Transcript
where we showcase how partners are leveraging the power of SailPoint's Atlas platform to innovate and deliver exceptional identity security solutions for our customers. Today, I'm joined by David Iwaka, Director, IGA Solutions, and Ryan Clough, VP of Identity Services, North America at Zalient. A SailPoint delivery admiral, partner, and our most recent delivery partner of the year. Congrats, guys. Together, we'll be exploring how Zalient is using the SailPoint Atlas platform to build their AI-driven Martina solution as part of their MSP offering, and what that means for our joint customers. All right, as a SailPoint delivery admiral partner, you've got a track record of building solutions to address unique customer problems. Can you talk about what you built with Martina and how it helps our clients? I can, we're very proud of these custom solutions that we built, and Martina is one of those that we're really proud of as well. So Martina is an AI-driven tool that we've created. It was actually born as part of our network security part of the business, focused on monitoring network security for across our clients, being able to detect, being able to predict, and even been able to actually fix certain scenarios before they become real issues. And we've since been able to turn that towards identity security as part of our growing of that particular solution, and we're excited about where we're going with that as well. Right now, it's being used to help us with our managed services clients, our managed services offering, and allows us to do some great monitoring and response of our SailPoint implementations. So how does Atlas make all of this possible? What technologies and frameworks within the SailPoint's Atlas platform are you able to take advantage of to set up and leverage Martina? Yeah, so for Martina, it's through Atlas APIs. We're consuming the information out of ISE. We're getting information such as VA sources, identity data, account data, and events of the system, and fitting it into the platform so they can be processed through our AI machine learning algorithms and surface any issues or anything that's happening with the baseline functionality of the platform. So as we know, SailPoint has the platform covered from the operational capability of the platform, but really, Martina, what it does is that once it's implemented the solution, what a good solution looks like for the customer, what pieces of the solution need to work, and when some of these pieces is not working as expected, what could be the possible impact to the solution for the customer, not the platform itself, because of course we know that SailPoint has that cover. So when we're talking about Martina and you're leveraging it on a day-to-day basis, is it helping to identify problems like connections not working and help resolve them? How does that work? Explain it in a little bit more detail for me. Right, so Martina basically operates in three levels, the observe piece, the resolve piece, and remediation piece. On the observe side, as you mentioned, through the APIs, we're able to identify issues with events, like an aggregation not working, a source is not connecting, a VA probably is down, things like that that will get back into the platform and triage these issues and coordinate a response to them. Response can be as easy as throwing up an email to an agent so they can help, triaging with an ITSM tool like ServiceNow and follow that through through a closed loop, and eventually predict some of these issues. With enough data and with the machine learning that we have in the platform, we should be able to predict some of these issues before they even hit the overall solution the customer has. As all we know, there is individual pieces in the platform, as you said, aggregation, the sources, the VAs. So if a VA is not working, you can, with Martina, predict that there might be an issue with a downstream connectivity. Might be a process that is depending on it, might be orchestration of data that we also are triggering in some of our other solutions through workflow in ISE. So what Martina is providing is more contextualized solution to the baseline issue that it's being reported on. That's pretty amazing. So how many use cases have you guys built into Martina to date to look over and watch, do you know? Yeah, I mean, we're basically covering all the baseline on ISE. So all the functionality provided through the Atlas APIs, again, sources, accounts, identities, history, events, all the events, when there is an issue, it's being reported back to Martina. Now Martina can triage some of these things and then respond to those. So we are adding extra functionality to it, for example, NIRM. So NIRM now is supported the NIRM workflows part of the platform or the add-on to ISE. It's also supported now with Martina. So if there is an issue with a workflow, if Martina detects that there might be an issue in a workflow because in the past, there was an issue in the workflow, it could, you know, typically NIRM sits in front of ISE. So it could say, if this issue happened, you're gonna have all these issues later with ISE and help contextualize what that issue means. And it's incredible. So one of the things I hear about from a lot of clients who are looking for a managed service, right, is, hey, we need real-time monitoring. We need around the clock. We need it to work. We need it to tell us when there's a problem. It sounds like what you guys are done is taken your managed service to the next level, created, you know, quite frankly, a large language model to overlook and watch the system even when people aren't and thus creating some value for your customers so they don't have to have a person watching it all the time, but instead know that, you know, we're up and running at all times, which is pretty amazing stuff. But you happen to use the word remediate earlier, which got me kind of excited. So when we start thinking about the remediate portion of this, right, and obviously AI can, you know, do a lot of things, but what issues are you guys currently remediating and where do you see it going, you know, in the next six to 12 months? I think our, yeah, I think our remediation at this point, we're not at the point where it's actually taking real action yet, so that's the stuff that we're working on right now. That's the exciting part, right? That's where we get to see what the agent can actually do and as we start to build some of these pieces out. Right now, it's mostly about taking the right kind of information based on what it's learned and then creating ServiceNow model for what the response should be to this particular scenario as opposed to actually making real changes in the system yet. So I can add a little bit to that. On the observability and prediction part of Martina, what basically we're doing is the roadmap, I can share a little bit of the roadmap here, is including agent, you know, response to it, meaning when there is a low risk issue, there is something that we already have seen in the past that we have a playbook for it. The agent itself could, you know, just respond to the issue and support the autonomous resolution of the problem. If it is a higher risk issue, what the agent will do is will go out, identify, get the context, get the evidence, propose a fix, and then send it to a human approval. So then, you know, the human has all the context. As I said, the baseline platform, SailPoint has it covered, which is great. But through Atlas, we can get all of this information and layer on top the overall end-to-end solution and the agent would understand that to provide that context and that value to, you know, the customer through managed services. So we can keep the solution working as expected, right? That's the value there. Wow. All right, so talk to us about what a successful deployment looks like for one of your customers. We use this solution for all of the managed services clients that we have. And so we have, for example, one very large client, a mutual client, I should say, that had over a million identities and NIRM in the process. And so we've been able to leverage this solution to help us get everything from when it was originally stood up to switching into managed services to actually provide some of that return for us to be able to provide some of that value back to our customer. And it goes beyond that. On top of Martina, right, as Ryan said it, we have a couple of other solutions that we implemented to this customer and other customers, you know, creating a holistic approach for our customers. One of them could be identity data orchestration. Again, we're using Atlas through the workflows to a trigger, whatever trigger that might be, to send information out to a bus. There are systems consuming this bus information, right, that need updated email information, updated account ID information, if you will. So there is that solution. We have other solutions like, for example, service now integration, right, where an emergency termination could be triggered out of service now, or through APIs, you know, offload some of that to ISE when needed. But also we have, again, the Atlas platform is becoming so powerful right now with flexibility and customization that we're using the capabilities provided in interactive forms and workflow to be able to give the same, almost the same solution to our customers, right, all within the ISE framework. Organizations that have service now and want to use service now can trigger to that because we're familiar with it. But if they don't and they want to have a similar solution, now, SailPoint can provide that. And all of this is through Atlas, right? It's incredible. So let's double click a little bit and let's talk about operational improvements. So what outcomes have you guys seen achieved by your clients so far? You want to take that one? I do, go for that. So the main core things are the ones you would imagine, right? It's mean time to detection, mean time to response and remediation. All of those improvements, so we're getting out of the platform for sure. There's also, as we continue to go down this path and pick up more capabilities, we're expecting that to provide even greater efficiencies, both for us internally and especially for our clients. So let's talk a little bit about trends for a second. How do you see Martina and other AI platforms interacting with each other and really starting to shape the future around governance and identity security? That is the hot thing right now. I mean, we can't get away from that conversation, right? It doesn't matter where you turn, we're always talking about what's the impact of AI, what's the impact of AI in the business and how are we gonna, you know, on one hand, we're all here sitting here talking about how we're leveraging AI to help us gain our efficiencies and improved experience, et cetera. And on the other hand, we're also going, oh my goodness, you know, this AI revolution is creating some real serious governance scenarios that we also need to go address with mutually, with our clients. And so, you know, that has to be top of the list for us. You know, non-human identity management, which was the broader picture with the AI agents and some governance around that is obviously the top of the list. It's coming up in almost every conversation that we're having with current clients and future clients. And so that is an area of focus for sure. I love it. All right, so what advice would you give organizations looking to modernize their identity governance approach today? And how do you see AI and automation influencing it? As for managed services and Martina, you know, again, we're working towards involving the agents agentic into the Martina workflow. So when there is a low risk issue, the agent itself can just deliver the value and, you know, autonomously respond to that issue. When it's a higher risk issue, the agent can get the information available and propose a fix for a human in the loop, right? A lot of other enhancements to the platform like anomaly detection enhancements and telemetry and all of these things that we're gonna start perfecting in the platform, their aim to basically create a platform that can predict some of these situations, reducing the incidents and instead of just simply responding to them, which is what currently is doing, right? Responding to some incidents and doing some triaging, but with a little bit more context and the agent functionality layer on top of that, it could do a little bit more intelligent resolution. And sometimes as the solution in the customer side matures, the majority of these issues could be resolved through agents. There is really information coming out to the customer of, yeah, there was an issue, but you didn't even notice it, right? An issue in the middle of the night, right? That happened that could impact your provisioning, but the agent is smart enough to say, well, this is more likely a password issue. We're just gonna go and get a password rotation through CyberArch or through Yantrust or whatever, and then confirm that this issue is fixed. Next morning, the customer will just get an email saying, yeah, there was an issue with this. It was a password. I applied the fix. I tested it. It's fine. There was no issue for you. That's amazing. I am loving where we're going. Well, David, Ryan, thank you guys so much for being with us today. Again, congratulations on your new award. And more importantly, guys, thank you so much for tuning in to Built on SailPoint. We really appreciate your viewership. If you've got any questions around Martina, please reach out to the wonderful folks over at Zalient. And again, guys, thanks for being on. Talk soon. Thank you. Thank you. Thank you.
