How Huntress Uses AI to Support Employees and Build Cybersecurity Products

What It's Like to Build an AI-Native Career at Huntress

At Huntress, Enablement Lead Maisaa Saadeh draws a line between AI that makes you a little faster at what you already do and AI that takes an entire task off your plate. The first is table stakes. The second is the bar she holds her team to, because it frees people for the work that actually needs human judgment.

While AI has been a "huge force multiplier" across every department at the cybersecurity company, Saadeh said the goal is not volume. It is giving employees more time for work that requires human judgment.

The example she points to first is not a product. It is a change in who gets to build. Six months ago, she had people in her training sessions who had never written a prompt. Some of them now build working tools their teams rely on.

"Watching someone go from 'I don't really get this' to 'look what I made' in that window is the part of this job I didn't fully see coming," she said.

As Huntress continues to grow, its leaders are looking for candidates who are eager to leverage AI to optimize their day-to-day work and unlock new opportunities for the business.

Below, Saadeh shares more about how Huntress is using AI to boost efficiency and develop impactful products, the purpose of the company's AI council, and what makes the company a great place to build an AI career.

Maisaa Saadeh

Enablement Lead • Huntress

Explain how AI is used in your organization. How is it connected to your product or product development?

On the product side, our engineers use tools like Claude to prototype features and identify code optimizations. The point isn't volume, it's quality. They're shipping better code, not just more of it. Our security analysts use AI and automation to take the repetitive parts of an investigation off their plate, the data gathering and timeline work, so their judgment goes to the decisions that actually need a person.

What's less obvious from the outside is that our AI usage isn't limited to the technical teams, and that wasn't an accident. We deployed it to reach everyone. People in marketing, sales, and operations are building their own automations now, because the enablement was designed for them too, not just the engineers. That's the part I'm most proud of, and it's the part that, unfortunately, most other companies skip.

Tell us about a project or milestone involving AI. What was its purpose, and what was it like to work on?

The milestone I point to isn't a product launch. It's a shift in who is able to build.

Six months ago, I had people in training sessions who'd never written a prompt and were a little unsure about the whole thing. Some of those same people are now building working tools that their teams actually rely on. Watching someone go from "I don't really get this" to "look what I made" in that window is the part of this job I didn't fully see coming. The part of my job that I always tell people is my favorite is what I call the "lightbulb" moment. It's when a concept clicks for someone when it was a daunting idea just seconds before. I take great pride in the fact that so many people on various teams felt empowered to explore the possibilities of what could be built with AI. Those are the pivotal moments that tip the scales and change the way they work.

"The milestone I point to isn't a product launch. It's a shift in who is able to build."

How Huntress Supports Employees in Their AI Adoption

Are there any unique approaches or philosophies to AI or data management that you or your team have?

A few. The first is that our leadership centralized this in an AI council instead of letting every team improvise. It owns governance, vets tooling, and drives enablement. That sounds like overhead, yet it's the opposite. It's why we moved quickly, because we weren't solving the same problem in six places at once.

The second is one I feel strongly about. We don't measure adoption by the numbers that look good on a slide. Although we track AI spend, that's not an adoption metric, because the research on self-reported ROI is shaky, and people tend to report what they think you want to hear. I'd rather measure whether someone's actual capability changed. It keeps us honest about what's working and what's just noise.

The third is unglamorous but real. We had our data reasonably organized before AI arrived, concentrated in a few core repositories instead of scattered everywhere, so searching across it and connecting tools to it actually worked. Many AI programs quietly stall on exactly that problem.

What Makes Huntress a Unique Place to Build an AI Career

What technical aspects of your work are you most proud of, either individually or as a team?

What I found truly admirable is that we resisted the easy way out. The easy version is handing everyone a tool, celebrating the rollout, and never checking whether it changed anything. We built the language, the guidance, and the measurement around it instead, so we actually know when it's working.

On the technology itself, I'd separate two things people tend to blur together. AI making you a little faster at what you already do is fine, but that's table stakes now. The part worth building toward is when it takes an entire task off your plate, something that never really needed a person, so the person can go do the work that does. That's a higher bar, and it's the one we hold ourselves to.

"The part worth building toward is when it takes an entire task off your plate, something that never really needed a person, so the person can go do the work that does."

What do team members working with AI at Huntress get to do that they might not be able to elsewhere? Why should an applicant interested in AI join your team?

You don't have to be on a technical team to build something real. And we don't just teach the tools, we spend real time on how to think about a process before you automate it, because automating a broken process only gets you a faster broken process. I'd also add that nobody gatekeeps knowledge. The council meetings are open to anyone, so there's no curtain around how decisions get made.

We're not looking for people who already have it all figured out. We want people who are willing to learn, because that's where the interesting stuff comes from. The innovation shows up when someone opens up to how AI could change the way they actually work, not when they already know all the answers on day one. You don't have to be an engineer or an "AI expert." You just have to be curious enough to try.

"You don't have to be an engineer or an 'AI expert.' You just have to be curious enough to try."

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