AI Agents Never Come to the Office

AI is already part of security workflows. The question is whether it can handle the full flow.

We got very good at detecting everything

The cybersecurity industry crossed $255 billion in 2025. The vast majority of that investment went toward a single mission: see more, detect more, alert more.

And honestly? It worked. Over the past decade, the industry built an extraordinary detection system. SIEM laid the groundwork for centralized visibility. EDR brought the fight to the endpoint. SOAR tried to automate what humans couldn’t keep up with. And now, the AI wave is adding yet another layer of detection intelligence on top.

We are better than ever at knowing something happened. And that’s exactly the problem. The more we see, the more we generate. More alerts. More noise. More decisions.

Teams are still drowning

Let’s be honest about the state of things. The average organization deals with thousands of alerts a day, many of them false positives, and most never get addressed.

This is alert fatigue. Not as a buzzword, but as an operational reality. When everything is urgent, nothing is. Humans aren’t built for maintaining perfect attention across thousands of signals, day after day, shift after shift.

This is exactly where AI agents enter with a compelling promise. They don’t get tired. They don’t lose focus. They can process thousands of alerts without a single one slipping through the cracks. For the first time, alert fatigue has something that can actually step into the ring and fight it.

But AI agents have a problem humans never had

Humans were never great at handling alert volume, but they were good at making sense of a ticket.

Take a simple case: a DLP policy blocks an email. On paper, it’s just another alert. In reality, it might be a signed contract in the middle of a live deal, or a routine attachment no one will look at for a week. Same alert, same severity, completely different outcome.

A human analyst can tell the difference. They understand the business context: what’s in motion, who’s involved, and what it means for the business if this gets blocked or delayed.

AI agents don’t have that layer. They never come to the office, and it shows. They don’t have hallway conversations, they don’t carry institutional memory. They process what’s in front of them (a ticket, a severity score, a set of fields) and operate within that boundary.

So they do what they’re built to do: sort, rank, and recommend. But in doing so, they treat fundamentally different situations as if they were the same.

You see it across everyday workflows. An access request from a new hire looks identical to one from an experienced employee. A policy exception requested by a contractor carries a very different risk profile than one from an executive, yet without business context, both are handled as interchangeable cases.

Once that context is missing, the system doesn’t really prioritize or decide. It processes, it may even recommend, but it doesn’t actually make the call. And that’s why humans are still in the loop.

It starts with prioritization

Security tools have been prioritizing by risk score and severity for years. That was always an incomplete picture, but humans could compensate, filling in the gaps with context and judgment.

If we want AI agents to make decisions at scale, those gaps can’t exist. Real prioritization isn’t risk alone. It’s risk in the context of the business. What’s the security risk, and what’s at stake if this isn’t handled in time?

Business context is what makes AI operational

If we want AI to actually work in security operations, we need to solve the business context challenge. That means connecting every security event to the business: who this person is, what they’re working on, what the policy allows in this situation, what happened last time, and what’s at stake if we get this wrong.

When that context is available, everything changes. AI agents can still do what they’re good at (process at scale, stay consistent, never lose vigilance) but now with the understanding to match. Decisions become clearer, and aligned with what the business actually needs.

This is what makes AI operational at scale.

Without context, agents assist, sometimes recommend.
With context, they can actually handle the work.