Brian Murphy, CEO of ReliaQuest, explains how generative AI automates repetitive tasks like phishing analysis and alert triage, freeing security teams for higher-impact work. AI will shift analysts from tool maintenance toward data interpretation and strategic threat hunting, requiring new skills in understanding data and translating findings into business decisions.
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You know, we're talking about the threats here. I think it's important to switch gears and this segues really well to discuss the generative AI opportunity in cybersecurity. In many ways, cyber security teams must fight fire with fire here to defend against this generative, these generative AI powered threats. So maybe to set the stage, you mentioned this earlier that you know, in the early days, kind of, you know, we're here talking early days of traditional AI and machine learning is already being used to tackle cybersecurity challenges. So maybe before we talk about how, how generative AI specifically accelerates cybersecurity efforts, walk us through how traditional machine learning is used in cybersecurity today.
Yeah, I mean traditional machine learning has been used for years. I mean it's basically statistical analysis. It tries to make predictions based on data attributes or trends. Right. So I'm seeing a trend go look here, or this trend is more like a trend that I've already identified as something I don't need to look. Right. And so these traditional ML models, they've been around for some time in cybersecurity to detect threats and, or anomalous behavior. Right. And that's really using that type of ability has been something we've been working with for a long time.
That's really great. And when you mention here, for example, generative AI being able to automate a lot of the mundane tasks that cybersecurity teams have, maybe give us an example of what type of tasks here are going to be automated or augmented. And how can generative AI help cybersecurity teams find the needle in the haystacks? You know, proverbially speaking here, a good.
Example is earlier this year we launched our phishing analyzer tool. And what it does is it analyzes the text and email. You've probably gotten training before. When you get an email that you think is phishing, forward it to phishing at whatever you need to forward it to and somebody in security is going to look at that. Well, that takes a ton of time and it's not very interesting work for someone in security just to be checking an email box. So we can use these models. And our phishing analyzer essentially just analyzes the text of the email to determine is it a phishing attempt, is it not a phishing attempt? We can auto isolate. There's so many things that we can do based on the behaviors that we've studied for the past decade. And that's why having data is so important in AI, is having the data to Train the behavior of the model. And so Phishing Analyzer is an example. Another example where we're using ML models is our customers get thousands of alerts per day. From a security perspective, it gets really noisy. And so the challenge in security is figuring out what's noise and then what needs to be investigated further, like what's noise, what can I ignore? And then where do I really need to focus my time because time is limited. And so as alerts fire, our ML model is able to compare that alert to past alerts to see if there's similar trends, to see if there's maybe something that we've learned that we know every time we don't need to worry about. Can we trace it back to the same issue? Is it a duplicate thing that's happened? So if you just weed out duplicates, weed out noise, that allows your security team to focus on things where there might be something. Right. And so they can free up time and visibility for the things that really matter.
Okay, that's really great. And you mentioned here the security team working hand in hand in a lot of ways with these AI tools and generative AI tools in their daily workflows. And I think, you know, we often talk about the difference between automation and augmentation when leveraging generative AI tooling at work. And I think cyber security teams are a great example of how generative AI can augment the current workflows of a particular team. Maybe. How do you view the skills of cyber security teams evolving as generative AI tools become more and more embedded in their workflows? What do you think are the primary skills they need to learn and where do you think, you know, what needs to change in the skills makeup of a cybersecurity team to become effective in using AI?
I think the dependency, if you look at, you know, prior to automation, and really we're still in the middle of this transition now of, you know, a security professional was really required to learn how to do use specific tools. It was really, you would talk to people in security and they would talk about their expertise around a certain type of technology, not even a category, but like a specific tool. And that's not really security. We want people thinking about the data, thinking about where that data came from, thinking about the meaning of that data to the organization, why it's important in helping the organization make accurate business decisions with accurate security information, not managing a tool, not running a technology. Right. We want us interpreting the security information to help make business decisions with it. And so I think what will happen is that will actually help evolve the careers of our cyber professionals faster. If you talk to most people in cybersecurity, they don't like the mundane tasks. They don't like managing the tool. They want to be doing the offensive stuff, hunting in people's environment, and they want to be advising the business. They want to be in front of their company's mergers and acquisitions team and helping to vet the company they're looking at acquiring based on their security protocols to see how hard it is or isn't going to be to roll them in the daily operations. I mean, there's so much value in the data that a security team gets access to. They could really be better advisors to the business. And AI is going to make us require less of our time to be spent managing a tool and making sure it works. It's like having a sports car and it's just sitting on cinder blocks in your garage leaking oil. That's no fun. I'd rather be out driving the sports car. And so we want them to drive the car instead of constantly be working on it in the garage.
