By When AI agents begin pulling live data from SaaS platforms, cloud systems, and on-premises sources simultaneously, enterprise teams lose the ability to track who is accessing what, what models are being used, and what it is costing the business. Security and compliance controls built for human users and data engineering pipelines do not extend to agentic workflows. The result is uncontrolled access, unbounded spend, and no audit trail.
In this interview on TFiR, Raviv Levi, Chief Product & Technology Officer at CData, breaks down why AI-driven data access creates a structural visibility problem and walks through how the CData Connect AI Gateway is designed to close it.
Guest: Raviv Levi, Chief Product & Technology Officer at CData
Show: TFiR
Here is what every platform engineer and enterprise architect needs to know.
Technical Deep Dive
Q: What does CData do and why does it matter for AI-driven enterprises?
Raviv Levi, Chief Product & Technology Officer at CData, explains that the company has spent over a decade building data connectivity capabilities, starting with connectors and drivers and expanding into data replication and data virtualization. More than 10,000 customers use CData to connect enterprise systems to SaaS, cloud, and on-premises data sources. The shift to AI means it is no longer only data engineering teams that require that level of connectivity: AI agents and every employee who interacts with them now need live access to the same data.
“It’s no longer data engineering teams that require that level of connectivity, but AI. And through AI, practically every employee, human or agentic in the company now needs live access to data.”
Raviv Levi, Chief Product & Technology Officer, CData
Q: What visibility and governance problems do AI agents create for enterprise data access?
As agents and human employees access data sources and models independently, organizations lose the ability to see who is using which systems, what the associated costs are, and whether security policies are being followed. Levi describes a scenario where there is no clear organizational view of what is happening, who is paying for which model calls, and what the cumulative cost is. Security concerns compound because there is no central enforcement point for access policies or compliance controls.
“You have employees and agents running around accessing all kinds of data sources, all kinds of models. No clear visibility to the organization of what’s going on, who’s paying for what, how much does it actually cost us, and a lot of security concerns as well.”
Raviv Levi, Chief Product & Technology Officer, CData
Q: How does an AI gateway fix the enterprise data visibility problem?
The CData Connect AI Gateway is an architectural layer that funnels all agent and human requests through a single control point, connecting them to models and data on the other side. By routing all traffic through the gateway, enterprises gain observability into which agents and users access which resources, can enforce security and governance policies centrally, and can apply cost optimization mechanisms. Levi positions the gateway as a required component in any organizational architecture that is serious about AI transformation done responsibly and at scale.
“By architecturally pointing out an AI gateway and making all those requests funneled through it, you gain a lot of advantages for the enterprise. You get the observability benefits, you understand what’s going on and who’s using what.”
Raviv Levi, Chief Product & Technology Officer, CData
Q: What specific enterprise benefits does the CData Connect AI Gateway deliver?
Levi identifies three categories of benefit delivered by routing AI traffic through the gateway. First, observability: teams can see exactly which agents, users, and workflows are accessing which data sources and models. Second, security and governance: policies can be applied and enforced at a single point rather than across dozens of individual integrations. Third, cost control: the gateway provides the instrumentation needed to track model spend and apply optimization mechanisms across the organization.
“You have security and governance benefits, and you can also apply policies and cost optimization mechanisms that would help you in your AI transformation in a more efficient and responsible way.”
Raviv Levi, Chief Product & Technology Officer, CData
Resources & Documentation
- CData Connect AI Gateway, enterprise AI gateway for data connectivity, observability, and governance across SaaS, cloud, and on-premises sources
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👇 Click to Read Full Raw Transcript
Swapnil Bhartiya: Let’s talk about CData. Who you folks are, what do you do and what role do you play or you are going to play in enterprise data connectivity space where AI agents are becoming more and more pervasive.
Raviv Levi: In a nutshell, CData as a company has been around for over a decade working diligently to perfect data connectivity capabilities. Starting with connectors and drivers, moving up to data replication, data virtualization and so on, has been used by over 10,000 and customers to connect enterprise to their data sources. Whether SaaS, Cloud, on prem, you name it, CDATA has been the solution of choice for those kind of data connectivity needs. What’s happened recently, interesting enough, is that it’s no longer data engineering teams that require that level of connectivity, but AI. And through AI, practically every employee, human or agentic in the company now needs live access to data. Now how you do that and how you enable that and how you partner with those companies, with those customers in their AI transformation journey. This is what we’re about here at CData. Transforming and evolving along with our customers and with the market from, from a place of pure and simple data connectivity to the more advanced needs that we see from AI applications, platforms and agents.
Swapnil Bhartiya: Excellent. Thank you for explaining that. And that also sets the stage, the foundation to better understand what you folks are announcing today.
Raviv Levi: Yeah, so as mentioned, the name of the game is AI transformation. And I’ve been spending a lot of time with partners, analysts, customers, tracking down the industry progress and how we treat AI. There’s been a lot of innovation out there. On a weekly basis you hear so many different announcements. It truly is an exciting time to be alive. But when you think about that in the business world in B2B, how are you able to really harness that power to improve your business to show preferred results? Well, the answer to that is that sure you have the intelligence, but you have to be able to connect that to data and you have to be able to do that in a responsible, efficient, accurate and secure way. And this is where CData and the Connect AI Gateway comes into picture. Connect AI Gateway is a solution that, that is built in order to help our customers transform their own organizations in order to become AI native themselves. It’s a high level speak to say that you need a component in your organizational architecture that would allow you to connect employees, humans and agents into models and data on the other side. And the way that you do that through an AI gateway would really determine the results that you’re seeing. There are many benefits to that. We’re going to talk about context in a bit. But even before we dive into context, just think about that. Right now you have employees and agents running around accessing all kinds of data sources, all kinds of models. No clear visibility to the organization of what’s going on, who’s paying for what, how much does it actually cost us, and a lot of security concerns as well. By architecturally pointing out an AI gateway and making all those requests funneled through it, you gain a lot of advantages for the enterprise. You get the observability benefits, you understand what’s going on and who’s using what. You have security and governance benefits, and you can also apply policies and cost optimization mechanisms that would help you in your AI transformation in a more efficient and responsible way.
