Tech Transforms: Episode 6

Dawn Zimmer: The brilliance was coming up with a relationship with our AI model provider that allowed us for web grounding. So all the data stays in our environment. I get a daily feed from the Internet, so I've created an environment where they're getting live Internet data, but our

Carolyn: data never goes back, never goes out. It's protected, right?

Dawn Zimmer: It is all protected. Um, I'm giving them the power of AI at its fullest capacity inside, in a safe environment.

Carolyn: The Department of Energy is pioneering something that's genuinely hard. Breaking down decades old data silos, modernizing legacy IT across national labs and critical infrastructure, and building AI tools in house that the rest of government can look to as a model for years to come. Dawn Zimmer is the CIO of the Department of Energy, and she runs on something she calls the speed of need. Not exactly a bureaucratic phrase, but that's kind of the point. Don's whole philosophy is that it doesn't get to show up at the end of a project and say, actually, that's not secure. Cybersecurity is baked in from the beginning. It is foundational. And somehow that's what lets her team move faster, not slower. Under her direction, her team has also built something called Quanta, a data platform that's quietly pulling together information from parts of DOE that have never shared data before. And Julix, an AI environment where DOE employees can use the full power of AI with a live feed from the Internet coming in and no DOE data ever going out. One direction only. That's not a constraint. That's engineering. I wanted to know how dawn thinks about her role as, uh, cio. You're modernizing systems that support everything from national labs to critical energy infrastructure with a lot of legacy old stuff. And when you think about legacy IT at, uh, doe, what's the real risk of not modernizing? Let me get that word out. And how do you prioritize what gets modernized first?

Dawn Zimmer: Sure, yeah. So I look at it this way. Modernizing legacy, it isn't just about new technology, right? It really starts about the mission, right. And being mission first, and then the technology follows. So in my career, every major failure I have ever seen from a technology perspective starts the same way. Someone fell in love with a solution, and then before they even understood the mission, they were already putting a solution in place. And then they go, oh, um, there's this big mismatch, and you don't have that luxury in government, right? You don't have the money, you don't have the time. So you have to think about this differently. Um, and so what I've been really pushing the teams to think about is what's mission outcome? We're trying to change what would better look like in operational terms, like, what are the things that we really want to get out of this and then make the technology the enabler. So one of the things we've been seeing a lot of is, you know, we're moving at the speed of light right now. I mean, it's this. I call it the speed of need is what is written into my strategic plan. And I get probably a call every single day that says, don, we came up with this really great idea. We just need you guys to take it over and run it for us. And I go look at it and I'm like, wow, this is really cool, but it's not safe. There's a lot of risk. Your data is out going to be all over the Internet in the. In the course of a week. Right. If not sooner. So why don't we roll back and tell me what it is you want to do. Let me be your strategic partner, and why don't you let us understand what your business problem is, and then we'll come up with something that gets you all the functionality you want, but in a safer environment that we can. And they go, uh, well, you know, we're kind of married to this thing and this thing. And I'm like, no, let's just try this. And so far, we've had three major successes in the past couple of weeks where the user community or the requester has been, uh, like, wow, that's actually even better than what we came up with. And this is safe and. Yes. And we can do more. Yes. So really, um, that mission first mindset just leads to a better way of doing business.

Carolyn: Right. I think that's such an important thing to step back and ask, no matter what you're doing. If you're the CIO of doe, maybe a little higher stakes than the VP of marketing. But even in marketing, when my team, we. We get going really, really fast and we start doing things just to do things, and it's really important for us to step back and say, what are we trying to achieve here? What is the actual objective? And are we going about it the right way?

Dawn Zimmer: Right. And, uh, you know, and I think the biggest challenge, Carolyn, is, you know, we need to also think about where is it we want to get to. Right. Because I can build for today, but I really want to be building for tomorrow because I don't want to rebuild down the road. Right. So it's. And the technology is moving so fast and the, you know, the power of AI, right? We just, you know, there's more and more and more that you can do and you start building a solution. They're like, well, I've solved problem A, and what I'm trying to do is look for, I want to solve problems C, D and E down the road for you, starting with A and be able to scale it. Right. So that I'm investing from a, from a platform that I can bring. Bring with you as opposed to having to trash that one. Start over to get to whatever your next business need is. And that's quite kind of what we've been doing with our Quanta product. You know, we started out with really simple, you know, kind of a data environment. And it turned into, um, let's collect some data to create some dashboards for some folks. And the next thing that happened was, hey, can we cross connect some data from this part of the organization and this other part of the organization with the data that we already have? And some of that data was not public data. And how do you do that? So we were like, well, we have an environment. It's in our cloud, it's secure and working with each of the stakeholders, showing them this is a secure environment. We've got all the security controls. We now have data coming in from parts of the organization that would never in the past have shared their data. And the power of this, this um, of Quanta now just, it keeps growing more and more and more, uh, uh, like on a daily basis, like, excites me every single day. Because they're like, and we've got another data source coming in. And then I go into a meeting with an executive and they're like, yeah, we'd like to tell this story. I'm like, we got all that here. And we show them the prompt and we show them what the tables we can build and they go, wait, you can do that? And you already had that. And like, that's that speed of need. They needed it and it's already happening.

Carolyn: So this initiative is bringing, is, is getting rid of the silos, the data silos from the different groups, bringing them all in and using AI quantum to analyze and integrate and move, like, come up with solutions.

Dawn Zimmer: Yeah, so it's. So it's artificial intelligence for sure. Uh, the name of the product that we've deemed it, our internal product is Quanta. We're not quite at the point where we're using quantum computing yet, although I'd love to someday. Um, but really we've just, you know, we kind of gave it a little like internal marketing name. Um, and it's just, you know, it's built, it's built on a data platform with databricks and we're just building off of that incrementally. Um, but breaking down those data silos for sure.

Carolyn: Was that the objective of Quanta, to be able to aggregate data from all over doe?

Dawn Zimmer: Absolutely. It was always part of our data strategy from day one. Um, there's a chart on one of my deputies, um, uh, the deputy CIO for this particular area, she has this diagram on her wall and it showed like all of the places, right. And like where we wanted to make all these connections come together. And um, every time it's been up there probably for a year now, like, and none of us ever want to erase it because it just kind of always reminds me that that was, you know, where we started was a conversation on a whiteboard. How can we cross connect all these things? And sometimes I just want to go in and go, oh look, we got that one check and we got that one check. And like, you know, it's just starting to come together. Um, and what we're giving them is we're giving them decision support tools, you know, real time. So that, you know, the power of being able to use our taxpayer dollars to make good decisions that enable energy dominance and meet the secretary's goals at the speed of need is just, you know, it's just a beautiful story of all of us working together. Mhm.

Carolyn: There's so many things that I love about this story. One of them is that you sat in an office, human beings in person sat in an office together and wrote on a whiteboard. Um, this isn't, you know, there's a lot of fear right now about AI taking our jobs and being able to do what we do. And I still maintain that, that we have to, you know, the creativity, the ideas are still driven by us.

Dawn Zimmer: Right.

Carolyn: So I'm also wondering, you know, you mentioned planning for the future, planning for down the road, not just to get to this milestone, but to get, you know, 100 miles down the road because rebuilding is hard. You, there's got to be a lot of legacy it and doe, I have no idea how much. There's just got to be a lot. So multiple part question, was Qantas part of the objective to help you identify what legacy it to modernize first, like where to, how to tackle that and how, and even how to start modernizing it is that part of the objective?

Dawn Zimmer: No. Okay, yeah. Yes. Yes and no. It wasn't. It wasn't necessarily, um. It was part of the objective. Yes, it was. So, yes, there is a lot of legacy it out there, but there's lots of systems doing very specialized things. What we're finding is that Quanta is able to fill some of those gaps without us even realizing. We went into a meeting the other day and we were showing Quanta for a completely different use case. And at the same time, I was rebuilding a legacy system into something a little bit more modern, uh, with a little less technical debt for the same organization. And we all looked at each other and said, you know, we could take that and move it over here and meet the objective by putting it all in Quanta as opposed to building two systems. So it's not necessarily. It wasn't necessarily like we went in with that objective, but we got ourselves there. So sometimes we stumble into it.

Carolyn: That's right.

Dawn Zimmer: Yes. It is very, very. Sometimes it is very, um, predetermined where I know where the legacy is and where I want to move it to. Um, but every day I stumble upon a new system that I didn't even know existed out there.

Carolyn: How do you modernize without compromising mission continuity? It's like building the plane while you're flying it.

Dawn Zimmer: Or, or you build two, right? Or you fly one plane and you build the other one, right? And at some point, you have a path that takes you from one to the other. I always call it like, you know, I always think about the Commander Stadium in D.C. right? You know, you're gonna. We're gonna build. You're gonna build the new stadium. You're gonna play in the old one until it's, until it's finished, right? And then you're gonna have your migration plan to move folks over to the new one. Um, and I think being really. And making sure, you know, and my. My team will tell you my number one motto is we don't ever intentionally break anything, right? And we look for all the ways we can unintentionally break something and mitigate for it earlier. So we're, We're. The first thing I'm always thinking about is I don't want to hurt anybody's anything, right? We're gonna, uh, we're gonna have. We're gonna be very mindful in the way we cut over. So if you look at what we did with workday, right, we took our HR IT system, we built work. We stayed on the system we were on, which, uh, was a legacy system we built workday out. And we very intentionally moved pieces over one at a time. Right. And tested, made sure, okay, great. Set a date for a cutover, moved over. But we didn't try it. We did it in small, manageable chunks and where we had confidence levels built. We have a governance structure that makes a go, no go decision. And we're not doing anything where, you know, 100,000 employees could end up without, you know, HR services because we got either too aggressive or didn't think through all of the pieces. And that's kind of how I approach all of our modernization efforts. Um, just, you know, very, uh, consciously as we're going through it and really kind of thinking through, like, what's the worst scenario that can happen here? Okay, let's plan for that one.

Carolyn: That sounds like a much gentler migration path, um, than I was imagining. Honestly, right now I'm going through a small. Like, my house is getting painted just on the outside. Not a lot. It's really painful. And I just keep thinking, you know what? I think it might be easier to just bulldoze it and start over.

Dawn Zimmer: Uh, I feel the same way. I mean, you know, the thing is, I came from, you know, faa, right, where we tried to modern. We've been modernizing the air traffic system for, you know, 25 years, 30 years. Right?

Carolyn: Yeah.

Dawn Zimmer: And I worked on NextGen, so I saw, like, you know, how important it was to kind of, you know, know, know where you were, where you wanted to get to, and then have just applied a lot of those lessons in this role and, um, and try to, you know, think about. It's also, we're in a different place today. We're able to build faster, Right. The tools are there to allow us to build faster. It 10 years ago, the process was, you know, I spent six months gathering requirements. I brought every stakeholder I possibly could into the room. I had 100 pages of requirements and who wanted to feel blue and it had to do X and how to do Y and how to do Z and whatever. And then you never got any time to build anything because it just. Everybody would go back and say, oh, now the requirements changed. And then you finally started to build something and you went into a year build cycle, right? And then you tested. And then, you know, by the end of it, it was obe because the business had changed, the administration had changed, the needs of the organization had changed. And today, being a CIO today is so exciting because the tools are there. Uh, you've got initiatives like techforce, where I can get coding help now and hands on keyboards. I've got vendor partners, industry partners who are like, tell us what you need and how fast you need it. And they're creating stand up solutioning teams as well that you can partner with. I've got folks that are thinking about the problem just differently and going well. We saw success here. We could build off of that. It's just like you're just moving so much more quickly that, that flying the plane, you know, uh, the two planes at the same time or the same thing that's like gone down to like you know, weeks versus or months versus years. Right. Which makes it a lot easier than to make those transitions because you're not kind of having to wait until all the pieces are built out. You know, you're being able to do things a little bit more with um, a little bit more agility I think, um, than ever before.

Carolyn: Well, I think AI has brought a whole new component to that too. You know, you talk about how, how quickly we can code. I don't know how to code. I took a coding class. You know, I think I was in junior high. I don't know how to code, but I code with AI. Which brings up should I be coding with AI?

Dawn Zimmer: Right.

Carolyn: Should I be coding at all? Let's talk about that. Let's talk about AI at doe, how you're handling it, the security around it, the guardrails around it. What are you doing with it?

Dawn Zimmer: Yeah, again I go back to. We started with the mission first and um, we are definitely using AI to solve as many problems as we can. We launched Juliex back in, I want to say 2023 was, uh, before I arrived, um, and it was this kind of, you know, small chat GPT kind of. We called it Energy GPT. Um, and it was one component of a larger toolkit called Julex.

Carolyn: And is Julex a DOE proprietary thing?

Dawn Zimmer: Because I don't know that it is a proprietary environment. And in there we put um, uh, something called Energy GPT, which is like your chat GPT, man, you guys are pioneers.

Carolyn: Like you are way ahead of this.

Dawn Zimmer: Was some glass being broken and I cannot take credit for it, but I'm going to talk about it as much as I can because it's the coolest thing uh, in government right now. We are probably the only department or agency doing this. Um, so what we. And in there we also put like a position description builder and a uh, performance workstation builder. So using AI technology, uh, to feed it the brilliance was coming up with a relationship with our AI model provider that allowed us for web grounding. So all the data stays in our environment. I get a daily feed from the Internet. So I'm getting. I've, uh, created an environment where they're getting live Internet data, but our data

Carolyn: never goes back, never goes out. It's protected, right?

Dawn Zimmer: It is all protected. So, um, I'm giving them, you know, the power of AI at its fullest capacity inside, in a safe environment. So I can throw all the DOE data in there that I want to do some of the, you know, ask the questions, you know, give us the answer. So we put in there a performance workstation builder. We put in the position description builder. We took all the executive orders and put all the executive orders in there. So you can do anything you want in terms of searching on all the EOs. And, you know, comparing, contrasting. We are now looking at, um, like, how can we do set up some, uh, technologies that'll actually allow you to, like. I picked an executive order web scrape for everything Secretary Wright has said about that particular executive order or the press has reported on and like, put that into, uh, a daily news thing that I could just like, you know, hit my prompt and have it give me all of that, uh, in a single interface. Right? Um, so we are just like really leveraging that AI and just starts. That adoption just starts at the workforce level. Like, how simple, right, that I can just log into Julix. I use it every day. I go in there like I'm trying to write a memo sometimes, you know, yes, you've got copilot that you can go to. But sometimes I'm like, oh, I need a little bit more information. And I go over to Julie's and I'm like, okay, well, what executive orders are out there that DOE has done? Da, da, da, uh, compare it to give me these things and bring that in. We gave them a work environment called Canvas where they could find all of their stuff, bring it over, and then start to build their memo or their white paper or their notice of funding opportunity or whatever in a, uh, kind of a workspace that's also AI enabled. So really bringing it starting there at the desktop and then just giving them that capability, and then that just incrementally grows, like I said earlier, where we're pushing it everywhere we can. Um, I'm looking at like, you know, what's our correspondence system look like? Can we add AI to that so that we can have more intelligent approaches to doing correspondence? We're talking to the FOIA folks. We're Talking to the general counsel folks. Right. And the environment is hungry for it. The problem is, you know, you have to keep up with all of that. There's a pace there, right? Yeah. You only have so many people.

Carolyn: That's right. It's breakneck.

Dawn Zimmer: Yeah. So. Yeah. So. And that gets us to you, like I said, using a lot of like, reusable tools. What are the things that we already have that we can continue to build off of and that we're not creating new? Every single.

Carolyn: Mhm.

Dawn Zimmer: Single place. Um, we're seeing, you know.

Carolyn: Sorry, how do Quanta and Dulix, or do they uh, work together?

Dawn Zimmer: They work. They. Well, they're separate products. Mhm. But they do, they do interact.

Carolyn: I would imagine Dulix is feeding Quanta data because you said it's bringing in

Dawn Zimmer: data sometimes some of the data that's in quanta, like there's a little. It's more Julex feeds into Quanta than Quanta feeds into Julex.

Carolyn: Yep.

Dawn Zimmer: Yep.

Carolyn: Okay. So you know, we're talking about this breakneck speed that everybody's going at. Agencies are trying to adopt AI, they're trying to modernize, balance, speed, governance, security, risk, especially in critical infrastructure environments. What advice would you give about balancing all that?

Dawn Zimmer: Um, yeah, I don't know. I always feel like. Remember the lady with the spinning plates? Yeah. That's kind of the way I feel half the time. And I think it's, um. You know, the balance is for me is it's so easy to get so excited and almost outpace yourself. Right. And get the organization so excited. And we do, we sit back sometimes and we're like, okay, wait a second, like, let's just stop and think before we run down this path. You know, first thing is, it will always be my number one priority is going to be protect the environment, protect the data. Make uh, sure, sure that it's not a separate step. Like cyber has to be baked into it, it's foundational, it's non negotiable and yet not make it a constraint. Right. So we start with that as a big part of the conversation. What's our, you know, what capabilities do we have in the toolkit that we could, we can leverage? And then what's our capacity to take on work? Um, and, and then, and to your point, it is, it's a balancing game. So we meet every week. You know, my leadership team and I meet every week. We are doing, you know, we do monthly program reviews. We are constantly looking at all the opportunities that come our way through, uh, an Opportunity management program. We assess them, we try and figure out, well, you know, this one actually we could use. This we've already built. It's a simple modification, you know, bang. We can move that one out the door versus this one's going to be a little bit more of a heavy L shift, you know, where, where can we fit it in, how can we fit it in? Um, and what does it bring to the business? What do we gain from it? Um, obviously if it's a mission partner is asking for it, you know, those always go to the top of the list. Um, and then you know, at the same time you're trying to do things internally for your own organization to make sure that you've got all the tools, you know, kind of, kind of moving because you know, you need to, you need your toolkit to always be sharp. So yeah, it's definitely a bunch of plates all day long.

Carolyn: That is my new logo for you. Is Don Zimmer, CIO of doe, spinning plates? And with that we're going to take a quick break right here and hear from our sponsors. We're going to take a quick pause to thank the sponsor who makes these conversations possible. This episode is sponsored by OWL Cyber Defense, a pure play cybersecurity company delivering Made in the USA data diode and cross domain solutions trusted to protect some of the most sensitive government and commercial networks worldwide. OWL enables secure, near instant collaboration across network boundaries, helping military, federal and critical infrastructure organizations make faster, safer decisions. To learn more, visit owlcyberdefense.com we're back from our break. I'm Carolyn Ford, this is Tech Transforms. I'm with DOE CIO Dawn Zimmer. And right before the break Don you were talking about, you touched on, and you've touched on this multiple times, how you bake cybersecurity into everything you do. Um, and before we even go there, I wanted to say I asked you that balancing act and you conjured up the image of the woman with the spinning plates. How you balance governance, security, AI, guardrails, all of that. Um, I realized this whole conversation, and you started the conversation this way, is what's the mission? What's the objective? That's what you keep going back to. Even when we're going at these breakneck speeds. We're not going to bring on new tools just to bring on new tools. How are they going to help us achieve the mission? I think that's a really important point. And how you're able to keep this balancing act going.

Dawn Zimmer: Mhm. It really is, um, and like I said, that's been our cornerstone. Um, you know, is keeping that the hard. Like I said earlier, I think the hardest part is, um, you know, this is an environment where, you know, everybody wants the. Let me, let me put it this way. Fifteen years ago I came up with this notion that IT users or users want to use it in the office the same way they use it at home. And that hasn't changed. Fifteen years later I've got uh, users who come in and they're like, hey, at home I can go on Claude or Gemini or whatever it is and I can do these things. I came in the office and I wanted to do that. How come I can't do that?

Carolyn: Mhm.

Dawn Zimmer: And you have to. Trying to explain why that's not secure and that how the models are getting fed and how you're putting data out there, you know, could put the department at risk is sometimes really hard to do because folks, they just, they're like, but, but I do it at home. And I'm like yeah, but are you putting your, you know, you may as well put your Social Security number out there. Like, are you putting your Social Security number in the AI, in your AI environment at home?

Carolyn: Right.

Dawn Zimmer: Well, no, I would never do that. Then why would you put the DOE data about, you know, what our next funding opportunity might be out there? M. Right. So trying to find a way for them to understand that, that, that yes, you want to use it the way you use it at home in the office, but we need to do that safely. And you know, that just take it, it takes some doing and a little bit of finesse. I think we're making some real progress there. When I, you know, kind of give them some examples of uh, what, what could potentially happen and how, you know, I give them their doomsday scenarios and they go, oh, well, we didn't know that. And I, you know, I'm like, well, you know, the more you put in there, you're actually feeding the very organizations or competitors that you're, you're trying to do business with. You're, you're giving them. Because they're not going to say, they're not going to do a search that says hey, what's do we asking? But when they do a search about something, you've just put data in there that's going to give them that information and they're like, oh right, okay, we got that.

Carolyn: And, and at home you're not securing the nuclear power plant.

Dawn Zimmer: Correct.

Carolyn: So it seems like it's kind of a no Brainer.

Dawn Zimmer: Right, right, right, right. And you know, at home you're not min. Minerals, right? You're not looking for ways to lower.

Carolyn: Securing our water system.

Dawn Zimmer: Um, exactly, exactly. You know, the grid, I don't know. So, um. Yeah, so, so yeah, I mean, and they, and, and it's not. I mean, I work with the smartest people in the world and they get it. You know, it's just, um, a lot, you know, of folks, you know, it's a different environment, you know, got some folks who have not necessarily been in government before. So this is like new information, right. And I'm super sensitive to that and always looking for like I, I know, I know, you know, like. I know, but I'm gonna. Let me explain it to you again. Um, and coming to a happy, happy compromise. But one of the things like we are doing is um, you know, this is still, you know, it's obviously just, even your basic cyber is a hop up, right? And you still have like basic things you have to do. So we, we actually launched an initiative this year called Echo, um, which is our energy consolidated cyber office. Um, and that is giving us a dashboard where I'm able to pull the cyber data from across the entire organization, um, to include the labs, plans and sites into kind of a single pane of glass and start really understanding what our cyber posture is and where our risks are. Because you know, it's really easy to forget, you know, you have to be brilliant at the basics, right? You kind of get all wrapped up in all the AI and the modernization and all the cool stuff that's going on out there. But at the same time there's some foundational, just regular work that still happens. Right. I'm still, you know, I've still got infrastructure that's got to be protected. I've still got an email, you know, email tenants that need to be protected. Um, so how do I, you know, use the power of AI to help me just manage the regular day to day security posture, understand where our risks are and make sure that we're not losing sight of the, of that stop. Because we're so spun up on all the other cool stuff that's going on out there. Um, and I think that's really where the balance sometimes comes in is, you know, kind of, you know, just making sure that you're remembering, you know, that there is this basic, you know, operational stuff that enables all the other things and you need to make sure you're protecting that as well.

Carolyn: Yeah, just that basic cyber hygiene that we've been talking about for 20 years.

Dawn Zimmer: Yeah, gotta keep that. Yeah, yeah, yeah.

Carolyn: So what are some measurable outcomes that you've seen with the modernization efforts and the ones we talked about today? And just in, in general, have you seen, um, reduced risk exposure, improved incident response times? What have you seen?

Dawn Zimmer: So we're definitely seeing, um, reduced, uh, we're definitely seeing big improvements in our response time on things. In fact, one of the initiatives we're working on, we were just talking about this yesterday, is pulling all of our help desk tickets, um, and using AI to start, like proactively, like, hey, all these users, you could do the analysis to say, oh, we got 53 password reset calls last year. Right. But how do I use AI to actually figure out what does that really mean? What are those 53 password resets really mean? When do they happen? How is it, you know, what's the user profile? Uh, how can I put other tools in place? Um, it always happens on Mondays, you know, whatever. Right. So do like, more in depth analysis on some of our tickets. Um, and I think that the other big thing we're seeing is the cost savings. I mean, last year, you know, uh, we saved a lot of money. We started to really reduce contracts and then we're, you know, refocus our efforts instead of doing, you know, 100 small things, you know, that just, we're going nowhere. We're, you know, we're doing, uh, you know, maybe 50 now, you know, 50 big things. Right, right. But we're making bigger investments in, in things. So we're seeing, you know, better cycle times. We're seeing reduced tools. Right. I'm seeing reduced licensing costs because I'm not having as many, um, as such a variety of things, or, you know, looking for redundancy and getting rid of the duplicity of tools. Right. And saying, okay, look, let's just have one tool to do these things and save some money. So. And then being able to redirect those funds into, to something else.

Carolyn: So this impact, are you seeing it from the modernization efforts in general, or are you thinking about AI specifically that's saved?

Dawn Zimmer: I think it's both. I think it's both. Yeah, we're definitely seeing the impact of AI saving time, um, allowing for more time to do more things.

Carolyn: All right, well, before we go to our TechTalk questions, is there anything else that we, we didn't touch on that you'd like to add here?

Dawn Zimmer: Um, I know, I think we covered like all of my, uh, you know, my things. I don't, um, you know, it's like I said, it's. It's an exciting time to be in federal government. Um, and it's exciting time to be a cio. There's, um, every day, Every day I go in, like, I go in with a smile on my face and super excited to see what the day brings because it's always a new challenge. And I just, you know, I love working with my. My user base and my customers and, um, you know, being a solute, you know, providing them with solutions and watching the look on their face. And I go, that's so cool. We can do that. When you go, yes, we can do that for you. How soon can I get it? I can have that in set up for you in three days. And they're like, really? In three days? I can get that? I don't have to wait a week? No, we'll have it in three days. So that's the. That's the win. Every day.

Carolyn: Your excitement and your passion is infectious. Like, I feel like I've just had a hit of probably cocaine's not the. The PC thing to stay here, but, like, just energy.

Dawn Zimmer: I'm telling you, uh, it's energy is energizing. I mean, you know, you have just,

Carolyn: like, pumped me up for the day.

Dawn Zimmer: It really is. Carolyn, I gotta tell you, this is probably. I mean, I've been doing this a long time, and this is the coolest job I have ever had the honor of having. And I feel, like, so blessed every single day that, you know, Greg Barbaccia and Carl Coe were like, yes, we want you as our cio. I will forever be thankful that, you know, they. They stood behind me on this, um, because it's just. It is a wild ride. I love it.

Carolyn: We've definitely got the right person for the job right here. So let's go to our TechTalk questions. So these are just fun from the gut questions.

Dawn Zimmer: Yeah. Yep.

Carolyn: Um, so if you could give every federal CIO1 superpower to tackle legacy, it or just a superpower, what would it be?

Dawn Zimmer: It would be the laser sword that allows them to break the complexity of how government systems were built and how the legacy, it just is like a bowl of spaghetti that can never get unwound.

Carolyn: M. So it's the Sword of Dawn. It's not he man's sword. It is the Sword of Dawn.

Dawn Zimmer: Sword of Dawn.

Carolyn: Honestly, after talking to you, I would give them your superpower. I would say it's the dawn superpower. All right? AI and critical infrastructure. Is it your right hand person or is it autopilot?

Dawn Zimmer: Uh, definitely your right hand person. Never trust the con to anybody. Even on the plane. There's always an override.

Carolyn: That's right. That is the right answer. Okay, last question. Cybersecurity strategy. Fortress walls or intelligent immune system?

Dawn Zimmer: Definitely an intelligent immune system. Got to make it easier. Got to have some appetite for a little bit of risk. Um, and you're just monitoring the symptoms all the time, making sure there's no disease getting in there. Yep.

Carolyn: Love that analogy. All right, well, thank you so much for joining us today.

Dawn Zimmer: Well, thank you.

Carolyn: Fantastic start of my day and I know that you joined me on your pto, so I really appreciate it. For leaders looking to modernize their legacy systems and adopt AI securely, where can our listeners connect with you to learn more about your work? Um, and adopt it maybe into their own organizations?

Dawn Zimmer: Follow us on doe.gov thanks for tuning in.

Carolyn: If you found this episode valuable, be sure to share it, leave a review and smash that like button to help us reach more people who could benefit from the conversation. I'm Carolyn Ford. Tech Transforms is produced by show and Tell and sponsored by OWL Cyber Defense. Until next time, stay curious and keep imagining the future.