Transcript
that your organization has already adopted AI, either internally to help your team out or by integrating it into your products. It doesn't take a detective to see how much it's taking over organizations, for better or for worse. The thing is, AI didn't slowly roll into organizations, just showed up. You know, chatbot here, LLM there. And before you know it, there's a whole fleet of agents doing work in the background. Most organizations didn't decide to adopt all this AI, but it happened faster than security could keep up. Now, there's a simple question almost no one can answer clearly. What can our AI actually access? And therein is our problem. That said, there are solutions out there. Some are great at finding the AIs a company is using. Others monitor what they do or deal with governance. But AI doesn't work in pieces. AI runs on data. That means your customer data, your employee data, your code repositories. AI can look through all of it if you let it. See, when security tools only see one slice of that picture, AI security becomes fragmented. You can't govern AI with a spreadsheet, and you can't protect it with the tools that only see part of the system. Think of your AI environment like a science lab with a bunch of stations. One station mixes chemicals, one measures temperature, one writes notes. They're all running at once. Each one will give you something useful, but if you don't know what's in each station, who's allowed to use them, or what happens when certain chemicals are combined, you might be walking into a bad chain reaction. This is why security leaders keep asking the same questions. Things like, which sensitive data can AI systems access? Are our agents configured correctly? Can controls be bypassed? If you're scaling your AI and can't answer those kinds of questions, it's just not safe. So, as a cheat sheet, here's the cleanest way to think about AI security. Call it the donut, or bagel, or pizza, it doesn't matter. Point is, inside it, we have all the pieces that make up effective AI security. On the outside, there are three outer rings that have to work together. Visibility and posture, protection, and governance. Miss one, and the whole thing falls apart. First, visibility and posture. You need to know everywhere AI exists in your environment, including the AI that hasn't been officially approved. That means discovering AI agents, assets, models, embedded AI, and constantly assessing their posture. Some AI projects are only discoverable in source code too, so you'd need to be able to scan code repos in Hugging Face or GitHub. Because knowing an agent exists isn't enough. You need to know what it can access, and whether that access is normal, risky, or outright dangerous. Second, runtime protection. This is where monitoring, AI detection and response, and runtime guardrails come in. These are controls that observe problems and can stop them in real time. Let's say little intern Jimmy copies and pastes your source code into a public AI tool. Your protection shouldn't just log the event. It should understand what that data is, understand your policy with it, and block the AI tool from misusing it. Bad Jimmy. If an agent tries to delete records, move data, or trigger workflows, guardrails need the data context to understand intent and prevent outcomes that should never happen. The final piece of the puzzle is governance. That means third-party risk management and AI compliance management. You'll need to manage the AI usage across your supply chain and make sure that your company is up to date on constantly changing AI regulations and frameworks. You'll need automated, audit-ready reporting, clear alignment with AI regulations, and visibility into third-party AI risk. Not just are we secure, but can we prove it? Now you know the three elements, but you'll need a security tool that gets them working in tandem. That's where Varonis comes in with Atlas. Atlas is an AI security platform designed to cover all three of those rings, across the full lifecycle of AI you build and the AI you run anywhere. It's AI-agnostic, so your security strategy isn't tied to a single model. It's data-aware, so it understands exactly what your AI can access and why, and it builds on the same data security foundations customers already rely on, extending that trusted context into AI. The pace of AI isn't slowing down, but speed doesn't have to come at the cost of control. If AI runs on your data, then security has to understand that data across visibility, protection, and governance. I'll say it one more time, visibility, runtime protection, and governance. Get those three things right, and you can stop worrying about what AI could do to you and focus on what AI can do for you. Stay safe out there. Stay safe out there.
