Preparing Enterprise Data for Safe AI Deployment - with Todd Vancil of Securiti AI

**SPEAKER_1** (0:12)
Welcome everyone to the Emerge AI in Business Podcast. Today's guest is Todd Vancil, Vice President of Veeam's Securiti AI Sales Engineering Team. Securiti AI's Data Command Center enables AI-powered data security, privacy and governance, providing unified visibility and control across hybrid and multi-cloud data environments. Todd joins Daniel Faggella, Emerge CEO and Head of Research to explain why AI forces enterprises to expose sensitive data to new systems and why securing the data itself, not just the perimeter, is now the operational priority. The outlines our leaders can control what AI systems ingest and retrieve, reduce redundant data that increases risk and respond quickly when exposure occurs at machine speed. Today's episode is sponsored by Securiti AI. In this episode, we cover data security. To go deeper on the topic of AI and learn how financial institutions are digitizing paper-based records to unlock usable data for AI and using alternative data like public web and social signals to enhance risk assessment, download our free PDF report, AI in Financial Services Executive Cheat Sheet at emerge.com/fcs1.
That's emerj.com/fcs and the number one. Now the conversation with Todd.

**Daniel Faggella** (1:51)
So Todd, welcome to the show.

**Todd Vancil** (1:52)
Hey, Dan, thanks, it's a real great pleasure to be here.

**Daniel Faggella** (1:55)
Absolutely, we're touching on a topic that's top of many people's minds here in the AI world, as sort of security becomes a bigger and bigger gap and problem within the enterprise. You were talking to a bunch of different enterprises, you guys are now obviously with a much larger organization. I want to kind of let you set the table on what seems most important. So when we look at where AI security sort of seems to be a hurdle within, let's say, larger enterprise corporations, what are the patterns that come up time and time again for you?

**Todd Vancil** (2:23)
Yeah, it's, you know, our viewpoint of the world, Dan, is really centered around a data security perspective. And if you back off, maybe in a 30 or 40,000 foot view of the AI landscape, ultimately, the very lifeblood of AI is data, right? The way that this revolution is happening with large language models and agentic AI, it starts with these language models being trained on massive amounts of data. And as we're looking to optimize our businesses, large enterprises in particular, the data that is most valuable to them turns out to be their most sensitive data, right? So one of the examples I like to use, I live in the Dallas-Sport Worth Metroplex, which makes me pretty much hostage to American Airlines. I love American Airlines. I've been flying with them for 30 plus years. But for me to do business with American Airlines, American needs to be in possession of quite a bit of my sensitive data. Some of that's very obvious, such as my address, my birthdate, my passport number, but some less obvious. Airlines know when I'm going to travel, where have I traveled. Sometimes they have partnerships with other organizations where I might be staying, or where do I like to stay.
That all becomes very sensitive data. Of course, we know there's tremendous amount of regulations that govern the custodianship and the use and the care of that. However, if American really wants to, or other airlines want to innovate through AI to understand us as travelers better, to create new and better products and services, routes, and so forth, well, they need to train their large language models or even, or make available to their agentic AI agents. They need to make this data available to them. That's what is valuable. And so we see this again in the game of organizations is almost every AI project circles around, okay, why would we do this for a business objective? What are we going to do to innovate? But it comes back to the data, which sort of lands back at our front door as an organization that builds technologies to help with the mission of securing sensitive data. Yeah.

**Daniel Faggella** (4:27)
So it sounds like what you're highlighting here first, I opened it up with wherever you want to go with it, which is sort of like, hey, where is sort of AI security showing up time and time again in the enterprise? What I'm hearing from you is there's a new capability where there's tension to create it because we got to be competitive and we got to make our customers happy and our employees happy and stay with the times. And at the same time, that means new people and new systems are going to be accessing data and then using and airing that data in brand new ways. By golly, how the heck do we deal with that? So it sounds like for you, it's like the security thing that comes up is capability desired, bunch of new man and machine concerns open up with new capability. Repeat, repeat, repeat. Is this the pattern that you're kind of putting a thumb on?