Black Hat USA 2024 | Jackie McGuire | Cribl

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Jackie McGuire

Principal Analyst

theCUBE Research

Three insights you might have missed from Black Hat USA

The growing threat of cybersecurity attacks along the increasingly complex AI landscape reflects one reason Black Hat USA 2024 is one of the biggest cybersecurity conferences of the year.With 17.8 million phishing emails in the last six months — 62% of which bypassed existing domain barriers — it’s easy to see why robust, versatile cybersecurity and risk management solutions are more crucial than ever.“I think the next five years, and then the next 10 to 20, will be a reset for security,” said John Furrier, executive analyst at theCUBE Research. He added that generative AI could be an opportunity to solve data security problems at scale

Meshing data sources: How Cribl continues to solve data wrangling complexities

“Data is the new oil” being cliche only highlights the statement’s relevance. As data volumes continue to multiply, so do the complexities of management — especially in cybersecurity. Cribl Inc. is expanding on its data maturity ethos and mission to reinvigorate enterprise data management and security with offerings such as Cribl Lake and Search.Discussing Cribl’s data management and security approach with Jackie McGuire.“The number of things you need to monitor has exploded [and so has] the amount of time you have to hold that data,” said Jackie McGuire (pictured), senior security strategist and market strategy manager at Cribl

play_circle_outline Cribl's solution to managing data volume and complexity

play_circle_outline Cribl's products Cribl Lake and Cribl Search for data storage and discovery

play_circle_outline Importance of data wrangling and vendor-agnostic approach

play_circle_outline Bridging Department Priorities: A Comparative Analysis of Observability and Security in Business Perspectives

play_circle_outline Evolution of data trends from databases to generative AI and data engineering

play_circle_outline Cribl's value proposition in providing a platform for data management and sharing

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Jackie McGuire | Cribl

Jackie McGuire

Principal Analyst theCUBE Research

Jackie McGuire, a senior security strategist at Cribl, discussed challenges with data wrangling and the importance of high-quality data. She highlighted Cribl's solutions for data management, including Cribl Lake and Cribl Search. Jackie emphasized the role of communication in addressing fragmented data across teams and the changing landscape of data management. She stressed the significance of proactive data management and adapting to evolving data trends. The conversation touched on legacy systems, key person dependency risks, and the need for effective dat... Read more

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What was the initial problem that Cribl was solving? add

What are some of the key features of Cribl Lake and Cribl Search products? add

What were some of the key takeaways from the Supercloud 7 event at the studio last week? add

What are some challenges in aligning department or team initiatives with group initiatives in organizations? add

What impact do you see data engineering and analytics having on organizations as they continue to grow and evolve? add

What is Cribl's role and advantage in terms of data integration and security within organizations? add

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Jackie McGuire | Cribl

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>> Hello, welcome back to theCUBE's coverage here at Black Hat. I'm John Furrier, host of theCUBE with Savannah Peterson, my cohost. Savannah, great to see you. Awesome segment coming up. Jackie McGuire is here, senior security strategist at Cribl, a company that's been growing super-fast. We've been covering for a long time. Jackie, great to have you on. theCUBE.

Jackie McGuire

>> Great to be here. Thank you.

>> Thanks for coming on.

Jackie McGuire

>> I'm super happy to be here.

>> So a lot going on. Savannah, we're just talking off camera with you about your story. Let's start there.

Savannah Peterson

>> So wait, I want to dive in there a little and tee her up, John, just because it's a really compelling story. You come from money for tech companies and research, and you end up selecting Cribl, which I think is rad. But you were doing research in this space when you became acquainted with them. Tell us a little bit about that.

Jackie McGuire

>> So I started my career accidentally in finance, didn't know what a mutual fund was, and applied for the wrong job at Fidelity. And turns out I applied to be a stockbroker, thought I was applying to process payroll. So I did that, became a registered investment advisor.

Savannah Peterson

>> I thought you just got it and ran with it then. Go for it.

Jackie McGuire

>> I mean you'd be surprised with 72 hours in an internet connection how much you can BS an interview. No. So I became a registered investment advisor and moved over to Silicon Valley Bank and did that for CFOs for a while. And then I wanted to do something different. So I found UC Berkeley had a data science boot camp, and I thought people who can actually explain data are kind of hard to find. And one of the things that I had found was one of my strengths in my career was being able to explain things. So I did the data science boot camp, built this machine learning model that could judge a book by its cover and a data scientist at Adlumin, which is a SIM, just happened to see it and reached out and said, "Hey, I'm looking for kind of our first algorithm hire. Would you be interested?" And that's how I got...

Savannah Peterson

>> How long ago was this?

Jackie McGuire

>> This is 2018.

Savannah Peterson

>> Oh, that gives me shivers. You just went for it. That's not even that long ago.

Jackie McGuire

>> Yeah, yeah. No, it wasn't. And I think there's a lot of things. Everything in life is risk management. Every decision you make is risk management to hopefully stay alive. So going from finance to security, while there's a lot of terminology and things like that to understand, it's all kind of the same game. It's how do we manage our risk? How do we get more out of every dollar we're spending? That kind of thing. So it was a leap, but I love it. And I have to say that security is the industry I found where I can be the most myself of any industry I've worked in, which is always nice, because I'm a little neuro spicy.

Savannah Peterson

>> We're sitting next to some neuro heat right here, so I love that. I'm here for it. So I want to step back because that's so cool. So when we're big fans, we go to women in data science WIDS every year and it is very passionate for us. So I love that you learn this skill. Ladies, if you're listening, it's not too late to learn data science. Anyone listening, it's not too late just for the record, but you're such a great example of it. So you go and learn, you're the first algorithm hire, which is rad in itself, judging books by their cover quite literally also super cool, I'm into that. Talk to me about the journey from then in 2018 to finding Cribl, because I know you were on the research side as well.

Jackie McGuire

>> So working at the SIM, I found out that I'm a pretty okay data scientist, but a lot of the time I spent as a data scientist was just trying to wrangle data. I was like, oh, I'm going to write all these cool algorithms. And it was more like the first three months was a text file of Syslog and Sysmon and just trying to Regex it into a JSON dictionary. So when I was looking for a change kind of coming out the pandemic, I posted on LinkedIn, what do I do? I have all this different experience. I really like security. And one of my husband's good friends who he played rugby with, Justin Lam from 451 Research, S&P reached out to me and said, "Hey, you would make a great research analyst." So I did that, which is how I met Cribl. And when I met Cribl, I was like, you mean I don't have to use Regex anymore? And that was literally like, as soon as they said, we can do the Regex for you or get you 90% of the way there, I was sold because it's such a... Data wrangling is one of the most painful parts of being a data scientist. And just looking at the toolkit Cribl had and kind of the direction that things were going in terms of data volume and needs. I was like this company is doing something different. And being vendor-agnostic also really resonated with me. I'm a big open source proponent. I think if you look at what our adversaries are doing, they open source everything. They work together. There's no barriers to entry, there's no licenses...

>> No governance.

Jackie McGuire

>> Yeah, no governance.

>> Fire everything.

Savannah Peterson

>> Also that. Yeah, also that.

Jackie McGuire

>> So coming from finance, a little bit of governance would be good, but really if you think about it, if you want to beat your enemies, Art of War, you have to think like you're enemies. And so I was really impressed with their commitment to staying open source or staying vendor-agnostic, working with everybody open sourcing some of the products that they had. And when they reached out, it wasn't a tough decision. I do miss being a research analyst, but this is fantastic.

>> So you saw Cribl basically... Because they came in from more of that observability side because where their roots were, but you saw them as a bigger opportunity and you mentioned the adversaries, they're just all in. They don't need governance, just fire everything. And the world has gotten more complex. One of the big conversations we have in theCUBE is complexity is getting more... It's more complex every day and the data volume is growing. So you've got complexity in platforms and tools and more massive data volume. So this is a field day for the hackers, I mean for the bad guys. And Cribl, what does Cribl do to solve that? Because that's a big, big part of the theme I'm seeing with Cribl. What's the update on that? Because every enterprise is growing exponentially with the data. It's not getting less complex.

Jackie McGuire

>> So the last figure I saw is data is growing at something like 28% compounded annually and most security budgets...

Savannah Peterson

>> Wow, that's a lot actually.

Jackie McGuire

>> Yeah, it's doubling every couple of years.

Savannah Peterson

>> That's a great data point. Holy moly, that's wild.

Jackie McGuire

>> And the first talk I wrote as in this role at Cribl was called Security is a Data Problem. And that's the reason that without trying half of Cribl's users were using Cribl for security when I met the company, is that our ability to generate data has completely outstripped our ability to make sense of it. And so if you think about 15 years ago, if any of you were in the enterprise then... I'm dating myself.

Savannah Peterson

>> Don't worry. You're sitting here with...

>> has been around for 15 years, so we were here. Yeah, we're here.

Jackie McGuire

>> You were lucky to have maybe a desktop at your office and if you were super fancy, you had a Blackberry connected to the internet. And now if you think about the number of things in the enterprise that are connected to the internet, all of which need to be monitored because if you lock a door, a hacker is going to climb through a window. We have hackers coming in through the automation systems for air conditioning. If you look at the target breach, we have every...

Savannah Peterson

>> Oh, wow.

Jackie McGuire

>> And so the number of things you need to monitor has exploded the number of... And then the amount of time you have to hold that data. So one of the other interesting things we're seeing is dwell time has tripled in the last 10 years. And so hackers are being very patient. They'll get into your systems and they'll wait for 18 months, two years, as long as it takes. So the amount of data you have to retain has also really exploded, and regulatory requirements are only going to make that worse. So what really impressed me is that Cribl was building this kind of Swiss Army knife for data, and it was really a user led development approach where it's a product built by users for users, and they were looking at where can we remove friction in the data process? And that's really what it's all about is how do you make better quality data and remove friction so that the data is where you need it, when you need it.

>> What's the core problem that Cribl solved? Because originally it was data volume, move it around, getting things set up, wrangle it. Is there another problem that you guys are eyeing that you see that you solve that companies are seeing that's a little bit broader? Can you share us the update on the problem that the customers have?

Jackie McGuire

>> We solve a number of problems. I think the initial problem that Cribl was solving was how do you get data from where it is to where it needs to go in the format that it needs to be in? And so as an economist, one of the things that you've seen in the last 25 years is venture capital and private equity fueled this proprietary platform explosion where they didn't want to invest in anything that wasn't defensible, but the IP...

Savannah Peterson

>> A 100% these weird silo channels.

Jackie McGuire

>> So it was almost this venture capital fueled silo farm.

Savannah Peterson

>> Totally.

Jackie McGuire

>> And so the first...

Savannah Peterson

>> That's a good call out....

Jackie McGuire

>> problem we really solved was kind of get that data in, process it and get it where it needs to go and only the pieces of it that need to get there. And now we've moved more towards storage and discovery, because that's really the next problem that we see. So we launched Cribl Lake, which is a managed lake product that literally within a minute you can spin up a lake without having to put a ticket into IT...

Savannah Peterson

>> For a minute.

Jackie McGuire

>> Yeah.

Savannah Peterson

>> That's impressive.

Jackie McGuire

>> And all of the identity and access management pushes down from Cribl Cloud. So it really removes a huge hurdle to establishing object storage. And then Cribl Search, which is our federated search product where you can go out search data at rest where it is, and then only pay to pull back results. Because one of the really inhibitive things when you're trying to respond to an incident is if you've got to pull eight terabytes of data in to find 24 kilobytes of data, it's expensive and it takes a lot of time. So Cribl Search was the other like, let's figure out how we make searching more efficient. How do we search multiple clouds? And we have a lot. And then I think if I think about where I think we're going, we've developed a lot of really great tools for our customers, and now we need to help them prioritize how to use them. So somebody hands you a Swiss Army knife and it's like, great, but which piece do I use first? How do I figure out where we're at? How do I figure out where we're going? And that kind of guidance is I think where we're going.

>> I want to get into how does an organization implement those

Jackie McGuire

>> Definitely....

>> problem? Because we just had our Supercloud 7 event in our studio last week, and one of the things that came out, it's interesting how the conversation and data changes. The hottest conversations are open table formats, governance and cataloging, because the data is being separated from the computer and the database. And that's a whole nother conversation. What's the role of the database? But we don't want to go there. Open table formats, cataloging, and then intelligent applications with generative AI that'll be native to the apps which is coming. So that means every company wants to modernize. They have to figure out, how do I get a comprehensive scalable data strategy that enables the apps? And that seems to be the high level boardroom that's dictated down to the platform engineers and the data science teams, the analytics teams, and then... So how do you guys walk into that environment with Cribl and say, look, we're here to modernize you, because we got a tool, does this, but you have a bigger problem.

Jackie McGuire

>> We don't say that.

>> Well, they do. Well, they do.

Jackie McGuire

>> So one thing is I am a people watcher, so I've been observing for a couple years, and one thing vendors do is they walk into a customer and they say, you really ought to be driving a Ferrari and we sell all of the parts you need to build a Ferrari, so just buy all the parts and then build a Ferrari. And the customer is looking at them like, that will be great, but I'm driving a '98 Corolla that I inherited from seven different people and I have to swap parts out as we're driving, and if it breaks down or I have to pull over, I lose my job.

>> And then people don't work here anymore.

Jackie McGuire

>> Yeah, exactly.

Savannah Peterson

>> That's great a analogy, honestly.

Jackie McGuire

>> And so what I said is I was like, well, let's help customers segment data into the different stages of collection and processing and all of that, and then let's give them what I call a maturity model to assess where they're at, because that's really what you need to do. Being a financial advisor during the financial crisis was all about helping people continue to make progress and continue to make good decisions even when they're scared, even when there's so much information, you can't absorb it all.

Savannah Peterson

>> That confidence, confidence in the chaos is essentially what you're providing.

Jackie McGuire

>> And if you don't give people the best first step, they'll do nothing, because there's a 100 steps and thinking about all of them is just...

Savannah Peterson

>> It's overwhelming.

Jackie McGuire

>> Yeah, you shut down. And so that's what we help customers do. We help them look at where are the different places there are friction in your enterprise, and where are the different stages of data movement, processing, storage where there's low-hanging fruit or there's really obvious need for improvement, whether it's data collection. Are you using a 100 different agents that are all manual? You have no fleet management, whether it's storage. Are you basically making a choice, either we're going to store it in our SIM or we're not going to collect it at all, which we see a lot. So really helping customers figure out where do I start and then how do I keep progressing?

Savannah Peterson

>> How do they know they're on the right track?

Jackie McGuire

>> That's where the maturity model comes in. So my colleague, Ed Bailey and I developed this what we call a data maturity model, which essentially shows you what are the signs of being in each stage of maturity? And that way you can say, I'm not trying to get from no visibility to everything is AI powered and automatic. I'm trying to get from no visibility to some visibility. And so kind of breaking it down into steps, not just a beginning and a destination.

Savannah Peterson

>> And then you get to feel that progress too, because it can be a pretty overwhelming journey, I would imagine.

Jackie McGuire

>> Yeah, because what I found in finance was that when people make decisions when they're scared or they feel like they're being reactive, even if they make the right decision, they don't feel good about it. So if you can help people feel like they're being more proactive and you help them instead of saying, your SIM is really expensive. Say, you spent good money on this SIM because it does a lot of really good things. Let's give it the best data we can. That helps people feel better about the decisions they're making and feel more proactive, and ultimately they'll continue to make better decisions.

>> You mentioned open source before...

Savannah Peterson

>> That will make you a loyal customer.

>> You mentioned open source before. I want to get your thoughts on customers sometimes have the right intentions, they want to do the right thing, but they get stuck in the mud and they kind of hit a wall. And based on your observation, what are some of the reasons why is that? Because sometimes it's either they think they're doing things, they're spinning their wheels, but it's not actionable, it's not moving the needle. Where do people get stuck and what's the needed to get them on track to your question?

Jackie McGuire

>> I think I see a few things. One is that when you talk about terms observability and security and things like that, on the vendor side, we put these different things in very neat boxes. On the enterprise side, a lot of things like observability isn't a job, it's a set of things that have been fragmented across like 50 different people in an organization, and security is very similar. And so I think one of the places you find friction is competing priorities or competing interests. And so there's often a failure to align the department or team initiatives with the initiatives of the group as a whole, and then the initiatives at the executive level. So I really find that helping take a step back and say, how does this project tie to the next step layer up in the next layer up, being neuro spicy...

>> I love that term.

Jackie McGuire

>> It's not always natural for super technical people to understand explaining things at a much higher level. A lot of times I love to data dump, so I'll come in and be like, let me tell you about JSON.

Savannah Peterson

>> Let me tell you about one of these details. Let's get into it.

Jackie McGuire

>> Yeah. Let me tell you about Cribl's best pipelines and exactly what they do. No, but one of the best analogies I use for Cribl all the time is we're kind of like commercial plumbing for data. So rather than just having one shutoff valve for your whole house, we give you controls everywhere data, starts or meets other data stops.

Savannah Peterson

>> Just one of those .

Jackie McGuire

>> Yeah, exactly. So if your toilet leaks, you don't turn all of your water off. And that's being able to come up with analogies, higher level strategic messaging. Those are the tools that we find that users and technical champions really need to be able to advance projects with higher level sponsors. They're executive sponsor, board of directors, if it's a huge initiative.

Savannah Peterson

>> It's a lot about communication is what I'm hearing.

Jackie McGuire

>> Yeah.

>> It's interesting you mentioned the plumbing, commercial plumbing. If you think about the data trends, it's been data science, dashboarding, analytics. It's a database industry. If you were a data person when I was in the industry and as a data person, it was all about the database.

Jackie McGuire

>> Yep.

>> And then you had data warehouses, then the cloud data with snowflake and data breaks in the lakes. Now you're seeing engineering and real architecture to your point around thinking about holistically an organization and saying, this isn't a data scientist coming in and saying, we have a problem. No, this is a platform thinking, systems thinking. So you have data engineering going on and still growing analytics. How do you see that rationalize for organizations? Because one, they're growing, some get automated, some don't, but you have to do that plumbing kind of... You got to lay it out. Where does it go?

Jackie McGuire

>> Well, so I think one of the really interesting things right now is we've spent the last 20 years telling people all your data is in silos and that's a bad thing. You got to break the silos down, put all your data together. Well, to your point now we're starting to see every enterprise that's connected to the internet wants to use generative AI. Well, guess what? Everything that comes out of generative AI has to go in a bombproof silo. Because if your generative AI accidentally eats that for training, it's going to do what's called model collapse, which is a very expensive process by which your AI gets dumber and dumber and then just stops working.

Savannah Peterson

>> It drives itself into the ground.

Jackie McGuire

>> Yeah, yeah.

Savannah Peterson

>> Model through a self-learning process, which is even worse.

>> What's it called again?

Jackie McGuire

>> It's called model collapse.

>> Model collapse.

Jackie McGuire

>> It's essentially a snake eating its own tail. And there was a really interesting case where this happened with ChatGPT because Quora, the answer website used ChatGPT to generate a whole bunch of answers to questions, and they've got Baller SEO. So their answers always show up like top three...

Savannah Peterson

>> They do.

Jackie McGuire

>> Well, guess what? ChatGPT trains on Google results. So if you start training on a whole bunch of answers that you generated, ChatGPT 3.5 had some problems for a couple weeks and they pushed four out to fix that. So I think the entire way we think about data in the enterprise has to change. So to me, data is an asset. It needs to have a value both as an asset and as a liability. We need to understand the risk associated with that data as an asset. And it's difficult because the value of data is dynamic. Data from nine months ago might seem worthless until you find out somebody breached you 10 months ago and then all of a sudden it's invaluable. So I think we really need to one, help technical champions be more strategic in their thinking and communication, understand strategic thinking. And then two, help executive level champions, CISOs and CEOs really understand how the whole business benefits from maturing the security function.

>> It's interesting you mentioned about the silos, vaulted silos or bombproof silos. Also, centralization is coming back too, data led, so centralization and silos, but that could be distributed. So I want to get your thoughts on this because we're seeing generative AI, it needs data. So silos traditionally tend to be slow, lower, higher latency retrieval. So how are companies thinking about making data available but also protected? Because you have to do both. That is the secret, the security paradigm, because if the generative AI app doesn't get the right data, it doesn't do the right thing. So you have model failure on one side, but also data availability is the other one. What's your thoughts on that?

Jackie McGuire

>> And I think we're seeing a lot. Originally the first kind of year of ChatGPT was a little crazy, because we had a lot of people putting sensitive data into ChatGPT through their personal accounts. So what we're seeing now is a lot of large enterprises are bringing those models in-house. So there's a number of different companies who produce proprietary large language models where you can either on a periodic basis retrain them from those slow databases, or you can do what's called retrieval augmented generation, where you put some of the really key nuggets in a more performant database. And then as you ask questions, it hits that performant database for the stuff that you don't want to have to train on a regular basis. And I think that's what a lot of companies are doing to help that. I also was just reading last night about there's a couple new technologies, Python packages out there to help... The small nature of those data pools makes them a little bit more fallible than a really large language model, so helping them make better decisions, helping them prevent from hallucinating. But to your point, it's a constant push-pull of how do we give the model enough data that it can give good advice to our employees, but not so much data that someone can accidentally a session token out of it, which we've seen.

Savannah Peterson

>> Which is a fine line. That's a really good point.

Jackie McGuire

>> Well, and the session token is kind of anecdotal, I'm not going to mention which extremely large technology company. But one of their data science teams pushed to a public GitHub of data science model that they had inadvertently put full machine backups in with unhashed session tokens. And so if even the largest companies in technology who are building these things are having these problems, obviously the smaller companies are struggling as well.

>> It's interesting, all these process problems that should be handled. And also it's come up a lot here in the hallways in this event. A lot of the systems were built by people don't even aren't around anymore. So nobody even knows what the config files are looking like or the metadata or the.

Savannah Peterson

>> It's legacy. That's pointing. Exactly.

>> Who did that? Who runs that cluster?

Jackie McGuire

>> I wrote detections for those old core banking servers, the cobalt ones that are in the corner of a credit union where if somebody shuts the wrong light switch off, your banking app just stops working. And I think there's kind of a lack of awareness that we've got all of these legacy technologies that are just piling up and without some strategy for how do we sunset them, or how do we make sure that we have a plan going forward. To your point, there's also a ton of key person risk in technology, which is a really big focus in finance of if this person gets hit by a bus...

Savannah Peterson

>> Oh no, it's always the analogy.

Jackie McGuire

>> But I've never heard key person risks discussed in a security meeting at all, ever.

Savannah Peterson

>> Really?

Jackie McGuire

>> Yeah.

Savannah Peterson

>> That really surprises me, given the level of... I mean encryption and security and everything, it'll be going around any activity there. It doesn't even matter if we're talking about LLMs.

Jackie McGuire

>> And it's just crazy to think it's just not a thing we think about, because we just assume that everything we do is backed up to a Git or something like that. But no, it's not as much of a focus.

Savannah Peterson

>> . I mean, it's just the nature of the beast. If someone is going to be more intimate with that data or that process or whatever, naturally. Wow.

Jackie McGuire

>> I think if you've worked in security long enough, we all know someone who's gotten recruited back to a previous employer, because something broke that nobody but that person could fix six or seven years later. I know three people I can think of off the top of my head who in the last two years have changed jobs, because something broke that they built that no one else could fix. And it's just not tenable, not with...

Savannah Peterson

>> It's so .

Jackie McGuire

>> Not with data growing at 28%.

Savannah Peterson

>> 28%.

Jackie McGuire

>> Yeah.

>> , maybe to the rescue. Jackie, it's been great to have you on theCUBE. Really appreciate you. Just the final question is we're seeing the research from our team, product sprawl still out there. There's still holes to fill, but there's a disciplinary approach to the portfolio of products and risk management. Integration interoperability is very key. You guys kind of play that plumbing area you mentioned. So as the product sprawl will continue, there is a platformization rethinking going on. How is Cribl solving that problem? What's the Cribl value proposition? End this with a quick plug for Cribl and how you solve all those problems.

Jackie McGuire

>> So I wrote a paper probably a year and a half ago on cyber security mesh, and think that's really where we're getting to is that pipes is not enough any longer. You really need a mesh that interconnects every system with every other system. And so Cribl is we're the mesh. We're the pipes between all the things that can take the data from where it is to where it needs to be. And so I think as we think about things as having to integrate security into the data science development process, Cribl really is the platform that can take data from one team, format it into formats that several different teams care about individually and send them the right streams of data that they need. And unless we are all sharing data within the enterprise the way attackers do, I don't think we're going to solve this problem. So that's what I see as being a huge opportunity in the future for us, is unlocking the value of people's data.

>> And the results, you guys are doing extremely well. You're in the top handful of all time growth companies in tech.

Savannah Peterson

>> Fourth largest, fourth-fastest growing infrastructure company in history.

>> History. That's huge.

Savannah Peterson

>> Amazing.

>> Well, we knew Clint before he was all famous and everything. Now you guys are doing great.

Savannah Peterson

>> We've been fans for a long time over here. We're OG.

>> He's followed theCUBE growth and congratulations to you and your team.

Jackie McGuire

>> Thank you.

>> The scope is broadening and you guys have a huge opportunity. Looking forward to it.

Jackie McGuire

>> Check us out. We've got tons of free tools and resources on our website.

Savannah Peterson

>> We also have sweet lightsabers that just need a shout-out, because they've been on our swag segments at , so shout-out...

Jackie McGuire

>> Have the best swag, so come find me at our booth and I'll hook you up.

>> Great winner on theCUBE swag segment for Savvy Swag. Thanks so much for coming on.

Jackie McGuire

>> Thank you.

>> Thank you for sponsoring us and to tell the team we're really appreciative to allow us to do what we do. And thank you so much.

Jackie McGuire

>> We love the work you're doing.

Savannah Peterson

>> They're lucky to have you.

Jackie McGuire

>> Oh, thank you.

Savannah Peterson

>> Genuinely. I'm so impressed. I don't say that always sitting here. I'll be honest about that.

>> Neuro spicy here on theCUBE. I'm John Furrier, just sitting here hanging on in theCUBE. Thanks for watching. We'll be right back.