Reimagining the SOC Analyst Role Using AI - What is Actually Realistic?
Cybersec Café #60 - 03/25/25
As the world becomes increasingly technology driven, AI continues to weave its way into more industries and seep further into our daily processes.
We’ve seen it reshape software engineering over the past few years, gradually make its way into nearly every SaaS product over the last two, and now it’s starting to drip down into the world of cybersecurity.
In Security Operations, AI is already being used to enhance threat detection, automate tasks, and improve incident response processes.
But with the growing prevalence of AI agents, it got me thinking: What role does AI realistically play in the SOC Analyst role moving forward?
If you’ve followed me for a while now, you might remember the lightweight AI SOC Analyst I built a few months back that integrates directly into SOAR workflows.
And while I firmly believe AI will continue to play an integral role in shaping the future of SOCs, I also think we’ll run into limitations on how it can be used without increasing risk.
Where AI Analysts Thrive
To kick things off, let’s talk about where AI can genuinely improve the lives of SOC Analysts.
I believe AI can serve as a powerful sidecar assistant in any engineer’s workflow - and SOC Analysts are no exception.
Context & Suggestions
AI shines during alert triage by providing quick, contextual suggestions to Analysts.
These AI models have become very good at rapidly processing evidence, interpreting an alert’s context, and offering insights on how to triage further. This makes it an excellent tool for accelerating decision-making and reducing the cognitive load during day-to-day operations.
But, the fidelity of these suggestions is entirely dependent on the quality of the engineering behind the solution.
If you inspect the code from my AI SOC Analyst article, I demonstrated how granular prompts and ample evidence from SOAR tasks are necessary for AI to provide trustworthy and actionable insights.
Without this, AI analysis has a high risk of inaccuracy.
And while this level of depth is extremely valuable, it does heavily rely on a robust Detection Lifecycle to hone the quality of detections and workflows.
Delivering Reports
Beyond triage, AI is also great for continuous reporting and insight into your attack surface.
By configuring your AI SOC Analyst to track detection and SOAR fidelity, you can receive ongoing feedback on your security posture.
Some use cases you can focus on from the start are:
Automating real-time detection assessments for each alert that fires.
Schedule regular reports on SOC performance, highlighting gaps or noisy detections.
But again, this requires some light engineering up front to ensure reports are actually valuable - otherwise, you’ll just end up with more noise (which we know, no SOC needs).
Giving Time Back
Anyone who’s worked in a SOC knows the majority of alerts are just noise - there’s no better way to say it.
An AI SOC Analyst excels at filtering out the low-hanging fruit: alerts that you want visibility over, but don’t want to waste time triaging.
By automating contextual summaries, triage suggestions, and basic alert resolution - AI allows SOC Analysts to escalate or close alerts in a matter of seconds rather than minutes.
The result? More time spent on valuable work to reduce your attack surface rather than wasting time on repetitive, low-fidelity signals.
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Where AI Analysts Falls Short
If I were a betting man, I’d wager that after reading the previous section, you’d assume that my first point about where AI falls short would lie in the engineering itself.
But you’d be wrong.
In my opinion, AI SOC Analysts fall short–and will always fall short—in one critical area: Confidence.
The Confidence Problem
What happens the one time an AI Analyst gets it wrong?
Let’s take a step back and look at it statistically - Achieving 100% confidence in any statistical analysis is nearly impossible.
Why?
Statistical models are based on samples, not entire populations. This leaves room for random errors and unknown variables, no matter how large the sample size.
AI models are no different. They rely on probabilities and make decisions based on historical data and patterns.
In layman's terms, AI models are reasoning to the right answer based on a massive pool of prior knowledge. And while they may guess correctly most of the time, they’ll never be correct 100% of the time.
So what does that look like in the SOC?
Well, anyone who has triaged alerts before has had that one gut-check moment, the one where everything seems normal but feels off.
Maybe the alert looks benign, but a small detail made you hesitate. You double-check the logs, perform your investigation, and then you find that needle in the haystack.
AI, for all its power, lacks human intuition - making it inherently risky in a high-stakes environment like the SOC.
Will Trained Models and Agents Make a Difference?
Sure.
I have no doubt that more data will decrease the margin for error.
But realistically, at what point can you confidently say you’ve fed enough data through your AI agent to consider it reliable?
Bringing it back to statistics, larger sample sizes do increase confidence. But is it ever realistic for a small-to-mid sized company to generate enough data to properly train their own AI Analyst?
Probably not.
There’s so many variables to consider. Every environment is different. Detection engineering requires critical thinking and contextual understanding. And edge cases will always exist.
No matter how trained your AI model is, it will never be right 100% of the time - and introduces inherent risk into a field where risk is already difficult to accept.
- What Do You Think? -
How do you see AI SOC Analysts Evolving in the next few years?
💬 Drop your thoughts below - I’d love to hear your perspective!
What’s Realistic for an AI SOC Analyst
Even as AI agents become more and more prevalent, I don’t ever see a world where AI SOC Analysts fully replace their human counterparts.
The problem simply lies with risk.
In cybersecurity, it’s incredibly difficult to accept avoidable risk - especially when it comes to detection and response. The stakes are too high.
Even if AI reduces the overall margin for error, the occasional mistake could still carry catastrophic consequences.
Now, I get it - there’s a valid counterpoint to be made here: Humans are statistically more prone to errors than AI models.
And I don’t disagree.
But here’s the thing: Where humans fall short, AI thrives. And where AI falls short, humans thrive.
AI is exceptional at being detail-oriented, identifying patterns at scale, and detecting anomalies.
But humans will always excel in understanding business and organizational context, creative problem-solving, and communication with stakeholders.
That’s exactly where I see things heading - a hybrid SOC where AI enhances, but doesn’t replace, human analysts.
I imagine architecture will look something like this:
AI becomes the first line of triage, providing context, identifying blind spots, and offering escalation suggestions - all with confidence ratings on its assessments.
Teams will toggle which confidence and severity thresholds warrant human escalation.
SOC Analysts function on an on-call rotation, where they are only passed alerts that meet this threshold.
AI Analysts continuously deliver feedback loops to engineering teams.
Analysts and Engineers have more time to collaborate and improve tools and processes.
A hybrid SOC is an efficient SOC.
But at the end of the day, it comes down to your organization's risk tolerance.
For a small company with limited resources, an AI SOC Analyst might be the perfect fit - streamlining triage and reducing noise.
But for larger organizations with a larger attack surface and the ability to hire high-quality security talent - maybe deep AI integration introduces too much unnecessary risk.
While we shouldn’t be standoffish towards AI in this industry, we need to be deliberate about how we use it.
AI shouldn’t be viewed as a replacement, it should be an enhancement.
Securely Yours,
Ryan G. Cox
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