Investigate every security event with an AI agent, without the frontier bill
AI agents can investigate suspicious security activity but applying them to every event is cost‑prohibitive. A two‑stage detection pipeline uses a lightweight model called Mambark to score all events and forwards only the most anomalous ones to a larger AI agent for deeper analysis. The approach aims to provide fleet‑scale security monitoring that is both affordable and precise.
- ▪Scoring every security event with a frontier‑level large language model could cost tens of millions of dollars per day, making it impractical for large enterprises.
- ▪Mambark is a 96.9 million‑parameter state‑space model pretrained on hundreds of billions of security events to predict the next event and generate anomaly scores.
- ▪The model’s linear‑time architecture allows it to consider contexts spanning tens of thousands of events on a single GPU, unlike quadratic‑cost transformers.
- ▪Mambark’s high‑recall scoring filters events so that only the most suspicious are examined by a more expensive AI agent, enabling cost‑effective fleet‑scale detection.
- ▪The pipeline is currently being tested with a small group of Datadog Cloud SIEM design partners.
- ▪],
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| Original publisher | Datadog |
| Canonical URL | https://www.datadoghq.com/blog/ai/ai-security-detection-pipeline/ |
| Publication time | Tue, 28 Jul 2026 15:34:17 +0000 |
| Retrieval time | 2026-07-28T15:50:26.160Z |
| Last seen | 2026-07-28T15:50:26.160Z |
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| Cluster | tS_8lGsZD2Cm · 1 stories |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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Opening excerpt (first ~120 words) tap to expand
AIInvestigate every security event with an AI agent, without the frontier billaisecuritybits aiai securityai researchPublished Jul 28, 2026 Read time8m window.matchMedia("(prefers-reduced-motion: reduce)").matches&&document.querySelectorAll("video").forEach(e=>{e.autoplay=!1,e.controls=!0}); Nicolas GrislainStaff Applied Scientist AI agents can investigate suspicious security activity by pulling in context from across an environment and explaining whether an event represents a genuine threat. But applying that kind of reasoning to every security event is prohibitively expensive.
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