 Hello, welcome to this CUBE Conversation here in Palo Alto, California. I'm John Furrier, host of theCUBE. We're here featuring Cribble, a hot startup at cloud security and IT security startup. We're here with Clint Sharp, co-founder and CEO, friend of theCUBE, CUBE alumni. Clint, great to see you. John, it's great to be back. So exciting to have you guys featured again in the AWS startup showcase. Again, for your awesome performances as your startup continues to grow and kind of grows and grows. Soon you won't be a startup anymore. You'll be a public soon or get bought out for massive amounts of money. You guys doing great. All kidding aside, IT and security are coming together. It's clear. The new IT is all about DevOps starting to see much more guardrails, words like guardrails for AI, guardrails for shift left in the DevOps pipeline. This is a big part of the data market. You guys have been pioneering a lot of new stuff. You got some new capabilities. Talk about the current state of Cribble, where you guys are at and your role in the cybersecurity landscape. Yeah, for sure. And I do agree, IT and security are coming together. Although I don't know that they would agree. And for sure, I think that both sides tend to use different verbiage. They tend to have very similar problems. And this is why we call ourselves the data engine for IT and security is that the core of what we do for our products, they're looking at the same type of data. They're asking the same types of questions, but they talk about it very differently. And so you really have to go to market very differently for both of those sets of personas. You have to talk to security people like they're used to being talked to. And you have to talk to IT people about how they're used to being talked to. But ultimately, they have the same challenge around data growth. Their data is growing into 25% keger. Their budget is not. How do I store and retain the right amount of data that my enterprise needs? How do I find out about problems with my applications quicker? How do I find out about security threats quicker? And ultimately, they're all dealing with the same sets of problems. On our last SuperCloud event, we've got SuperCloud 3, we've got SuperCloud 4 coming up, which is all about cloud meets data and AI. It's kind of a cool kind of new show or a program event we're doing for audiences. And we had a VC on, loved this quote. He said, you know, securities about protecting the cheese, you know, the bad guys want to get the cheese then the mouse wants to get the cheese and data is the cheese, right? The cheddar or whatever you want to call it. And so data protection is huge, right? So the question is, people are kind of thinking, if I store more data, there's more to be attacked, more to protect, or do I have a different strategy? In cybersecurity with AI coming, more data might be a benefit. So, you know, there's a lot of kind of approaches that are being challenged, methods are being challenged, processes that are being challenged by what's going on today in the architecture and the potential for AI. Is this a new challenge for security teams? Or is it more of the hype is a little bit further out there? What are some of the trends you're seeing around this data store? Do I store everything and just everything versus throw it away and get rid of it? So it's not attacked, but I can't use it for AI. What's your thoughts on that? What's your vision? And so I think part of what we're trying to educate the market about is that there are actually just many different classes of data. And so your business data, you know, the, for example, should you keep your email for seven years? I probably wouldn't. Should you keep all of your business transaction records? Well, you're probably, you know, regulated to like the, you're required to keep all of this data. And then the other class of data is the data that we work on. So we work on data that's telemetry data, log data, metric data, tracing type of data. And that is in a wholly different, very large class of data, very diverse data. And for that, you know, you can't ask questions of data you don't have. So really it becomes a question about your business requirements. If you need to go do a breach investigation, how far back do you need to go? If you want to understand your application performance, how much, you know, how much data do you want to be able to see how the application was running a year ago? If so, then you probably need some amount of data that you're retaining, maybe not the full fidelity. And so each of those classes of data, we have to answer these types of questions around retention, you know, based off of that particular business's requirements. Yeah, and that's a huge thing because that means the AI will work for you in the future, but also you got to have that position for the security teams. Is there any standards you're seeing around automation because often automation gets confused with AI? I know we were talking before we came on camera about the efficacy of AI, but certainly it's hyped up. But automation and the roles that are available, you can't always have people to scale up, if you will. You got to leverage some of the technology. What's your view on the skill gap and or roles of people in these socks and or the security teams? Yeah, and I think this is one of those areas where IT and security are actually very different. So, you know, in the IT area, you see the emergence of, you know, Terraform and a number of kind of open source offerings for managing machines. And then security automation looks very different. And so there's a whole market called SOAR, which has been around now for, you know, eight to 10 years. You know, it's pretty successful. You know, we see it in a lot of security operation centers, but very, very hard to implement. And it's interesting because it's not hard to implement because the software is difficult. It's because it sort of depends upon whether the security operation center really has good run books to begin with. So in order to automate something, you have to have a written down process. And so this is often a forcing function for the security operation center to say, like, do we always do this the same way? Do our tier one guys actually follow the same process every time they get this type of alert? And, you know, I don't know that AI solves that either, right? If you don't know how to, you know, go troubleshoot this particular security threat, you know, AI may be able to help you based off of what they've trained their models on. But, you know, ultimately we have to, you know, humans are at the start. The human has to know what to go do. And then we can figure out how to go make that run faster. Yeah, I think the human piece is huge. Good point. So let's talk about interoperability and collaboration. You guys are in the middle of a great conversation all the time around security teams, deal with multiple vendors, how do things interoperate, how do teams collaborate, kind of in that spirit of work and also multi-vendor, you know, a lot of telemetry tools out there in platforms. Yeah, and this is a huge challenge for our customers because they really are coming up with tiered strategies for managing all of this class of data. So, you know, they've got a premium tier that they're using for the data that's the most security relevant. You know, sometimes they'll have two or three other tiers that they're using for managing bulk data, for managing compliance. And then they also have, you know, new tools that they want to onboard. So they, you know, they have a great STEM and that they want to try user behavior analytics. And all of this is really the same type of data that they're looking at, a lot of it's log data. And then they have an agent fatigue problem while every vendor comes with their own agent. So therefore, I'm loading up three, four, five agents on an individual machine in order to get really the same data to a bunch of different tools. And this is where really we've come in to help with our stream product is helping them route data from a single agent out to multiple places. And we support pretty much every agent in the business. So they, you know, they can be agnostic as to what choice they made before. And then they can enable this tiered strategy. They can send data to a data lake. They can send data to a logging engine. And it's really giving them choice and control for the future because a lot of times if they want to go try another vendor, that's like a multi-month long process of deploying software out to thousands of endpoints. And we really give them the ability to just flip a switch and try another vendor, try a new technology, try a new way of finding security threats, which ultimately help the CISOs do what they need to do which is secure the enterprise against a bunch of people who are trying really hard to bust down the doors and do bad things. Clint, talk about your business, your business model, your customers. How are they using you? Why are they buying you guys? Why are they using your stuff? What's, take us through a little quick of the business. Yeah, for sure. We were one of the fastest growing enterprise software companies in Silicon Valley, I think for a reason because we really live by our customer's first value about providing value to the customers. We sell primarily in the upper end of the market. So we tend to be, you know, at brands that you would know, you know, logos that you're, that you probably do business with and, you know, you'll see them advertising during Super Bowls and things of that nature. So we're very comfortable in that enterprise space where I think a lot of startups, you know, aren't that they're kind of learning the enterprise but we came out of the enterprise and we know how to make those customers successful. And ultimately, you know, what we're giving them is in a world where, you know, budgets are constrained and they need to add more capabilities and they need to do more with less. That's certainly what we entered 2023 with a mission to do. Nearly all of our customers are budget flat or budget down and they're trying to figure out how they can do more with less. We help them eliminate noise and waste. We help them, you know, control their costs. We help them give them choice about their vendor relationships and their data storage and where they think that the best place to put each class of data is. And ultimately, you know, the value is crystal clear. That's why our customers continue to grow with us. You know, even after we, you know, do our initial transaction, they continue to buy more and more for more use cases and more value and our customer satisfaction is off the charts because we're really aligned with making customers successful. The enterprise is hard. I mean, you know, the old days of the enterprise game was you solve complexity by adding more complexity. Now with the cloud, you can't do that because you have to make things simpler, right? You got to make things faster, easier to work with. Talk about the success criteria in the enterprise. What it takes to be successful. I should love the customer first. That's always a starting point there. It's I'm North Star, I get that. But what's going on with the tech that makes things now possible not to be more complex, you know, getting a side, that's the key right now. You got to make things easier for the customer. Yeah, well, I think it's interesting. I'll actually flip it around a little bit and say it's not so much about the tech. And I think one of the things I say often internally is, you know, software is a people business. I think a lot of people think it's a technology business, but especially in the enterprise, you know, software is bought on relationships. Do I trust that person? They work for a company, but a lot of times they've had a relationship with a salesperson, with a sales engineer, with a customer success person that span multiple companies. And so they understand their enterprise. And so, you know, we've tried really hard to build a great team of people that knows that it's not just about the tech, but of course it is about the tech too. And I think that, you know, where we're really unique is helping them, you know, get value into matter, what else they already have. So we're not coming at them saying, hey, the way that Cribble helps you is just rip out everything you have and put all my stuff in. And then you'll get this value. We come to them from a perspective of, hey, I can provide value with everything you already have. And that message really resonates with customers. Give some examples of some customers where you solve some problems. Take us through the day in the life of the value proposition. Give some use cases or customer examples where Cribble's at in action. Yeah, for sure. And I'm close to, you know, a number of the ones that we can talk about publicly and not publicly on the cloud side, you know, Autodesk has been a customer of ours for a really long time. And we've helped them a lot of ways over the years. We've helped them with their cloud migration. So moving from, you know, an on-prem logging solution to a cloud logging solution, which is, you know, often a very long, difficult project that's made a lot easier with our streamage processing and routing technology. We've helped them enable a portfolio of tools and be able to put the data into multiple places. And they've made many different choices over the years and they've brought new vendors in and then they've, you know, they've changed vendors, you know, picking the right thing at the right time for them. And then, you know, we are in some of the largest financial institutions in the world. We are in some of the largest online services companies and these people are dealing, I wish I could name them publicly, hopefully soon we will be able to, but they're moving hundreds of terabytes of petabytes of data per day. And, you know, a large data warehouse is a petabyte and we know of customers moving a petabyte of log data per day. The scale of these operations and the cost of these operations is in the tens of millions of dollars. And for that it starts to become board-level material how they're managing this data and we're really helping them figure out the right data management strategies for 2023 that will help them, you know, grow their enterprises over the next decade. It really is a testament to your customer base, how it's evolved during your rapid growth. Congratulations. Clint, great to have you on. The final couple of minutes we have left. What's next? Where do you see the industry heading? As CEO, you've got, you're the captain of the ship. You've got a great team. Got some key trends happening. You've got relationship with Amazon Web Services but cyber is changing again. More data, more tax, less budget. Do more with less. Enterprise is hard. Large scale is hard. Large scale data is hard. What do you see the industry going next? What's your vision of the major trends emerging? Yeah, so one of the ones that I've seen personally that I thought has been very interesting is, you know, kind of everybody's on their cloud journey, some portion away on their cloud journey. And so, you know, we work with a lot of very large enterprises and they have huge investments in data centers. But even the most staunchly on-prem organizations that we've worked with are starting to come to the cloud for some portions of their workload. And I think that's really interesting because just in the last couple of years, it was people who, you know, three years ago when I'd meet with them, they'd be like, no way. We're never going cloud to now, you know what? We're moving some applications there. We're starting to do some work. And I think for us, because we're very specifically really in the logging industry, a lot of the first workload they're moving is logs. It's huge volume. It's very expensive for them to store. And so they're utilizing things like AWS security data lake, or just in general S3 storage and cheap storage that's sitting in the cloud to go move petabytes of data as some of their first use cases. And so I think that's really interesting. It's just, it's been a pattern that I've emerged or that I've I've intuited sort of from talking to a bunch of very large enterprises that, you know, I think, I think we're very early still in the cloud journey. And I think there's a lot more to go. And it's really starting to happen, you know, much faster than I was expecting. It's so awesome to see that custodial or storage mindset go to value, right? I mean, you've been in this for years. This has been a big data dream logs. There's a lot of value in there. A lot of important data. Huge. For sure. And, you know, for the enterprise, understanding, you know, John's experience with the application, understanding whether there are malicious actors coming after them. This class of data, super, super important for IT and security teams in order to meet their missions. And so figuring out how to store it cost effectively is kind of the biggest challenge that they're facing. And we're happy to be helping them with that. I love the new architecture. Love what you guys are doing. Clint, thanks for coming back on theCUBE. Really appreciate the update. Clint Sharp, co-founder and CEO of Cribble Fast-Growing Startup in a great position. Thanks for coming on and have a great day. Thanks, John. Okay, I'm John Furrier in theCUBE. This CUBE conversation with the hot startups, the news makers, the leaders in the industry. Thanks for watching.