, the guardrails, and everything, when you're just kind of running, running, running, and then suddenly you've run into a huge problem. But you see that now in Grok, and you're seeing it in Claude, they're doing a great job improving, building from that place of, we need guardrails, we need to make sure we're not just giving the AI access to everything. So I really like the direction things are going. Are we there yet? No, but compared to where we were a year or two ago, or three years ago when things really started, it's incredible, the improvement. But I do think ultimately it becomes one of those things where it's so good at helping alleviate those challenging, monotonous jobs. I mean, you look at the IT side, we're both old enough to remember the days before Google, you had a tech problem, you were on your own, buddy, it was you and the school of hard knocks and any manual you could find, and if you were coding and ran into a problem, it was tough. Then Google comes along and you can find answers so much faster, and it's like, oh, this is so great. And now with AI, it's so much more pleasant to get such fast responses and help with things, it's so accurate. It's been fun to see that progression from where we were a few years ago to now, it gives me great hope for the future. Now, it's also interesting, and that's on the user side, operationally. I think we really do have to, as leaders, stay focused on governance and help ensure we're safeguarding the organization's data properly.
Tony Bryson: Get it.
Matthew Connor: Now, where I think it gets really interesting, though, is on the security side. I think this is where, I don't know, I don't think it gets talked about enough in general, AI when it comes to security. And to your earlier point, it's been used for decades, true, and you take a look at things, and I'm a big fan of Darktrace, where they've been using machine learning for the last ten or fifteen years to do exactly this, and you see AI, that machine learning version of AI, doing really cool stuff, just like you'd want. And I think that's where it becomes really powerful. I don't know how much generative AI really helps currently when it comes to security on the defensive side of things. We've seen how it works for the bad actors coming at us, and other than manufacturers being able to leverage it to start finding vulnerabilities faster, I'm not sure there are that many products out there where, and I'm going to take a lot of heat for this, so hopefully it's just you and me, but the reality is I'm not seeing it just yet. I don't think we're at the point where generative AI plays a huge role in security just yet. But I do think machine learning is one of those things that gives you the power to have your army of three be more like an army of fifty-plus, because you see it in products like Abnormal Security and Darktrace, where it's so much more efficient than traditional tools. All right, that was a lot, but I'm curious your take on that.
Tony Bryson: Well, I think artificial intelligence is an extension of those who programmed it, and artificial intelligence is very much like the humans who programmed it. Human beings are very good at pattern recognition, we see patterns that exist within data, within the environment we live in, and we adapt based on that pattern recognition. Artificial intelligence is very much the same way, it's fantastic at identifying patterns that exist within your data, within your processes, and figuring out the best way of identifying this, maximizing the effect of this, minimizing the negative effect of it. Where it is weak, and it's the same thing with human beings, is behavioral recognition. I think it's better than human beings in a lot of regards there, but it's still not quite up to snuff where I can trust it to observe what's going on the wire, recognize the behaviors going on the wire, and alert me, hey, this is a bad actor trying to break into this particular system. I don't have my full one hundred percent faith in artificial intelligence there yet. Can I add one more thing? Another thing about artificial intelligence we have to recognize is that it does like to hallucinate, and it's one of the reasons why at the Town of Gilbert we always tell people, trust but verify, recognize that artificial intelligence is going to generate some good work, but let's make sure it's citing its work so we can go back and validate that the citations it's providing for where it got its information are accurate and valid. So when it comes to pattern recognition for behaviors, I think this is where machine learning is far superior to generative AI currently. And I think that's where when you see it implemented in, let's say, a network product, or in an MDR, it gets easy. Let's take MGM, for instance. The MGM breach is a great example of an organization with all the resources, plenty of money, that invests heavily in security, well-trained people all the way through their IT. And yet this breach happened because of social engineering targeting the IT department. The interesting part is, you can't blame the victim, when a very advanced actor comes and beats your human senses, so be it, we can all fall victim to a really good con. However, this is where I think machine learning really shines, in that within moments of that password reset, that new IT manager was doing stuff that the seasoned vets wouldn't be doing. They were moving so quickly, that's so abnormal, so far out of the realm of normal. And on top of that, machine learning, by its design, is trying to get an idea of, okay, let me see how you normally write email, let me see how you normally operate, and when it compares that to any other person, it's immediately like, this is crazy. So MGM would have been easily stopped with, I say that, but I think it's pretty safe to say machine learning would have prevented that breach, and breaches like it, because day one of a new admin, that's not day-one behavior of any user, and that's abnormal, and machine learning is great at catching that. So I think there's a real place for that, and I think the challenge is, with so many products out there, it's not necessarily about the machine learning, but where you move your dollars. And I think right now it's in those machine-learning versions of AI that do things like that. Because I just, I love generative AI, and you know, I beat it up pretty good when I said it wasn't there on security. But when you look at what companies like SentinelOne and CrowdStrike are doing with generative AI, it's great that they can take a look at what happened and then give you an AI summary saying, hey, here's what's going on, and that speeds things along for that SOC analyst, or for that one person who's in charge of managing the EDR when you don't have a SOC team, being able to see that and quickly do assessments. I think that's a great use for it. But that's about it, when it comes to the security side.
Matthew Connor: And I think you used a term there I completely agree with, and that was summarization. AI is fantastic at taking a vast amount of data, summarizing it, distilling it down to bite-sized portions it can give to a SOC analyst, a manager, an executive, whoever, where they can look at it and get an idea of what's going on at any given time.
Tony Bryson: The challenge I think we run into is that, just like human beings, artificial intelligence can get bamboozled by a false positive and waste a lot of time. We've had experience with that, where we've had false positives where the AI is just like, no, this is happening, you need to take this seriously, and we go look at the data and it's like, no, this is definitely a false positive, how could AI fall for this, this is pretty simple and straightforward. So it's one of those things again, trust but verify, make sure what's coming back is accurate, because a lot of times there have been hallucinations that have found their way into some of the returns that have been generated. And it's left us kind of sitting there scratching our heads, going, should we be turning over the keys to the castle to this thing? At this point, probably not, no.
Matthew Connor: And I think you nailed it there. This is really interesting, because when you think about the term super intelligence, or artificial general intelligence, we have this idea that it's all-knowing, all-seeing, flawless. Okay, first of all, flawless doesn't exist anywhere in anything, so to reach that level, one may argue that's an impossible definition, an impossible thing to ever achieve. So in some regard, we'll never be at that level. But if you rewound three or four years ago and said, hey, what would AI look like when it's at the level it's at today? I mean, just last month we solved, what was it, ten impossible math problems that mathematicians had been struggling with for decades, solved with generative AI, amazing. So do we say, when we have conversations, you can converse with it, and there's no way you're saying that isn't a brilliant person on the other end, it passes those tests. So I think the goalpost is moving on what that definition of super intelligence, or artificial general intelligence, is. So when it comes to do we hand over the keys to AI, I'm not sure we necessarily ever do, but right now we certainly shouldn't. But I want to bring this back to what you were talking about with the false positives, or wrongfully saying this was an issue when it's not. How does that compare to your more traditional tools, where you're getting notice after notice of false positives, and you're like, oh yeah, obviously? It's interesting, because I feel like we're very hard on AI whenever it's wrong, and yet our traditional tools are wrong constantly, we're constantly getting alerts and we're like, oh well, that's a filter, what do you expect? That's AI, it's nothing different, it's a more advanced filter. And yet we hold it to this standard, I think it's interesting. I mean, we do have to hold it to a standard, but it's interesting, how do you balance that, the alert fatigue versus the false positive? Alert fatigue on your traditional tools versus potentially that false positive on AI. If you were going to do a direct comparison of those two, how do you compare them, or is it like, you're trading one for the other, pick your poison?
Tony Bryson: Well, there is a little bit of pick your poison in there, but at the same time it's also recognizing the strengths and weaknesses of both the human behind the interface and the artificial intelligence doing the assessment of what's going on. One of the things I think we need to lean heavily into is the continued peer review of anything that goes on. I don't let my staff go and do anything without first having their work checked by one of their peers. If you're making a broad, domain-level change, you can do a lot of damage quickly with a fat finger. I want to make sure I have another staff member, whether from my information security team, or if it's affecting infrastructure, someone from that infrastructure team, go take a look at that security analyst's work, make sure it's accurate, that it doesn't have any errors in the code we're putting in, before committing that change. That's why change management is such an important facet in managing the security of your organization, and the overall function of your organization. If you don't have a good change management process, you're probably not running a very tight ship.
Matthew Connor: Well, let me ask you, is that one of those places where AI kind of shines, where you could put it in as a peer review? You do the work and say, all right, check my work, what did I miss?
Tony Bryson: You're right, not having it make changes, but as another peer reviewer, because there's liability involved with that, I would say no, I would not be comfortable using artificial intelligence to do that peer review, just because that is our last step before putting code into play, or putting any change into play, and there is a liability aspect there, and undue risk in just passing that off to a non-sentient being making a go, no-go decision on behalf of the organization. Because there is risk involved, because there is liability involved, you should always have a stakeholder who's going to put their skin in the game to say, yep, let's go forward with this.
Matthew Connor: A hundred percent agree. I guess let me clarify, would you add it as one of the peers to review?
Tony Bryson: Right, so I think that's where it really kind of shines, and then you're like, oh, I totally missed that aspect, great, thank you. But not as the ultimate decision-maker. It's funny, we're getting people who, very quickly and early on with generative AI, started trusting AI to make their decisions in life, and that to me is mind-blowing. I get it, as you've got a very informed advisor, they've amassed a lot of, they can access a lot of information and distill that down, summarize it, and give you some. But the fact that people, and I get it, maybe the average person isn't the brightest, but still, it's shocking that people would be turning over their lives to this.
Matthew Connor: And I think that for decisions, like any advisor, so many people are, and is this a bad sign for where things go for humanity, where we're just handing over control of our lives and our thinking? Will so many people go and do that, where they're just like, yep, AI told me this is what I should do? Now maybe their lives will turn out a lot better than if they'd made their own decisions. We're seeing it already.
Tony Bryson: Yeah, to me, it's, the question I always ask is, hey, would I want this AI running my retirement portfolio? Answer is no. I want somebody who has a lot of experience, who can recognize a lot of things that artificial intelligence is maybe going to skip right past, recognizing market trends from historical trends that maybe AI isn't going to be thinking about. I want to make sure that when someone is managing something as important as that, I have someone to yell at if something goes sideways. I can't yell at artificial intelligence. I want to be able to get my investment broker and give them a real good piece of my mind when they lose half my portfolio.
Matthew Connor: Yes, a literal neck to ring.
Tony Bryson: Exactly.
Matthew Connor: So, you know, it's really interesting, because we literally just had a guy on yesterday, and his episode will be coming out, Benny Zhang. He was a CISO at another company, and now he basically spends a lot of his time managing his own investments, and he uses AI to do that. No, he's not giving over full control, I say that, but he has given over some control to it. And the interesting part was how quickly it can assess so many opportunities and point out things that, as human beings, we just can't process that many, we'd miss a lot of opportunities. I'm not sure that's necessarily how I'd want to invest, that rapid day-to-day, kind of more like day-trading type of thing. I'm much more of a Warren Buffett kind of guy, I invest in things I know, things that make sense to me, and if it doesn't, I don't care, I won't miss any of those opportunities, whatever, fine, there's a billion things to invest in, but our little minds can only focus on so many things. So it raises a really interesting point when people are actively using AI to manage their money. So far, he's doing better with his AI account than he is with his personal account, I'm not sure what that's saying, I'm just kidding, Benny.
Tony Bryson: I think that's really great, I'm happy that's working out. But again, I'm just in that school of, I think it's a great advisor and assistant, but I just don't think it should be pushing the button without somebody watching and saying, yep, push that button, yep, push that button. It's why there's guardrails in place within the market itself, to prevent runaways in either direction. If the market identifies there's a runaway sell-off taking place, that there's panic happening within the market, there are controls in place where the market will automatically shut down. And I think without that, Benny would be out of his mind, but the fact that those controls exist, I think the risk he's taking may be worthwhile for him.
Matthew Connor: Yeah, I mean, it's really interesting, because so much of investing is psychology, right? That's what drives everybody buying and selling, and really almost every decision, it's an emotional, psychological one that we then justify with numbers. And so it becomes really interesting when you're using a machine, does it really understand the emotions at play? Heck, obviously we're not so great at it either, or more people would be really great investors, but it's really hard to beat the market, and very few investors do over the long run, beat the market historically for more than ten years, that's a pretty rare thing. So I don't know, I think it brings us to an interesting place, would the market be better if it were run predominantly by machines, so you don't have the emotional sell-off? Or would it just oscillate wildly because it's all machines working against each other?
Tony Bryson: I don't know, I think you'd experience more oscillation. But I love that you mentioned emotionality being removed from the equation, because that's where I really like to rely on AI myself, when I'm wrestling with an emotional decision and I want to make sure I'm taking the pragmatic route. I give AI all the data it needs and just say, if you were in situation X, Y, Z, what approach would you take and why, what are the reasons behind that, and let it give me advice and guidance. That's ultimately how I try to use AI myself, it's providing guidance, it's another tool for getting access to information, but it's still up to me to validate that information, make sure it's well-sourced, that it makes sense, that it's consistent with what's out there, that it's not just flying in the face of what we already know. So AI definitely has a place, we just have to recognize it's still maturing. Frankly, it's still a toddler, and I'm not going to hand over the keys to the car to my three-year-old and say, hey, drive me to the airport. I'm going to give the keys to my wife and let her drive me to the airport, and let her drive the car under the curb instead.
Matthew Connor: No, it's really funny, and I think you're a hundred percent right, it is still a toddler, a phenomenally intelligent toddler, but a toddler nonetheless. And I think keeping that perspective in mind would help people a great deal more. I'm very excited to see what the next few years, and the future, bring. I think we're living in the greatest time to be alive right now, and I'm sure the same will hold true in five to ten years, who knows, once we reach that level of super intelligence. I'm an optimist, I think things work out for the best, other people completely disagree with me and say it's the end of days, who knows, time will tell. But you know, when you were talking about the emotional stuff, it reminded me, I think AI is really great at helping with that. You get an email that angers you, and you try to write back as professionally as possible, and you draft something, you run it past AI, and it does a great job of tempering it. It reminds me, did you ever hear the story of how Abraham Lincoln would deal with situations like that? Apparently he had a very sharp tongue and was an incredible writer, and he would write these flaming letters that would just destroy people, and one day it got him in trouble to the point where a guy challenged him to a duel, and he thought, that's it, I've got to stop, I've got to stop doing this, this is going to get me killed. So he devised a system: I will write this letter and put it in this drawer, and if seven days from now it still holds true and I still feel like sending it, then I will send it. And as the story goes, he never sent another one, and he was never challenged to another duel, and he became known as a great speaker, and very friendly. But it was because of that. I kind of have to wonder how much AI would have helped him if he were alive today with that, if he'd used ChatGPT or Grok or whoever to help him temper that. I love the seven-day waiting period, it makes sense.
Tony Bryson: I do that myself. If there's an overly emotional issue that comes up, I'll draft my email response and leave it on my screen, or at least a day before I go back to it, look at it, and say, yep, that's good, that's ready to go, just because you don't want to respond emotionally. I know my staff tell me at times that I can be very unemotional, but that's part and parcel to being a CISO. You have to always have your head about you, especially during a crisis, your best bet is to sit back, observe, understand what's really going on before you make any type of judgment, any type of decision as to what we do next, to have all the information available. Some people may view that as being distant or cool towards others, but it's not that way, it's a matter of wanting to make sure we do the right things at the right time.
Matthew Connor: Yeah, I think that's super important, and I think that's a really hard thing to do, especially in times of crisis. And that is, I think, the burden of command, as we say in the Army, you have to keep your head about you when all others are losing theirs. And that's a real challenge, so kudos to you. And I think it's far better to keep your head and be calm than to lose your head, they know emotions have their place, it does, but I just don't think it's in the time of crisis. And I think that's a really hard thing to do, and I think leaders should be more cognizant of the impact of their emotions. I think we live in an age, especially modern times, where people have seen too much emotion in leadership, and I just don't think it's the way to go. I'd much rather see calm, cool-headed leaders than emotionally erratic ones, and I think the whole world would, so that's my stance on it. I'm not trying to get political, but I do think you're taking the right approach, and I think that's where AI can help those people who tend to get heated.
Tony Bryson: Yeah, well, there's always a time for passion. I can speak very passionately to a number of topics that come up, and I will draw my line in the sand and get on my soapbox and rail on for several minutes at a time about any given topic. But the reality is that when it comes to the rubber hitting the road and needing to make the decision, that's where you have to be calm, you have to weigh all options and then make the right decision. So that's where that calm demeanor is needed in those moments of crisis. I just want to make sure we differentiate between passion for your job and the things you do, and being completely, I guess, emotional in the moment.
Matthew Connor: Yes, no, and I think that's an important distinction. Tony, this has been an absolute pleasure, I've loved having you on, and I think we could do this all day. But before we go, can you tell everybody where they can find out more about you, and more about the Town of Gilbert?
Tony Bryson: Let's see, Town of Gilbert, you can look us up at gilbertaz.gov, I have all sorts of information out there, we have a fantastic digital government office that does a fantastic job getting information out to the community, to our stakeholders. For myself, I'm on LinkedIn, look me up there, best way to get ahold of me is through LinkedIn. Yeah, at the Town of Gilbert we love to work for our citizens, try to be the city of the future, that's probably all I can say on that particular topic.
Matthew Connor: That's perfect. Well, thanks again, Tony, and until next time.
Tony Bryson: You bet, thank you.