Yakov Faitelson
Analyst · Barclays
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, SaaS ARR, excluding conversions, increased 29% year-over-year to $522.6 million and total SaaS ARR, including conversions 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 and their AI stack. Varonis helps them to 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 adopt AI and prevent data breaches. Varonis 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 adopt 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 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 record 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 adopting 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, Varonis found a vulnerability called Reprompt, which allowed attackers to bypass safety controls in Microsoft Copilot [ personal. ] The vulnerability, if exploited, would give the attacker access to everything the Copilot [ personal ] 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 a lone unskilled attackers use 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 e-mail, Slack and key 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. Varonis 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 fix, rightsized permissions, label data and masking. 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 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 Varonis customer. They needed to quickly and safely roll out AI tools and also wanted to better protect customers and company proprietary intellectual property data to meet compliance requirements and perform forensics analysis in an event of the 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 were the key reasons why we were selected over several DSPM point solutions. They ultimately purchased Varonis for AWS, Salesforce, Google Cloud Platform and Google Drive as well as Varonis SaaS for hybrid with MDDR and Varonis for Copilot. 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 Varonis investments to cover internal AI systems and e-mail 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 Varonis is uniquely positioned to help with our unified platform that allows customers to put the right guardrails in place in order to accelerate their AI deployment plans. With that, let me turn the call over to Guy. Guy?