Varonis Systems, Inc.

greetings, and welcome to the Verona Systems, Inc. First Quarter 2026 Earnings Conference Call. At this time, all participants are in a listen-only mode. A question and answer session will follow the formal presentation. If anyone should require operator assistance during the conference, please press star zero on your telephone keypad. Please note, this conference is being recorded. I will now turn the conference over to your host, Tim Peirce, please go ahead.

Thank you, operator. Good afternoon. Thank you for joining us today to review Varonis' first quarter 2026 financial results. With me on the call today are Yaki Fidelson, Chief Executive Officer, and Guy Melamed, Chief Financial Officer and Chief Operating Officer of Varonis. After preliminary remarks, we will open the call to a question and answer session. During this call, we may make statements related to our business that would be considered forward-looking statements under federal securities laws including projections of future operating results for our second quarter and full year ending December 31st, 2026. Due to a number of factors, actual results may differ materially from those set forth in such statements. These factors are set forth in the earnings press release that we issued today under the section captioned forward looking statements And these and other important risk factors are described more fully in our reports filed with the Securities and Exchange Commission. We encourage all investors to read our SEC filings. These statements reflect our views only as of today and should not be relied upon as representing our views as of any subsequent date. Varonis expressly disclaims any application or undertaking to release publicly any updates or revisions to any forward-looking statements made herein. Additionally, non-GAAP financial measures will be discussed on this conference call. A reconciliation for the most directly comparable GAAP financial measures is also available in our first quarter 2026 earnings press release and our investor presentation, which can be found at veronis.com in the investor relations section. Lastly, please note that a webcast of today's call is available on our website in the investor relations section. With that, I'd like to turn the call over to our Chief Executive Officer, Yaki Feidelson. Yaki.

Thanks, Tim, and good afternoon, everyone. We appreciate you joining us to discuss our first quarter 2026 results. Our Q1 results reflect our strong performance as we execute on the growing need to secure data and safely enable the usage of AI. In Q1, SAS ARR excluding convergence increased 29% year-over-year to $522.6 million in total SaaS ARR, including conversion, was $683.2 million. Guy will review our results and our guidance in more detail shortly. We continue to see strong demand from both accelerating new logos and existing customers because companies understand that they must secure their data in their AI stack. Veronis helps them. Do that with minimal effort because of the automation built into our platform. In Q1, we saw continued adoption of MDDR and AI-related products, as well as traction in securing cloud environments. Early feedback on our newer products driven by acquisitions over the last year, including database activity monitoring, Interceptor, and Atlas reinforces our belief that these offerings are a strong fit to our platform and can help drive ARR growth over time. Now, I would like to take a step back from our near-term results and discuss why we believe we are best positioned to help companies safely adapt AI and prevent data breaches. Varun is founded on the belief that managing and protecting data would be impossible without automation. That belief is even more important today as customers work to adapt AI securely. The security model of the last 30 years was not built with AI in mind. Many organizations want to capitalize on the productivity gains from AI, but are only connected a small portion of their data to AI because of security concerns. Companies want to connect more of their data to take advantage of the productivity gains, but need the right guardrails in place to confidently move faster. When we look at what's standing in the way of broader AI adoption, we see three barriers, securing the data itself, securing the AI systems and agents that touch that data, and fighting AI-powered adversaries. The first barrier is securing the data and making sure only the right data is accessed by the right agents and systems. AI pushes existing access controls to their limits. because many systems and agents inherit user access that is far too broad. One classic example of this is an employee asking an AI chatbot a basic question and getting confidential information that they should not have access to such as salary data, financial records, or intellectual property in a response. This is content a human mistakenly had access to, but was less likely to find without AI. Previously, a human employee had to log in, navigate, download, and take action. There was friction because it took time and effort that reduces risk. In the agentic world, an agent can access a huge amount of your data estate in seconds. Agents can move fast, behave unpredictably, and maximize privileges by design. And if an agent doesn't have permissions, it will try to get them. Connecting agents and models to data is what's blocking organizations from safely adapting AI faster. They need remediation at scale, and to understand abnormal behavior, visibility alone is not enough. The second barrier is securing the AI systems themselves. In Q1, Voronis found a vulnerability called Repront, which allowed attackers to bypass safety controls in Microsoft co-pilot personnel. The vulnerability, if exploited, would give the attacker access to everything the co-pilot personnel session itself could access, including prompts, conversation history, and all of the data consumer assist could access. The third barrier is fighting the AI-powered adversary. We have already seen Examples of this, including last year, when attackers used cloud code to breach a major organization with minimal human involvement, or earlier this year, when alone, unskilled attackers used AI to scale an attack across 600-plus firewalls in 55 countries, an attack that would have previously required a team of experts to execute. AI-powered phishing doesn't just target humans, it targets agents too. Agents can read email, Slack, and team messages. One human clicking maliciously is one compromised identity. An army of agents can multiply the attack surface. The three barriers together, overexposed data, unsecured AI systems, AI-powered adversaries, create a dangerous environment and companies must build foundational controls that operate at the speed and scale of AI, starting from the inside out. Peronis does just that by securing the data itself using the automated find, fix, and alert approach. The first piece is find. Know what you have across the entire data store, structured, unstructured, semi-structured, and application data. Classified for sensitivity, context, and staleness, so you know what should and should not be connected to AI. The second is fixed. Right-size permissions, label data, and mask it. Manual process can't work anymore. The remediation must be automated and AI-driven. And finally, alert, monitoring who and what is accessing your data, and detect abnormal behavior quickly to stop breach before it happens. This is the basis for AI detection and response. AI security and data security are intertwined with one another. You need an inventory of every model, agent, and pipeline running in your environment, and you need access posture to know what data they can touch, what permissions they have, and where they are vulnerable. You need runtime guardrails to block malicious inputs before they reach the model, preventing sensitive data from leaking in outputs and restricting tool use. Finally, you must fight the AI-powered adversary. The volume and speed these attacks demand automation. These layers only work if they are connected. AI inventory and runtime protection is significantly more meaningful when you know what sensitive data they access and what data they are trained on. Guardrails that leverage the same accurate classification and labeling applied to enterprise data store reduce friction and increase control. We knew it would be impossible for humans to control data risk without tremendous automation Only AI can defend AI risk. When you trust your brakes, you feel safe driving faster. When you have the right guardrails, data and AI become a force multiplier, not a breach waiting to happen. With that, I would like to briefly discuss a couple of key customer wins from Q1. This quarter, a global technology company with over 50,000 employees became a Voronis customer. They needed to quickly and safely roll out AI tools and also wanted to better protect customers and company proprietary intellectual property data with compliance requirements and perform forensics analysis in an event of a breach. During the risk assessment, our MDDR team detected multiple active threats. We also identified risks in Salesforce and Microsoft 365 and provided an operational plan to fix these risks with intelligent automation. Our ability to provide these outcomes and safely enable the usage of AI was the key reason why we were selected over several DSPM point solutions. They ultimately purchased Varonis for AWS, Salesforce, Google Cloud Platforms, and Google Drive, as well as Varonis SaaS for hybrid with MDDR and Varonis for co-pilot. We also continue to see existing customers expand into new use cases as they consolidate point tools and utilize the breadth of our platform. In Q1, ServiceNow, a global leader of workflow automation, expanded its Voronis investments to cover internal AI systems and email security, including protection against advanced phishing and social engineering attacks used by AI-powered adversaries. In summary, AI is forcing companies to prioritize data and AI security. And Voronis is uniquely positioned to help with our unified platform that allows customers to put the right guardrails in place in order to accelerate the AI deployment plans. With that, let me turn the call over to Guy. Guy.