June 24, 2025
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There’s no shortage of headlines about AI reshaping cybersecurity. Depending on who you ask, it’s either the industry’s silver bullet or an overhyped distraction. Most professionals fall somewhere in between: cautiously curious but tired of the buzzwords.
And here’s the thing. AI is already in the mix. According to the SANS 2024 AI Survey, 66% of security teams use AI in some form today. It’s not a hypothetical anymore. But the gap between hype and real impact is wide, and teams trying to stay effective need clarity, not more flash.
So, instead of vague promises or fear-mongering, let’s examine where AI is making a difference and what smart teams are doing to achieve results.
Let’s start with some truth: AI isn’t magic. It doesn’t read minds, and it won’t handle your entire SOC while you sleep. But when used right, it saves time, reduces noise, and gives your team a serious productivity boost.
Security teams are using AI to:
That said, there’s a lot AI isn’t doing, and can’t do. It’s not strategizing, making judgment calls, or independently investigating threats. It won’t understand the nuance of a policy exception or interpret the complex business context behind a flagged incident.
Most teams use AI as a starting point. It drafts, it suggests, it parses. But it still needs a human to guide, double-check, and refine. AI isn’t replacing analysts. It’s helping them move faster and spend more time where it counts.
This isn’t just theoretical. Data from the MixMode’s 2024 State of AI in Cybersecurity Report shows that 64% of cybersecurity pros say their job satisfaction improved because AI took over the repetitive, time-consuming tasks. That’s a big deal in a field where burnout is a constant risk.
Here are a few numbers worth pausing on:
And behind the stats is something more human: AI is making security work feel less like firefighting and more like problem-solving. It’s helping junior analysts build confidence. It’s giving seasoned engineers more space to focus. It’s letting teams breathe again.
That’s real value.
You’d think dropping a powerful LLM into your SOC stack would be a game-changer. Not quite.
Many general-purpose AI tools (especially those built on large language models) still struggle with domain-specific context. They misinterpret environment-specific data, hallucinate when unsure, and often suggest actions that don’t align with an organization’s actual policies or infrastructure. While these models are excellent at generating human-like responses, they’re not trained on the realities of enterprise security operations.
These tools are trained on broad internet data, not the nuance of enterprise networks or security controls. They don’t understand the intent behind firewall rules, or how to weigh context like internal policy exceptions or active incident timelines. That gap between generic knowledge and situational awareness is where mistakes happen.
Only 18% of organizations say their AI implementation is fully mature, according to the MixMode report. That shows that most teams are still figuring out how to adapt generic models to real-world operational complexity. And more than half of surveyed professionals say they lack the expertise to even assess if these tools deliver on what they promise.
That’s why the focus is shifting. More teams are turning to purpose-built, function-specific tools designed to operate within defined security workflows. Ones that prioritize transparency, don’t overstep, and can be tuned to your environment.
Security folks are trained to question everything. So it’s no surprise there’s mistrust when vendors promise "no analyst needed" or "full automation out of the box."
Many practitioners say AI is only as good as the person using it. If your team doesn’t know how to prompt it or validate results, the tool becomes noise. Worse, it becomes a liability.
The teams getting the most out of AI are the ones still applying critical thinking. They use it to accelerate their work, not abdicate responsibility.
In short: Trust, but verify.
Forget the buzz. The best teams aren’t early adopters for the sake of trendiness. They’re strategic about it.
They use AI for structured, repeatable work, and always under supervision. It’s a trusted assistant, not a decision-maker. These teams build processes around it, measure its impact, and aren’t afraid to shut it down when it’s not working.
They’re also redefining roles. Job titles like "AI Security Analyst" and "Prompt Engineer" are coming up today. These aren’t vanity titles. They mark a shift: you now need people who understand both security operations and how AI models interpret, respond, and sometimes hallucinate.
What if your team could deploy an AI teammate built for a specific function, like threat intel, GRC, or incident response? One that doesn't just answer questions but fits into workflows, flags what matters, and helps keep the noise down without skipping the nuance.
These teammates wouldn't replace the role, but they'd handle the repetitive parts of it. They'd support analysts where it's needed most, giving your experts more space to do what they do best: think critically, investigate deeply, and act fast.
That’s what forward-thinking looks like: teams that use AI not to replace roles, but to rebuild them around human strengths and machine efficiency.
AI isn’t taking your job. But someone who knows how to use it well? They might.
The teams pulling ahead aren’t the ones betting everything on a flashy dashboard. They’re the ones cutting the noise, clearing time, and letting their people focus on the work that matters.
So maybe it’s time to ask: what could your team do with less grunt work and a bit more breathing room?
