Here’s How AI Finally Got Alerts Right

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Security teams have been drowning in alerts for years. Ask any SOC analyst what their inbox looks like after a weekend, and you’ll likely hear something close to panic. The sheer volume of false positives has become a full-time problem—one that traditional tools, frankly, haven’t fixed. But something has shifted.

Source: Rapid7

Rapid7’s new AI-powered alert triage system, built into InsightIDR, might just be that shift. It classifies alerts with an astonishing 99.93% accuracy, thanks to machine learning models trained on a massive dataset sourced from their global MDR operations [1]. This isn’t just another automation tool promising to save time; it’s actually doing it.

What sets this apart is the combination of accuracy and transparency. The system doesn’t just toss alerts into a “good” or “bad” pile—it shows its work. Analysts can review the AI’s decision process, which means they’re not being asked to blindly trust a black box. This kind of traceability is exactly what has been missing from most “AI in cybersecurity” pitches up to now.

Source: Rapid7

This isn’t just a performance upgrade—it’s a workflow transformation. Triage, once a repetitive and mentally draining task, is now largely offloaded to a machine that rarely gets it wrong. That means SOC analysts can focus on actual investigations instead of sifting through noise.

As the industry leans harder into AI, this development is a case study in getting it right: not replacing humans, but giving them breathing room. For once, the promise of AI in security doesn’t feel inflated. It feels overdue.

References

[1] Rapid7, “InsightIDR AI Alert Triage Automatically Classifies Alerts with 99.93% Accuracy,” Apr. 29, 2025. [Online]. Available: https://www.rapid7.com/blog/post/2025/04/29/insightidr-ai-alert-triage-automatically-classifies-alerts-with-99-93-accuracy/

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