AI Maturity Curve: Where Most Security Teams Actually Are

CISO Global AI Division

AI Maturity Curve: Where Most Security Teams Actually Are

There’s no shortage of vendors promising plug-and-play AI for cybersecurity. Dashboards light up. Detections appear smarter. The marketing says you’re now using “AI-driven defense.” But beneath the surface, most security teams aren’t nearly as far along the AI path as they (or their leadership) believe.

There’s a gap between aspiration and implementation. A maturity curve, if you will. And a lot of organizations are still hovering near the bottom.

It usually starts with simple automation. A script that enriches IP addresses with threat intel. A SOAR playbook that isolates a host when certain conditions are met. This is useful. It saves time. But it’s not AI. There’s no learning involved. No modeling. No abstraction. It’s just conditional logic.

The next step is often tooling up. Teams buy products that advertise “machine learning” or “AI-enhanced detection.” They trust the vendor’s definition of AI without understanding how it actually works. They assume the product is making intelligent decisions when, in reality, it might just be clustering based on thresholds or running predefined statistical models. Still not AI in the way most people imagine it.

Then comes experimentation. A few team members start playing with open-source tools or APIs from the bigger AI labs. Maybe they use a language model to generate reports or summarize alerts. Or they feed past incidents into a model to explore anomaly detection. But this is usually being done off the side of someone’s desk, with no formal process, no risk assessment, and no governance. It’s the early “let’s see what happens” phase that maybe exciting, but immature.

Delivering Value

Real maturity doesn’t come until AI is integrated into the operational fabric. That means retraining models with your own data. Understanding their behavior under different conditions. Knowing where they break. It means building confidence over time in what the model outputs, but also in the process around it: the data quality, the decision logic, the human-in-the-loop validation. It’s slow, deliberate work. Not many teams are doing it yet.

Worse, a lot of organizations get stuck in a kind of performative adoption. They buy the tools. They label a few reports “AI-enhanced.” They mention it in board decks. But there’s no shift in how they operate. No new insights being generated. No measurable improvement in detection or response. Just the appearance of progress.

And that appearance can be dangerous. It creates a false sense of competence. Executives assume the team is now “AI-powered” and that somehow this means faster, better, cheaper outcomes. Meanwhile, the team is still drowning in alert fatigue, struggling with tool integration, and doing the same triage they’ve always done just with a shinier interface.

Understanding the AI maturity curve is about resetting expectations. It’s about recognizing that there’s value at each stage (yes, even in basic automation) but that calling it all AI muddies the waters. Worse, it creates pressure to claim capabilities that don’t exist. That leads to shortcuts, overreliance, and, ultimately, disappointment.

Hype Cycle

This is the classic hype cycle: inflated expectations, disillusionment, slow climb to productivity. We’ve seen it before with other technologies. AI is just the latest. And just like those others, it’s not going to deliver value until we stop treating it like magic and start treating it like infrastructure.

Security teams that are mature in their use of AI know how to say “we’re experimenting” instead of pretending they’ve mastered it. They understand where the tools help, where they fall short, and when a human needs to step in. They focus less on the label and more on outcomes. Did this improve our ability to detect threats? Did it help reduce dwell time? Did it let our analysts spend more time thinking and less time clicking?

The truth is, most teams are still early in this journey. And that’s okay. What matters is not claiming to be “ahead of the curve”, it’s being honest about where you are on it, and taking deliberate steps forward.