Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Most “AI SOC” demos out there can look great. The polished dashboard, the confident verdict, the slide that says “autonomous.” A demo is built to show the platform at its best, on clean data, in a controlled environment, answering a question the vendor already knew was coming. The differences only show up after you’ve signed, when the platform meets your real stack, alert volume, and compliance requirements.

A year ago, a handful of vendors called themselves an “AI SOC.” Today, more than 100 do. The label now means whatever the person selling it needs it to mean, leaving security teams to buy very different products under the same two words. So let’s sort the market. Beneath the “agentic” branding, most AI SOC vendors fall into one of four categories, and none of them clears the bar. Each can look capable in a demo.

The rules of offensive security just changed. Here's what every SecOps leader needs to do about it.

In the current threat landscape, the pressure on security operations center (SOC) teams has never been higher. Yet for many organizations, the reality of daily security operations is less high-tech threat hunting and more of an uphill battle against manual processes and fragmented data. To understand why SOC teams are burning out, let's walk through a typical morning of an SOC analyst.

AI SOC memory is the difference between an investigation that starts from your team’s history and one that starts from zero. In most security operations centers, there is none: context disappears the moment a case closes. When a new alert fires, the investigation effectively starts from zero.