NetApp INSIGHT 2026 | Russell Fishman, NetApp

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Russell Fishman

Sr. Director, Global Solutions and Field Activation

NetApp

play_circle_outline NetApp Novus: massive scale and performance for next‑gen AI, 100TB/s and zettascale ambitions

play_circle_outline NetApp Platform: unified control plane and consistent data plane across on‑prem, cloud, edge

play_circle_outline AI Data Engine: prepares data for AI with security, governance, and accessibility features

play_circle_outline Customer offer: AI Data Engine trial deployable on existing NetApp systems, six‑month promotion

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Russell Fishman, NetApp

Russell Fishman

Sr. Director, Global Solutions and Field Activation NetApp

In this interview from NetApp INSIGHT 2026, Russell Fishman, senior director of global AI solutions and field activation at NetApp, joins theCUBE's Rebecca Knight and theCUBE Research's Christophe Bertrand to discuss how unified storage and an autonomous control plane are forming the foundation for AI-ready enterprise infrastructure. Fishman unpacks a wave of announcements led by NetApp Novus, a new architecture delivering 100 terabytes per second of performance and a vision for a zettascale singular namespace spanning global data. He details the NetApp platf... Read more

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What is NetApp Novus, what did you announce about it, and why does it matter for next‑generation AI workloads? add

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(INTRO)

Rebecca Knight

>> Good afternoon, everyone, and welcome back to theCUBE's live coverage of NetApp Insight 2026 here at the MGM Grand in Las Vegas. I'm your host, Rebecca Knight, sitting alongside, of course, Christophe Bertrand, who is theCUBE Research analyst in residence. I would like to welcome to the show next Russell Fishman, senior director, global solutions and field activation.

>> Thank you.

Rebecca Knight

>> Thank you so much for coming back to the show. Absolutely.

>> Always a pleasure. Always a pleasure.

Rebecca Knight

>> So this morning, yesterday, last Friday, we've had a dizzying number of announcements from NetApp. Spanning hybrid, multi-cloud, the agentic enterprise, of course, AI factories.

>> Yes.

Rebecca Knight

>> Why don't you walk us through them and talk a little bit about what is most salient to you?

>> Well, I think firstly, the watchword's innovation. I think that's back in a big way with a really huge set of innovation announcements. And so we're super proud of what we delivered here. Yeah, there's a whole bunch of great stuff to unlock. So let's start with probably the biggest one, which is Novus, NetApp Novus. And so, you heard— if you were in our keynotes, you would have heard some amazing announcements around this, just what we've built with Novus and the problems we're trying to solve with Novus. But essentially, we're delivering a completely new level of scale and performance for next-generation AI workloads. It's the sort of things you're going to see in AI factories, but also in Neoclouds. And we recognized we needed something new to go after this market. We're delivering it now with Novus. And Novus is— we announced a number, 100 terabytes a second of performance. It's almost unheard of in the industry, right? George, our CEO, talked about the concept of zettascale, zettascale singular namespace, which again is just dizzying. He talked about having all the data in the world. Imagine having all the data in the world available through a single namespace. It's obviously somewhere to get to, but it is something we're going to be delivering with Novus. So Novus is huge, very important for what we're going to do to help enable this next wave of innovation. But that was just one of the things we announced, right? So another big thing we talked about this week was the NetApp platform. So the NetApp platform is all about how we take all the amazing things that NetApp does as a company and bring them together into a single wrapper. Now, what does that mean? That means a totally consistent data plane where data moves absolutely seamlessly between these different environments. That could be on-premises, that could be in the cloud, that could be in a sovereign cloud, that could be in a NeoCloud, it could be at the edge and making that totally consistent, but also having a totally singular unified control plane and then still giving the opportunity for organizations to pick exactly the right components to construct their data environments. So that was another big part of what we talked about this week. And then the third one I was going to call out, and there's loads of other announcements, but we'll be here all day talking about them. The other one I was going to call out was AI Data Engine. So we, we announced a bunch of new capabilities in AI Data Engine. AI Data Engine at the most basic level makes your data ready for AI, makes it more intelligent, makes it accessible, but does it in a very secure and governed fashion. And honestly, that's one of the things that we think most organizations are going to want to take advantage of. It is a no-regret move to get themselves ready for what's coming at them in the agentic era.

Rebecca Knight

>> Right.

Christophe Bertrand

>> So it's interesting because you spent years disaggregating the platform to, in a sense, unifying the platform or the data, under it.

>> Yes.

Christophe Bertrand

>> In the context of AI. So obviously there's also this concept of, intelligent data infrastructure, which goes hand in hand. So walk us through this. I'm an end user. I'm still running traditional workloads, I've done some AI projects. Now we may be thinking about a new architecture.

>> Yeah.

Christophe Bertrand

>> for my next, sort of generation of investments. How does it all work out?

>> So it's funny you mentioned the phrase Intelligent Data Infrastructure. I think when people hear that phrase, they think, oh, sounds very marketing, actually, no. When I talk to customers about Intelligent Data Infrastructure, it's really what we're— how we're expecting data and storage to change. When we're looking at how data is gonna be used with all these new types of workloads and use cases. And it really talks about the evolution of that storage layer. Storage was fundamentally a retrieval-based system, right? You asked me for a file, I would check whether you had access to it, I'd give you access to the file. We're moving to where we're expecting that storage layer to start incorporating intelligence. Intelligence means even the ability to move to a more query-based system than something as simple as a retrieval-based system. It talks about moving from, static access controls, for example, to dynamic behavior-based access controls where access to data from agents is not governed by a yes or no, it's governed by how are you using the data. So we're starting to change the— and all of those concepts are starting to change what people expect, what organizations expect from their data layer. So when I— it's funny, so you mentioned Intelligent Data Infrastructure. It's not a product we sell. It's a promise we're helping organizations get to. So I think that was— I love the fact that you sort of hooked on to that. But yeah, look, the reality is that we've been through these transitions multiple times before in the storage industry.

Christophe Bertrand

>> Right.

>> New workloads come in, new requirements are clear. The question is, how do we evolve what we do to meet these new challenges? And that's really what NetApp has been talking about this week, right? The platform, which is the unification, as you put it, Christophe, it's super interesting, right? Because, you could go about this in two different ways. You could have a singular system that does everything. But you know what they say, jack of all trades, master of none, right? And it's very true in storage, right? If I came to one of my customers and said, hey, listen, we're going to do a singular system that incorporates object and you're going to put all your objects in this environment, but I'm going to sell it to you with NAS economics, they're going to laugh me out of the room. That is not the same thing. There is a very specific economic model for scale-out object storage. So we recognize that organizations want the flexibility to pick the right solution for the right part of their data pipelines, the right part of their data environment, but have the simplicity of having a singular platform. And that platform means, as I say, unified control plane. That means simplicity in operations and simplicity at scale, but more importantly, a consistent data plane. So that doesn't just mean the data moving, that means all the metadata. It means all of the security and governance and all the things around the data that makes the data useful. And that's really what— that's our vision for how we're bringing data together.

Rebecca Knight

>> So, Russell, what you were just talking about with customers, their expectations have changed and increased. Yes. They want richer context. They want things faster. How do you help them make the most of this intelligent data infrastructure and what does it unlock for them as an organization?

>> Yeah, it's a good question. And look, I— the reality is that it's not just about the raw data. When I say raw data, I mean the files, right? It's how those— how that data is utilized. Right. And actually, we talked today in one of the breakout sessions, we were talking specifically about the problem of dealing with massive amounts of data and how that data actually becomes useful in the context of AI. And it reminds me, when I go talk to organizations, so many of them have failed to get past a proof of concept. And one of the reasons for that is when you're in a proof of concept, you start with this curated dataset. You've handpicked 100 documents, 1,000 documents, and you know what? It all looks great. You're asking the chatbot a question, for example, a retrieval augmented generation, that's, for example, a solution, and it comes back with a great answer. The real world, of course, is that no one actually does that. When you actually move past a POC, you're consuming all the data. So now it becomes, how do I know intrinsically how useful that data is in comparison to the other data that might conflict within my organization. And of course, the reality is you have to look beyond the data to the metadata to really understand that. So part of our goal here is how can we take that inferred hidden metadata and expose it in a way alongside the data you already have to make that data more useful for AI use cases?

Christophe Bertrand

>> Okay, so let's talk about security.

>> This is all

Christophe Bertrand

>> great.Yeah, but cyber resilience is a big problem.

Christophe Bertrand

>> Yeah.AI is being used by cyber attackers to go after the data itself, certainly to go after the AI itself. We've seen some very interesting— I'm not talking just prompt injections. There are lots of things that are happening now that we could not even think of a few months ago. So how do you approach this at a data level? Because that's really where you play. Yeah.

>> And I sort of touched on this a little bit before and it's a really great question. It's actually probably the biggest concern most organizations have. They hear this, they see the news, they hear about agents breaking out, they hear about agents doing things that aren't even necessarily malicious. They just were acting within a certain bounded set of criteria and they thought this was the best way to get what they want to solve the problem that they'd been asked to solve. So yeah, there's real concern about it. So you're absolutely right for pointing that out. So traditionally, of course, NetApp has had a range of different capabilities in the data protection cybersecurity space. And we announced some more stuff this week in the more traditional areas. So, for example, our anti-ransomware protection service, which gives us AI-based detection and prevention of ransomware attacks. We announced the integration with Commvault, for example, this week. So it's a really great direct integration with Commvault. What that— by the way, what that means is that we become an indicator of compromise. So we can actually detect things and tell Commvault, hey, there's a problem and vice versa. Maybe someone else has spotted something. We can take some proactive measures. We've done this also with some other vendors. I will tell you this. So we've done it with Microsoft, with Microsoft Sentinel, we've done it with Cisco and Splunk. So this is something— this really describes how the cyber resiliency platforms that we're building integrate into a broad enterprise context. So that's part of it. But then more specifically in the AI space, it really has to do with how data is accessed and starting to get away from traditional anthropomorphize— anthropomorphization. Yeah, I feel like I got that. You did.

Rebecca Knight

>> You did.

>> I got it now. OK. OK. Yeah.

Rebecca Knight

>> There you go.

>> There you go. We're starting to get away from thinking about everything through a human lens and starting to think about it, hey, this isn't a human. This is an agent. Agents don't think the same way, they're not acting the same way. So the question for us has been, how do we make that data available and useful? So we talked about that before, but now in a way that protects us against unwanted consequences. And I didn't say malicious consequences because actually most of the organizations I talk to, it's the un— it's the unexpected consequences, right? So the way we're doing that is in our AI Data Engine that we, that Some of the new features we announced this week includes a behavior-based policy engine. And what it's now doing is not just saying, hey, do you have access to this piece of data or don't you? It's saying it's starting to think about how that data is being used. For example, looking at how much data you're utilizing. That seems weird. I would expect you to look at maybe one customer's account, not all of the customers' accounts. That seems a bit weird to me. So how are we going to be able to react to that? At the speed of AI, which means that you can't always have a human in the loop. We have to have that automation in there. So that's the sort of intelligence that part of that IDI, Intelligent Data Infrastructure, that's the intelligence we're bringing in now.

Rebecca Knight

>> I once heard an expert talk about how the only time it's useful to anthropomorphize AI is when you're saying how sometimes we don't know how humans make decisions or do the things they do. That is also true of AI. So that's the one time you can actually say—

>> but listen, I will tell you, as a vendor in this space, it's in our interest to create the environment where organizations feel comfortable doing this stuff, right? And so we have to start leading the way and saying, look, we don't just— we hear you, we recognize, and we're building what needs to be in place to protect your data. And I think that's really critical.

Rebecca Knight

>> So you started this conversation talking about customer outcomes and really building from there. What do you want to see for enterprises that are trying to turn their AI ambitions into those measurable outcomes.

Christophe Bertrand

>> What is—

Rebecca Knight

>> what are some of— how do you help them approach it hand in glove? Yes. And what are some of the most important decisions that they need to be thinking about as they go through that process?

>> So there's things that I could repeat that you've probably heard a million times before. Focus on use cases, focus on the business outcomes. But I'm not going to bore you with that because I'm sure you've had plenty of guests talk through that with you. I'll tell you what we are seeing that's kind of new and different. Right. So if I went back 18, 24 months ago, even actually 12 months ago, infrastructure teams just weren't really involved in most conversations on AI. What we tended to find is that the board or the C-suite had said, listen, actually ignore all the standard corporate structures that were in place because they're going to slow you down. Go and go and do some stuff. Right. and what happened is that they went and did some stuff, but not a lot of that actually turned into production useful ROI. Because the reality is that, you know, doing something in a lab is very different from doing it in production. The CTO comes in, goes, what are you doing with the data? The CIO goes, I can't operate this. And then the finance guys are like, this doesn't make any sense. We're not saving any money doing this, right? So it's changed in the last— even the last 6, 12 months, we've started to see a recognition that you need more of the folks that are going to make AI real in your organization. At the table at the start. Right now, the reason I'm saying all this is because all of a sudden IT has recognized that they have a role to play to create the environment for AI innovation in their organizations. So what does that mean? It means starting to build the same sort of shared services that IT organizations have always built to enable a range of different application owners to go and do what they want to do. And this, again, this is not a conversation I was having 12 months ago. I absolutely wasn't. But now we are having that conversation. So now, that's really focused on, again, this concept of a no-regret move, right, of what any organization should be building in terms of shared services. And we have real clarity about what those features and functions are. AI Data Engine that we've talked about here just this week is a key part of what we think is a shared service. It means that instead of building data paths that are specific to individual use cases, starting to recognize that we have to have a consistent way to broker and secure and govern data throughout the enterprise. So to specifically answer your question, very roundabout way again, well, we're suggesting, we're recommending that organizations build those shared services that enable that innovation. AI Data Engine, of course, a great way to do that because it secures, protects, governs, exposes, and prepares data for all of these new types of data consumers in the enterprise.

Christophe Bertrand

>> So look, INSIGHT 2026 is kind of a different vibe from last year, to be honest. I remember I was here last year and we talked quite a bit about what was going on with clients and AI was a project. Now it seems to be happening for sure. And it's really a second phase, I think, in the adoption of AI by and large. And there may be more, but at least we feel— it feels very, very real now. And certainly the announcements support that this is happening and you have to really think as a business how data is central to everything. It's data infrastructure, how it has to be pervasive, how it has to be governed, controlled, et cetera. In wrapping up, what are the thoughts or your last, your closing thoughts and what advice would you give Insight 2026 attendees? What should they do next when they go back to their office?

>> Yeah. And by the way, you're 100% correct that there is a different vibe here. The reality of what organizations are faced with, it's not a wave, honestly, it's not even a question of innovation. It's a question of keeping up at this point. that's the reality, right? Yeah. Well, the attendees that typically come to something like Insight, right, they are people who understand storage and data management very well. They know NetApp very well. Right. And so, for the attendees here, we're talking about big install base. NetApp has 100 exabytes of data that we manage or is managed by our systems globally. So there's a lot of data out there. This is the fuel that's driving AI, that the custodians here this week, these are the custodians of that data. What is the role that they need to play? What's the evolution of that role that they need to play to take advantage and to prepare for this? We actually announced an offer this week, and this is probably one of the most practical things that any of these folks can do, which is to take up the offer of AI Data Engine. AI Data Engine can now be deployed against any existing NetApp system externally. And what that gives you is this layer of data readiness preparation, AI readiness, security control around the data. It's something that any organization can go look at it now. They can take it up as an offer. It's 6 months, I think we're offering it for free. There'll be certain features that are included in customers' existing ONTAP One licenses moving forward as well. But again, that said, if I was an ONTAP admin, if I was a NetApp customer, that's the first thing I'd be doing. I want to lock this down now. The risk of not doing so, not just from a lack of innovation, but the risk of misuse of data, the lack of control, It's too great. Thankfully, we got the right answer.

Rebecca Knight

>> Well, that's a great call to action. Russell, thank you so much for coming on the show.

>> Thank you so much for having me. Thank you. Thank you.

Rebecca Knight

>> I'm Rebecca Knight for Christophe Bertrand. Stay tuned for more of theCUBE's live coverage of NetApp INSIGHT. You're watching theCUBE, the leader in enterprise news and analysis.