Automation vs. AI: Dispelling Myths and Misconceptions

CISO Global AI Division

It’s everywhere. Every security tool now supposedly has “AI” baked in, whether it’s a firewall, SIEM, email filter, or endpoint agent. It’s the badge of innovation. If your product doesn’t have it, or if your team isn’t using it, you risk looking behind the curve. But the truth is, what’s often labeled as AI is anything but. A lot of it is just automation with a new sticker slapped on the front.

Let’s be clear: automation is valuable. Automating repetitive tasks, whether it’s log parsing, enrichment, or running response playbooks, saves time and reduces human error. But it’s not artificial intelligence. It’s a workflow. It’s a script. It’s an “if this, then that” decision tree, even if it’s wrapped in a slick UI and marketed like the second coming.

AI, when it’s actually present, is something more. It should be capable of learning, generalizing, or spotting patterns beyond what’s been explicitly coded. It should be synthesizing something new. It should be able to surface insights a human might miss after drowning in terabytes of alerts and logs. If it can’t adapt or reason or surprise you, then it’s not intelligent. It’s just obedient.

That’s where the line gets blurry for a lot of folks. Just because something uses Python, or has “machine learning” in a marketing deck, doesn’t make it AI. A basic rules engine is still a rules engine, even if it has an impressive logo and sales pitch. And a generative tool that rearranges what’s already been written which has sometimes been described affectionately (or derisively) as “spicy autotext” isn’t the kind of AI we should hold up as the future of our field. It may generate convincing language or code, but it’s remixing. That’s not thinking.

Part of the problem is the pressure. There’s this creeping sense that if you’re not integrating AI, you’re already obsolete. People scramble to adopt something (anything!) that lets them say, “we’re doing AI.” It’s like we’ve collectively forgotten how technology matures. Gartner’s Hype Cycle is real. We climb this mountain of inflated expectations, convinced this time the buzzwords will deliver magic, only to tumble into disillusionment when we realize the tools aren’t there yet, or they were built on shaky assumptions. Eventually, yes, we find a steady plateau. But getting there requires realism.

This isn’t to say AI isn’t useful in cybersecurity. Quite the contrary. There are places where real AI, applied carefully, does make a difference. Pattern recognition at scale. Clustering anomalies. Prioritizing what a human should look at next. But even then, it’s not a replacement for human judgment. Not in detection, and certainly not in response. Humans bring context, curiosity, and skepticism. These are things models don’t have and perhaps never will.

Which brings us to ethics. If your organization is playing with AI, it’s time to ask hard questions. What kind of data are you feeding into these systems? Who approved that? If you’re inputting confidential case notes or sensitive internal metrics into a public or third-party model, you may be handing over more than you realize. These systems learn by example, and in doing so, they often build a profile on the user. You. Not to mention that the results they return might be factually incorrect, out of step with your company’s tone, or even laced with copyright from elsewhere.

Ethical use means setting boundaries. Don’t feed it proprietary data unless your organization has reviewed and approved the tool. Validate outputs before using them externally. Accept that these systems, while powerful, still hallucinate and misfire. And remember that human oversight isn’t optional. It’s the only way this works.

Everyone wants to be a leader in AI right now. But very few actually are. Most are somewhere between curiosity and experimentation. That’s fine. That’s honest. The real goal isn’t to eliminate humans but to free them up. Let them work on higher-value tasks. Let them govern, audit, guide. Because that’s what maturity actually looks like: knowing what the tech can do, what it can’t, and where it needs a human in the loop.