Future of AI: What’s Next, What to Watch Out For

CISO Global AI Division

The hype will fade. It always does. But something useful will remain.

In security, AI’s future is probably less about autonomous decision-making and more about augmentation. Triage assistance. Predictive analytics. Smarter correlation across diverse telemetry. Think co-pilot, not captain.

We’ll also see more adversarial AI. Attackers are using AI to craft phishing, automate recon, or even probe defenses. Defenders will need models that detect machine-generated threats. AI vs. AI.

But we also need to watch for overreach. Systems that claim to “think” but really just paraphrase. Tools that act without oversight. Teams that chase AI adoption because it looks good, not because it solves a real problem.

The future will require maturity. Discerning buyers. Responsible implementers. Honest marketers. And humans who understand both the promise and the limits of what these systems can do.

We’re not there yet. But we’re headed in that direction… slowly, with eyes open.

The AI buzz isn’t going away, but the pendulum is already swinging. The early phase where everything was branded as “AI-powered” is giving way to a more sobering reality: most organizations aren’t seeing magic. They’re seeing modest gains, a few headaches, and the realization that AI doesn’t think, it predicts.

That said, some real shifts are coming.

We’ll see tighter AI-human loops. Systems that suggest next steps and ask for validation before acting. AI will assist with threat hunting, anomaly detection, incident summarization, and possibly even some policy generation. But always with a human in the loop.

We’ll also see continued adversarial AI. Attackers using generative tools to create more convincing phishing campaigns, more dynamic malware, and faster reconnaissance. It’ll be AI versus AI with defensive models learning to detect synthetic behavior, while offensive models adapt to avoid detection.

And there’s the economic angle. AI won’t replace analysts, but it might increase the productivity gap between teams that have AI and those that don’t. That’s going to create pressure on budget, staffing, retention.

What to watch out for? Overconfidence. Tools that act without oversight. Organizations that cut analyst headcount thinking the machine can cover the gap. It can’t. Not sustainably.

This is a long curve. We’re still near the top of the Gartner hype cycle. Disillusionment is coming. And after that (maybe) something stable and useful. If we build carefully.

There’s a lot of noise right now about what AI will become. Everyone has a prediction. Some are convinced it’s going to eliminate half of all jobs. Others think it will unlock a golden age of innovation. Security vendors promise fully autonomous SOCs. Executives are told they need an AI strategy or risk being left behind. But in all this excitement, a simple truth often gets lost: we’re still figuring out what AI is actually good at, and that discovery process is going to be messy.

Right now, we’re in the inflated expectations phase. Everything that even smells like automation gets called AI. Basic correlation? AI. Email filters? AI. Spreadsheets with macros? Somehow, also AI. The term has become a kind of marketing duct tape, slapped over old tech to make it sound modern. That won’t last. Eventually, people will start asking harder questions, and the products that were all smoke and mirrors will fall away.

The path from here to useful, trusted AI is more evolution than revolution. Expect incremental gains. Better data enrichment. Faster triage recommendations. Smarter alert grouping. Tools that suggest likely root causes based on historical patterns. Those are useful. They save time and reduce noise, but they’re not magic. They still require humans to validate and act. The danger is in thinking we’re closer to artificial general intelligence than we actually are. We’re not.

If anything, the real breakthroughs may come from boring places. Improved log normalization. More robust tagging of assets and identities. Better frameworks for sharing threat intelligence across orgs. These foundational improvements are what allow AI to be effective. Without them, you’re just throwing algorithms at bad data and hoping for insight. The tools that quietly improve context and visibility will outperform the ones that overpromise and underexplain.

Another thing to watch for: the way AI reshapes job roles. It’s not going to wipe out the analyst. But it will change what the analyst does. There will be less time spent hunting through logs line by line, more time validating AI-generated hypotheses or working on strategy. The skill set shifts from technical grunt work to critical thinking, domain understanding, and judgment. We should be training people for that now.

Expect a correction in the market as well. Some companies will realize they spent too much on tools that don’t deliver. Others will double down on internal development. Over the next few years, we’ll probably see fewer all-in-one platforms claiming to be the brain of your SOC, and more focused tools that do one thing well and integrate cleanly. Modularity is going to matter. So will transparency. If you don’t know what your AI is doing or why, you can’t trust it.

Then there’s governance. We’ve barely started having the real conversations. Who owns the output? How do you audit a decision made by a model? Can an AI recommendation be used as evidence in an investigation? What happens when the model fails silently, or worse, subtly? Security teams are going to need new processes, not just for using AI, but for overseeing it. Humans aren’t going anywhere, because governance doesn’t scale the way compute does.

And on the cultural front, expect backlash. People are already getting tired of every product claiming to be AI-powered. The novelty is wearing off. What matters now is trust. Does the system make you better at your job? Can it explain itself? Does it behave consistently? If not, it’s a distraction, not an asset.

There’s no doubt AI will be part of cybersecurity for the long haul. But the hype needs to cool. The tools need to mature. And the people using them need to stay skeptical. Progress won’t look like a breakthrough moment. It’ll look like analysts making better decisions a little faster. It’ll look like fewer missed alerts. It’ll look like time saved on noise so humans can focus on what matters. That’s the future. Not flashy. Not disruptive in the Hollywood sense. Just better operations, built one careful improvement at a time.