By Jerry Derrick, Security Strategist and Senior Security Leader
For most of cybersecurity’s history, our work has been defined by reaction. We waited for the logs, the alerts, the calls from operations teams, and then we dug in. The best analysts could find the threat fast enough to stop the bleeding — but more often than not, the response began after the damage was already done.
Twenty years ago, “real-time” monitoring meant data that was eight to twelve hours old. Analysts pored over CSVs and Access databases, flipping through endless logs from network switches and servers. The move to SIEMs (Security Information and Event Management systems) helped speed things up, but it didn’t fix the fundamental problem — the overwhelming volume of data.
Every new device, every cloud service, every endpoint multiplied the noise. The challenge wasn’t collecting data anymore; it was making sense of it. That’s where AI has become the ultimate force multiplier.
AI Doesn’t Replace Humans — It Amplifies Them
Over the years, I’ve heard the same fear every time a new tool arrives: “Is this going to replace us?” The short answer is no. What AI actually does is take the repetitive, time-consuming analysis that humans have already mastered — and does it faster, at scale, and with the ability to pull context from more data than any team could process manually.
Think of it as freeing human analysts to focus on the next hard problem — the one that requires creative judgment, experience, and intuition. AI isn’t about replacing the human mind; it’s about amplifying it.
From Reactive to Predictive Defense
AI-driven systems now help teams move from reaction to prediction. By using machine learning to identify anomalies and correlate threat patterns, AI tools can detect the earliest indicators of compromise before an incident spirals into a full breach.
For instance, when Microsoft releases a patch advisory or a new vulnerability disclosure drops, AI can instantly map that information against an organization’s infrastructure, highlight at-risk systems, and even suggest or simulate remediation. In other words, we’re no longer chasing yesterday’s threats — we’re preventing tomorrow’s.
Finding the Needle in a Needle Stack
One of the biggest challenges in cybersecurity is the signal-to-noise ratio. Networks are noisy by nature — systems constantly talking, updating, and shifting traffic. The goal is to recognize what’s normal so you can spot what isn’t. AI thrives in this environment. Once it understands your system’s baseline behavior, it can instantly flag deviations — the new connection, the unusual time pattern, the data flow that just doesn’t fit.
That’s not something you want a human watching for in real time. But when the AI surfaces that insight, the human can immediately assess context and intent. It’s the best of both worlds — speed and precision combined with human judgment.
The Human Element Still Matters
Even with all the advances in AI, cybersecurity still requires human intuition. AI can hallucinate; it can misinterpret context. You still need a person to make the call — to approve a firewall rule, to validate a detection, to understand how a security decision aligns with business goals and risk appetite.
AI gives us speed. Humans give us wisdom. The future of cybersecurity depends on that partnership.
The New Arms Race
Let’s be clear: adversaries are using AI, too. Nation-states, ransomware groups, and organized cybercriminals are leveraging AI to probe networks and automate attacks. Humans alone can’t compete with that scale. The only way to fight AI-driven threats is with AI-assisted defense — systems trained and guided by human experience.
AI isn’t the future of cybersecurity. It’s the present. And those who learn to use it wisely will define what resilience looks like in the years ahead.
