Role of AI in Incident Response: Hype vs. Value (Part 1)

CISO Global AI Division

Vendors love to pitch AI as the future of incident response. Faster. Smarter. Hands-free. But look past the slogans and you’ll find a much more grounded story.

Here’s what AI is actually good at in IR: parsing volumes of data quickly, surfacing patterns, and suggesting correlations. It can help identify related alerts across systems. It can prioritize based on likelihood. It can draft summaries or timelines once the facts are in. All useful.

But here’s what it can’t do: make judgment calls. Understand organizational context. Decide when to escalate. Talk to legal. Communicate with executives. Those are human jobs and they’re not going away.

The danger is thinking the tool will run the incident. It won’t. Not reliably. Not yet. And if we hand it the reins too early, we risk missteps we can’t take back. The smart move is to use AI as a force multiplier and not as the leader. Let it speed up the repetitive work. Let the humans focus on strategy.

Real incident response isn’t just technical. It’s political. It’s narrative. It’s about risk and reputation. Machines don’t understand that. So use them, yes. But don’t worship them.

Ask any vendor and they’ll tell you: AI is revolutionizing incident response. It’s the dawn of hands-free security operations, where your SIEM thinks, your SOAR acts, and your analysts… what, exactly? Watch?

It sounds great, but the reality is more nuanced and more grounded.

Let’s talk about what AI actually helps with:

• Speed: An AI system can scan log data and flag anomalies far faster than a human.
• Correlation: It can spot patterns across multiple data sources and suggest relationships that might otherwise be missed.
• Summarization: After an incident, AI can help write the post-mortem faster, using structured data as a prompt.

These are real benefits. They reduce time-to-know and time-to-communicate. They reduce toil.

But there’s a catch. AI still lacks critical capabilities:

• Context: It doesn’t know your business. It doesn’t understand what a high-value asset means to your org.
• Judgment: It can’t weigh the political implications of a breach or assess the risk appetite of your board.
• Ownership: It doesn’t take responsibility when things go wrong.

What you’re left with is a tool that assists but cannot decide. It’s not a replacement for your incident commander. It doesn’t coordinate teams, talk to legal, or brief executives. It won’t understand what’s at stake when a system goes down before payroll.

That’s why the hype is dangerous. If we think AI will “handle the breach,” we’re inviting complacency. Instead, the right frame is augmentation. Use AI to pre-process data, prioritize alerts, draft timelines but let trained humans lead.

That’s where the value is. The faster the machine gets you to the decision point, the more room your people have to apply judgment, communicate clearly, and contain the damage.

AI won’t save you. But it might give your humans enough lift to keep up.

AI gets a lot of credit for transforming incident response. It’s sold as the key to speed, scale, and reduced human workload. The idea is simple: let the machine do the heavy lifting, detect anomalies, prioritize alerts, suggest responses, and maybe even close the loop automatically. On paper, it sounds like the answer to burnout and backlog. But when you strip away the marketing language, most of what’s in place today is far from that vision. It’s valuable, but not magic.

To understand where AI fits, you have to separate the promise from what actually ships. Most AI in IR today is really pattern recognition and workflow automation dressed up as intelligence. Correlation engines that surface related events aren’t thinking. They’re using known relationships or statistical proximity to suggest context. Language models that summarize incidents aren’t analyzing them. They’re rephrasing what you already have. The benefit is real, but the process is still reactive. You’re not stopping the breach faster, you’re just getting the same story in slightly less time.

That’s not to say there’s no place for AI in IR. There is. But it’s not where people think. You don’t need AI to run a playbook. You need it to show you something you weren’t expecting. The actual value is in surfacing outliers, connecting seemingly unrelated behaviors, or flagging changes in attacker tactics that might get missed in the noise. But even then, the system can’t tell you whether it matters. It doesn’t know your environment, your thresholds for risk, or your operational constraints.

Analysts often find themselves rechecking AI-generated summaries and classifications because the outputs don’t align with what the humans actually observe. The tools don’t know what’s “normal” unless you feed them enough examples, and even then, they tend to chase the dominant pattern. Anything novel or context-specific still requires manual review. And in a real incident, you want clarity, not speculation.

The real power of AI in IR is about narrowing the field. It can suppress duplicate alerts, prioritize based on context (if configured well), and assist with ticket enrichment. These functions make life easier for analysts, particularly in high-volume environments. That’s a win. But it’s a supporting role, not a starring one. When AI is marketed as the centerpiece of IR, organizations run the risk of over-trusting it and under-investing in their human teams.

The problem is further compounded when vendors overstate their capabilities. Words like “autonomous,” “self-healing,” or “zero-touch” show up in brochures, but few systems deliver that in practice. What you often get is a glorified rule engine or a natural language wrapper on a traditional toolset. There’s a name for this phenomenon: spicy autotext. That’s how some engineers describe generative AI that sounds impressive but doesn’t do much beyond paraphrasing. If that’s what your incident response relies on, you’re not accelerating your capabilities, you’re just decorating them.

And yet, the pressure to “do AI” in IR is intense. Executives want to report it to boards. Security leaders want to stay ahead of the curve. But being early doesn’t always mean being right. There’s a hype cycle here, just like there is with every new technology. First comes inflated expectations, then disappointment, and eventually a more realistic integration of what the technology can actually offer. Most organizations are still stuck in the inflated phase.

There’s also a governance issue. When you let machines suggest or automate actions in the middle of a breach, you have to ask: who’s responsible if it gets it wrong? Who explains the decision to leadership or regulators? If you can’t answer that, you’re not ready for AI in IR.

You’re just gambling.

A mature AI-assisted incident response program has humans at the center. Machines help them see faster, think deeper, and act with better information. But they don’t decide. And they don’t replace the need for training, judgment, or institutional knowledge. To get value from AI in IR, focus on the boring parts first. Clean data. Structured logs. Consistent ticketing. Defined playbooks. Without those foundations, even the best AI will produce garbage. But with them, you can start to build a system where AI isn’t the hero, but a valuable assistant. One that helps your people do their jobs better and not one that pretends it can do the job for them.