Varonis Systems at 45th Annual William Blair Conference: Data Security and AI

On Wednesday, 04 June 2025, Varonis Systems (NASDAQ:VRNS) participated in the 45th Annual William Blair Growth Stock Conference, showcasing its strategic initiatives. The company highlighted its transition to a SaaS model, the role of AI in data security, and its robust financial outlook. While Varonis is optimistic about growth opportunities, the evolving threat landscape presents challenges.

Key Takeaways

• Varonis is accelerating its transition to a SaaS model, expecting 80% of ARR from SaaS by year-end.
• The company is investing in AI-driven data security solutions to address rising data vulnerabilities.
• Varonis aims to surpass its $1 billion ARR target by 2027, driven by strategic investments and market demand.
• The platform’s focus on automation and threat detection differentiates it from competitors.
• Varonis’s managed data detection and response service enhances customer value and renewal rates.

Financial Results

• SaaS transition, initially projected for five years, is now expected to complete by the end of 2025.
• SaaS mix guidance was raised from 78% to 80%, with a 25-30% revenue uplift compared to on-premise models.
• ARR contribution margin stands at 17%, with a target of 20% by 2027.
• Free cash flow is projected between $120 million and $125 million for 2025.
• The company reaffirms its $1 billion ARR target by the end of 2027.

Operational Updates

• The platform identifies, locks down, and monitors sensitive data to prevent threats like ransomware and AI abuse.
• Varonis emphasizes the importance of data protection across three dimensions: data importance, access, and usage.
• The managed data detection and response service provides anomaly detection and response SLAs.
• The platform automates risk mitigation, such as excess access and misconfigurations.

AI and Data Security

• AI tools like Microsoft Copilot highlight existing data security challenges, increasing demand for Varonis solutions.
• AI’s role in exposing data vulnerabilities is a catalyst for Varonis’s growth.
• The company is positioned to address AI-driven data security challenges.

Future Outlook

• Varonis identifies a larger market opportunity beyond its initial projections, driven by AI and data security needs.
• Shortened SaaS sales cycles and improved renewal rates are expected due to the SaaS model’s stickiness.
• The adoption of AI is anticipated to drive demand for Varonis’s data security solutions.

Q&A Highlights

• Competition remains steady in traditional areas, but new markets bring encounters with other vendors.
• Varonis’s comprehensive approach to data security differentiates it from competitors focused on discovery.
• The SaaS model simplifies customer experience, reducing hardware and headcount costs.

Readers are encouraged to refer to the full transcript for a detailed discussion of Varonis’s strategic initiatives and financial performance.

Full transcript - 45th Annual William Blair Growth Stock Conference:

Unidentified speaker, Moderator, William Blair: Alright. Good afternoon.

Welcome to the afternoon of the conference. Very happy to have Varonis here. We’ve got Guy Melamed, the CFO and COO, and David Gibson, the SVP of strategic programs. Before we begin, I’m required to inform you that a complete list of research disclosures or potential conflicts of interest is available on our website at williamblair.com. With that out of the way, we’re gonna see some slides, and then we’ll have some time for q and a and then the breakout upstairs, right afterwards.

David Gibson, SVP of Strategic Programs, Varonis: Great. Thank you so much. My name is David Gibson, and Varonis is a data security platform that’s delivered in a SaaS model. We protect data, and, we kind of I guess the real easy way to think about what we do is we find data that’s important and in harm’s way. We lock it down so that only the right people can touch it, and then we monitor the heck out of it so that we can detect and stop threats to data like ransomware, like external attackers, like insider threats, and like AI abuse.

So I’ll be talking a little bit about how we do that today. Here are some stats. We’ve been doing this for a while. We have many customers that, say wonderful things about us in in addition to having good analyst feedback because of the mileage that we have. So this is the way that I try to explain what Varonis does when I’m talking to new customers and existing customers that haven’t seen us for a while, and I, in my role, get to talk to our larger customers.

And this is kind of the way I walk through what Varonis does and the value that that we help people get. So Varonis has been protecting data since we a while back. It was before data security was really a thing that anybody talked about when people talked about security back then. It was endpoints and perimeter, and a lot of that people have. And I think people have realized that all of them are usually in service of protecting data.

If an end user gets phished or somebody downloads malware, if no data was taken, it’s just another day. Right? But if data gets taken or exfiltrated, then it’s a really big problem. So this is where we started and where we’ve developed our solution, and we’ve learned a lot over the years about protecting data. One of the big things that we’ve learned is that data is very hard to protect when so many people and identities, human or nonhuman, have access to so much data that they don’t need to have access to.

And we have seen this phenomenon wherever we looked, and people have data in a lot of places. And wherever it’s stored, when we do a risk assessment, and that’s the way we sell, by the way, is we look at people’s data through our lens and show them where they have risks. Wherever we’re looking, we see data is open to way too many people. Often, everybody in the company have access to sensitive data. As cloud stores like three sixty five and Google Drive and Box have become popular, end users are sharing without any IT help or oversight, and it’s a bit like a party with no parents.

People are sharing file by file, link by link, you know, with everybody in the company, with external users publicly if it’s allowed many times, and a lot of the data that’s being shared this way is sensitive. In the cloud infrastructure world, it’s very easy now to spin up infrastructure. When I was working in IT and infrastructure, if I wanted a database, I needed to spin up. I needed to rack a server and actually configure the operating system and then load the database and then populate the database. Now it’s just code.

It’s a shell script. So it’s a bit of a party with no parents for developers, right, to spin up infrastructure. SaaS has become very popular. Wherever we look, people typically have more access to more data than they need, whether you’re a human or a nonhuman identity. And so we call this concept the blast radius.

If a user was compromised, if it was an insider, how much data would they have access to? What would the damage be? And we’ve been talking about this in the context of insider threats because the more access that an insider has, the more dangerous they are. We’ve been talking about it in terms of ransomware. Right?

Because if a user gets ransomware, the the bigger the blast radius, the more damage that will happen. External attackers have a much easier job if the blast radius is big. And in fact, we’re seeing that attackers aren’t breaking in these days so much as they are logging in. And when they log in, they compromise an account. They have access to a lot of data.

Right? It makes their job easy. We also now are talking about the blast radius in context of AI. People are starting to realize that most of the enterprise AI assistants use what you have access to to create a response. So when you ask a question of Copilot for three sixty five or ChatGPT Enterprise, it looks at the data that you have access to to generate your response.

So if you have access to more data than you than you need, the chances that you will see data that you shouldn’t see go up that much more with AI. And there are a lot of use cases. I think people are starting to realize that AI is a data security problem. So this is one of the big problems that we see in data security that we help solve. There’s some others as well.

It’s not like people haven’t been doing anything to protect data. They’ve been trying a bunch of different techniques. DLP or data loss prevention is probably in its third iteration now. Many people that I talk to have several scars from going through different DLP projects. By that I talked to are trying to do data loss prevention by putting a label on the files that they don’t want to be leaked, that they don’t want to be sent by email.

They don’t want to be saved to USB key. This is one technique that people have tried. A lot of people are starting to do more discovery projects. The rationale is, well, we better understand where the data we care about lives so that we can go lock it down. And people have also been using the native tools that are inherent in each of these platforms.

But how is that going? When we ask people how their DLP projects are going or how the d Discovery projects have been going, the answers that we hear are like this. Well, if we’re doing DLP, we struggle to get enough labels on enough files accurately enough to actually do any meaningful blocking. So we’re doing not data loss prevention. We’re doing data loss watching.

How’s discovery going? Well, my scans didn’t finish. They’re scheduled. Right? We’re lucky to get an incomplete scan every six months.

Oh, and by the way, we didn’t get enough context to actually figure out what problems we needed to solve. None of these solutions actually monitored what was happening with data, so we couldn’t detect any meaningful threats with data. We don’t hear people get to the outcomes that people want to get to with data security very often unless they have Varonis, which is data is locked down, that blast radius is closed, and it’s monitored very tightly for threats. So how do we do it? Wherever enterprise data is stored and these days, it’s stored in the data center.

It’s stored in the hyperscalers like AWS and Azure and GCP, stored in SaaS applications like Salesforce and ServiceNow and Databricks There are many of them. Wherever it’s stored, we have seen you need these three dimensions in the middle in order to protect it. What’s important, Who’s got access? And who’s using it?

If you don’t have these three dimensions, sometimes you can see a problem, but it’s very hard to actually solve the problem. So if you know where your sensitive data is, the next question is, well, is it locked down? Is it at risk? I don’t know. Who has access to it?

What are the permissions? What are the configurations? Is it masked? Is it labeled and encrypted? We have to actually look at the state of all of these preventive controls in order to see if it’s in harm’s way.

If it is in harm’s way, as we find almost all the time well, actually, every time in risk assessments, How do I fix it without disrupting business? I don’t know who’s using it. Right? So we see very quickly if you have one or two of these dimensions, you need the other dimensions in order to protect data. Now with that, we give and, actually, I’m quoting a recent customer, unprecedented visibility into where sensitive data is, where it lives, where it’s in harm’s way, how to fix it, how it’s being used.

And from there, we automate the outcomes that people wanna get to. We safely lock the data down. We’ll safely apply a label to it. We’ll safely restrict the permissions, fix the entitlements, fix some masking. All of the preventive controls, we can optimize, fix the configurations or what people are calling posture these days.

And we’ll also monitor the heck out of it to spot insider threats, ransomware, malware, external attackers, AI abuse, all, actually without the customer having to do anything with our managed data detection response service. So we will baseline what’s normal, detect abnormal behavior, and be responsible for calling you with an SLA if we detect something. So for example, if we see ransomware, we have thirty minutes to call you. And so this is how we’re actually able to get to the outcomes that people want. With Varonis, data is locked down, and it’s monitored much more closely for threats so people can spot them proactively.

These are the kind of real world outcomes that we’re able to help people achieve. So imagine this in the AI example. A lot of organizations that I talk to are under pressure to deploy some kind of AI, whether it’s Microsoft Copilot or a ChatGPT Enterprise. But security teams can be scared. If I do this, what are people going to see?

Sometimes unintentionally, people can stumble on onto stuff much more easily if they’re using AI. So we’re able to rightsize the access controls, fix the links that shouldn’t be out there, and then monitor it to prevent breach. So to go a level deeper with our visibility, we’re looking deeply into the content. So we’re looking inside the contents of files, of object stores, of databases to see what’s sensitive. We are mapping the permissions and the configurations, the masking, the, seeing the label.

We’re looking at the activity, what people are doing, what files they’re opening, creating, deleting, moving, and modifying, what changes they’re making, what SaaS applications they’re going to, all sorts of telemetry. And we also you may have seen we go very deeply. We actually did a press release on this yesterday about our identity component. We’ve seen as we go outwards from data, understanding the identity layer, understanding who has you know, who what’s a risky identity, what what people are doing, from an identity perspective, both human and nonhuman is a big component here. But these are some of the elements that we have in visibility, which provide a lot of context.

So not just where the sensitive data is, but where is it at risk? How’s it being used? One thing that’s really important is because we see the usage of data, we’re able to keep up with these very large data stores. Our inventory, our visibility is always current. This is something that other solutions aren’t providing.

Right? They do periodic snapshots of where sensitive data is. So because we have the access activity, we’re able to do, we’re able to keep up with the pace of change on even the largest datasets. And then we also because we’re able to do that, we’ve built our solution to look at all data, not just a sample of data. So real time visibility, again, this is repetitive here, but defined.

And then when we have that visibility, we’re able to automatically fix what we found in terms of the risks, excess of access, misconfigurations, labels that aren’t applied, third party applications that might be risky or stale or not being used that people are installing in Azure and Salesforce and things like this, disable stale users, and also delete the data that is not needed anymore, which is often called rot when you’re talking to IT or compliance folks. That stands for redundant, obsolete, and trivial data. So all of these are really optimizing the preventive control set that has been woefully unaddressed over the years, and this is all happening in an automated fashion with Varonis. And then from a detection standpoint, we’re giving people a lens. Often, if a user is compromised, one of the hardest to answer hardest questions for IT and security teams to answer is, did this user touch any sensitive data?

How much sensitive data? What data did they touch over the last thirty days where? We have that activity stream. And just like your credit card company monitors the credit card transactions to detect fraud, We monitor all the data transactions to detect insider threats, to detect ransomware, to detect AI abuse. We are monitoring this.

Our behavioral models are firing if we see something that is looks like a deviation that’s interesting. Our MDR analysts combined with AgenTik AI are triaging the events, investigating, and calling if we think there’s a real breach in progress, a real incident that you need to know about. All of these use cases are very relevant for AI. AI is the new salsa. It makes data taste better.

It goes well with everything. But, we’re seeing that, there’s multiple use cases for AI. People have started with copilots like, three sixty five, ChatGPT Enterprise. There are many of them out there. People are worried about data being exposed through AI, so they’re looking to shrink the blast radius and monitor what people are doing through AI as well as just on the dataset in general.

People are worried about AI agents, which have the same core problem. If they have access to too much data, not only could they reveal things that shouldn’t be revealed, but actually create more derivative data using that and proliferate the risk further. There are also some risks that people are worried about as they start to build and train their own models to make sure that the training data is intact, verify the integrity of the training data, make sure it’s not poisoned, make sure the data doesn’t contain things that should not make it into the model, secure the models themselves, also secure the underlying infrastructure that goes into creating the models. So there are many use cases here that are providing a tailwind for us in addition to the core security use cases that continue to be very, very important as well as compliance. So we want no breaches, no fines, no effort.

That’s what we’re helping our customers achieve. The way we start is we sell through a risk assessment. We want everybody to take a look at their production data through our software, help you know, it’s a quick install because it’s a SaaS solution that’s very quick to spin up. We can start to assess a portion of the customer’s data very quickly. We’ll take a look at what’s sensitive, the state of those preventive controls that I mentioned, and start monitoring it for threats.

And once a potential customer starts looking at their data through our lens, it’s very hard to unsee the risk. It’s the best event that we can have from a sales perspective, and this is part of our sales motion. So I guess with that, see if there are any questions.

Unidentified speaker, Moderator, William Blair: K. Great. Maybe to start out, it’s a dynamic space that you guys are playing in. Historically, you’ve talked about sort of, you know, only seeing competition in, like, one out of 20 deals. How has that changed over time?

And and then, you know, are you seeing kind of a new sort of vector of competition coming in that whole DSPM category, which maybe we need to for folks here. But but maybe just talk about that competitive evolution.

David Gibson, SVP of Strategic Programs, Varonis: Sure. I don’t think that the competition has changed much in the areas where we’ve been traditionally, been in the data center and three sixty five. As we’ve expanded our coverage and gone into structured data and more of the cloud stores, the more SaaS applications, we’re able to participate in more discovery and DSPM opportunities. And with those, we’re, of course, encountering you know, because they’re they’re RFPs or RFIs. We’re encountering other discovery and DSPM vendors, so in these new areas.

So I say that’s changed. There’s a lot more activity, a lot more focus on data. And with that, some of the new players there, which I think the important thing to remember is discovery is not security. Discovery, we think, is just a sliver of what you need in order to actually protect data and get to those outcomes. You need to not only see the sensitive data, discover that, but also have deeper discovery.

Where are the the risks because the preventive controls aren’t in place? How is it being used so that you can then automate fixing the things that you find? Otherwise, you’re just kind of left with liability and busy work and then followed by a breach.

Unidentified speaker, Moderator, William Blair: Is that right is that the right way to think about your differentiation is that you guys have the sort of breadth across on prem, cloud, SaaS, as well as that that ability to go beyond just discovery and classification? Is that the

David Gibson, SVP of Strategic Programs, Varonis: right framing? I think both the depth and the breadth. Coverage has become a huge weapon for us because we do have coverage for all the enterprise data stores. That is often right out of the gate a big differentiator, but it’s not just the coverage, as you mentioned. It’s the functionality, the ability to remediate the risks that we find with the automation to do the managed data detection response, the automated threat detection from a data level.

These are some of the differentiators we see. When whenever it’s really a data security use case as opposed to just a discovery or maybe a privacy use case, we’re in really good shape.

Guy Melamed, CFO and COO, Varonis: I wanna maybe visualize it for some of the nontechnical people in the room because the everything sounds the same, and, and it’s it’s very hard to understand what we are doing different, in comparison to to some of the other verbiage that all sounds alike. The best way to visualize it is a bank. You know, when you think about protecting, the vault within the bank, there are multiple ways to think about it. That you need the cameras outside the bank. You need the guards.

You need the fence. You need all of that protection. We sit on the vault itself. We identify that any abnormal behavior that is in relations to data. So if someone touches, data from an IP that isn’t recognized, or opens a thousand files or 10,000 files instead of five file sensitive information, someone’s trying to get into that vault, and we can disconnect the account and make sure that nothing nothing happens.

It doesn’t mean you don’t need the fence and you don’t need the cameras and you don’t need the guards. You absolutely need them. But at the end of the day, in order to identify if anything abnormal is happening in relations to data, we have the sophisticated algorithms to identify if something’s happening. And on top of that, the end of the day, no one breaks into the bank in order to steal the pens. So we really sit on what is the most, sensitive part of the organization.

And not only are we protecting against someone trying to come in from the outside, if you have 10,000 employees and you’re a c level executive and you believe that all 10,000 employees are ethical, you probably shouldn’t be running a company. So we protect from the inside out, trying to make sure that no one takes information and gives it to competition. We’ve seen so many, instances where an employee was either selling information to competition or is about to leave, and they were gathering all the sensitive files so they can take it to their next jobs. So either it’s from within or someone taking over your credentials from the outside, we can identify that through the sophisticated algorithms. K.

Unidentified speaker, Moderator, William Blair: Oh, is anyone else freezing in here? Because it’s like it’s like a meat locker in here. Guy, can you talk about the SaaS transition? Because, you know, David didn’t mention that. He did a little bit in terms of how it’s helping you guys with the risk assessment.

But talk about from a financial perspective, you know, how that’s how that transformation has gone, where we are in the process, and then what it brings to the table, like, across all elements, like, you know, sort of the win win win Yep.

Guy Melamed, CFO and COO, Varonis: Aspect. In order to understand the financial kinda implications of the transition, I’ll touch first about how much better the the SaaS product is because that’s that’s where everything starts from. And we have been able we announced the transition at the beginning of twenty twenty three, and we initially talked about a five year transition period. And we define the transition to be complete when we get anywhere between, 70 to 90% of our ARR coming from SaaS. And then we cut it from five years to four years, and we recently cut it to three years.

So we expect to be done with the transition at the end of this year. We just raised our SaaS mix guidance from 78% to 80%, so we expect to be, 80% of our ARR at the end of the year coming from SaaS. We talked about three north stars when we initiated the transition. We talked about ARR because revenue becomes noisy with kind of the the way revenue is recognized on the on prem versus SaaS. In SaaS, it’s ratable.

In the on prem, there’s there’s a a a big chunk that’s recognized up front. So we talked about ARR as one of the north stars. We talked about ARR contribution margin, which takes into consideration kind of the cost structure, and we’ve done a very good job in maintaining the cost structure even during the initial stages of the transition. And the third north star is the free cash flow, and we have shown some pretty significant improvements on our free cash flow. We finished we we guided to a hundred and 20 to a hundred and $25,000,000 of positive free cash flow for this year, which is significant improvement from last year.

So we’re very happy with kind of the leverage we have in the model. There is noise on the standard p and l if you think about it just because of the revenue, side. But from a cost structure and from a generation of cash, we’re ahead of schedule. When we laid out a five year plan in our investor day at the beginning of twenty twenty three, we talked about a $1,000,000,000 target by the end of twenty twenty seven, and we talked about having ARR contribution margin in that 20% range. And when you look at the levels that we are today, we’re in that 17% ARR contribution margin, so we’re literally ahead of schedule and and kinda on the path there.

Where we sit today, we have never seen the opportunity as large as it is. So some of the investments that we’re making today are actually to support growth post that $1,000,000,000 mark, as we see a path to capitalize on a much larger opportunity than we originally thought. Copilot is one of them. David talked about it a bit. But when you see, companies rolling out Copilot and realizing how vulnerable they are, if you have an organization that rolled out Copilot and then one of the employees goes into the checkbox and writes who got a raise last year and suddenly the full set of data of all the raises within the organization, pops up within seconds, that’s a disaster for an organization if that employee didn’t shouldn’t have had access to that type of information.

So Copilot and it’s not just Copilot. It’s any GenAI is really putting a spotlight on a problem that always existed, but now it’s just becoming simpler for the for not just for, employees, but also for hackers to take advantage and find where the sensitive information is. So Copilot and GenAI as a whole has really helped us in kinda seeing how this world is in the direction it’s moving, and we wanna capitalize on that opportunity.

Unidentified speaker, Moderator, William Blair: And is it in a catalyst at this point, the the GenAI stuff in Copilot?

Guy Melamed, CFO and COO, Varonis: It’s coming up in every conversation. We actually started seeing it as being a contributor in the last one to two quarters, but it’s not anywhere close to where we think it could be. It’s probably in the first inning, if at all. When you think about the rollout of Copilot, we’ve been very consistent that you know, I I think investors expected Copilot to become a thing way quicker than what it’s hap in the way it’s happening, but we’ve been very consistent that you don’t really know if it’s gonna happen the next quarter or the next year, but eventually, it’s gonna happen. Organizations that won’t roll out productivity tools, I don’t think will exist.

So whether it happens in six months or a year, we don’t know, but we’re there to capitalize on it. Okay.

Unidentified speaker, Moderator, William Blair: And then just to to round out the SaaS, transition question. Can you talk about, like, what it has done for customers and for your Salesforce channel, etcetera? From a customer perspective, it’s simplified, kind of the whole way they think about the problem because MDDR is only offered under the SaaS offering.

Guy Melamed, CFO and COO, Varonis: And in MDDR, the only thing you need to do is pay. We basically will take care of, the rest. And at the end of the day, you can save on on the hardware because you’re buying the SaaS offering, and you can save on the headcount because the automation of the product takes care of a lot of the alerts, you don’t have to manage it yourself. We do it for you. We expect that from a SaaS perspective, we expect renewal rates that have been consistently over 90% to actually improve, because of that stickiness that the MDDR generates.

And and when we look at the sales cycle, sales cycles of SaaS have been shorter than the standard sales cycles for on prem subscription. So from a from a financial strength perspective, SaaS is a no brainer for us. It’s also a no brainer for the customer because of the value of the product.

Unidentified speaker, Moderator, William Blair: And what’s the uplift versus the on prem?

Guy Melamed, CFO and COO, Varonis: Apples to apples, if you’re buying the same number of licenses, it’s a 25, 30 percent uplift. The total cost of ownership for customers is lower still even with that uplift because they can save on the headcount, they can save on the hardware. But in some cases, we see customers actually consuming more of the product, so they pay more than that 25, 30 percent uplift. Okay. We’ll have to end

Unidentified speaker, Moderator, William Blair: it there. Thank you everybody for coming. Thank you, guys. We’re gonna go upstairs to This presentation has now finished. Please check back shortly for the archive.

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