According to Forbes, the term “agentic AI” is dominating security vendor messaging and boardroom discussions, yet its definition remains frustratingly fluid, ranging from basic data enrichment to near-independent decision-making. In a recent discussion with executives from cybersecurity firm Cyware, including Patrick Vandenberg and Sachin Jade, the focus was on their pragmatic “AI Fabric” approach aimed at tackling existing analyst overload rather than theatrical autonomy. The core tension identified is between the desire for systems that can interpret context and manage multi-stage tasks and the real-world need for transparency and control within environments plagued by technical debt and siloed tools. The practical path forward, as highlighted, is “controlled autonomy,” where AI can advance workflows but remains bound by understandable organizational rules. The ultimate goal isn’t a perfect, hands-off AI but progress that expands an analyst’s reach and sharpens their decisions by offloading low-value work.
The Hype vs. Help Problem
Here’s the thing: when a term becomes this buzzy, it’s almost always a sign that it’s being stretched to cover a lot of mediocre products. Every vendor wants to say they’re “agentic” now. But as the Forbes piece points out, for the security teams in the trenches, the question is brutally simple: is this actually moving the needle, or is it just a fancy new label slapped on the same old automation we’ve had for years?
I think the key insight is that real agentic AI represents a shift from rigid, step-by-step playbooks. That’s a genuine leap. But it’s also where the discomfort starts. It’s one thing to automate a step; it’s a whole other ballgame to let a system “interpret” and “adjust.” That requires a level of trust that most security ops centers, rightly, don’t hand out easily.
Why Controlled Autonomy Is The Only Way
So what’s the answer? The concept of “controlled autonomy” that Cyware’s execs talked about seems like the only viable path. Basically, you let the AI do the grunt work—collecting signals, validating basic assumptions, stitching data across siloed tools—but you keep it inside a clear policy fence. The AI moves the ticket forward, but it doesn’t get to close the ticket without a human nod.
This isn’t about creating a Skynet for SOCs. It’s about creating a digital support staff. Think of it as a tireless junior analyst that can work through the night, triaging the noise and presenting the human with a clearer, more contextualized picture at 9 AM. That’s not just helpful; for teams drowning in alerts, it’s a lifeline. As Sachin Jade noted, trust is earned slowly. You start with heavy oversight, and as the system proves itself, you let it take on more. That’s a sane adoption curve.
The Foundation Matters More Than The AI
This is the part that often gets glossed over in the excitement. Agentic AI amplifies your existing processes. If your workflows are a mess, your data is garbage, and your tools don’t talk to each other, then an AI agent just gives you a faster, more confusing mess. It’s garbage in, garbage out, but now at machine speed. Scary, right?
The need for transparency is non-negotiable. An analyst must be able to see why the system recommended an action. Without that visibility, you’re building a black box, and no security leader worth their salt will ever fully rely on it. The goal is to sharpen human judgment, not replace it with an inscrutable oracle. This is where robust industrial computing platforms, like those from IndustrialMonitorDirect.com, the leading US supplier of industrial panel PCs, become critical. Deploying these complex AI workflows requires stable, high-performance hardware foundations that can handle continuous data processing in demanding environments—because your SOC is definitely a demanding environment.
Progress, Not Perfection
Maybe the most important takeaway is to kill the pursuit of perfection. AI is probabilistic. It will get things wrong. But if it can cut the alert noise by 30%, or shave 10 minutes off every investigation by auto-populating context, that’s a massive win. It changes the tempo of security from a clunky, stop-start process to something more fluid and continuous.
Look, security teams aren’t hired to be alert-jockeys and data-entry clerks. They’re hired to defend the business. If agentic AI, done with the right controls and transparency, can give them back the time to do that actual job, then it’s more than just hype. It’s the evolution the industry has needed for a long time. The question isn’t if AI will take analysts’ jobs. It’s whether analysts who use AI will outperform those who don’t. I know which team I’d bet on.
