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Cloudflare is deploying a multi-agent system that separates deterministic evidence gathering from model inference to reduce alert fatigue in security operations. By integrating with OpenAI's Daybreak Defense Network and Anthropic models, the harness consolidates scattered detections into actionable insights for human analysts. This approach directly addresses the "alert paradox" by automating data aggregation and prioritization at scale.
Building an evidence-grounded agentic security operations harness on Cloudflare

Cloudflare Managed Defense uses a team of specialized AI agents built on Workers and global network telemetry to analyze security alerts. By separating deterministic evidence collection from model inference, the system delivers grounded recommendations to Managed Defense Analysts.
Security alerts rarely arrive one at a time. A single alert can cause a spike across the environment, requiring a human analyst to decide which alerts are related and what they mean. When multiple arrive at the same time, it can quickly overwhelm even a seasoned security analyst. Enter the alert paradox. Now, our built-in, multi-AI-agent security operations harness can handle more of this work at Cloudflare scale. Our Cloudflare Managed Defense AI agent harness speeds up the process of gathering data, connecting and aggregating detections, and accounting for missing sources while new alerts continue to arrive. To further analyze context, we make use of the OpenAI Daybreak Defense Network and our partnership with Anthropic. Cloudflare uses approved OpenAI Daybreak and Anthropic models, including GPT-5.6 Cyber and Mythos, for deeper model-backed analysis. Initial analysis and scoring is done with Clef, Cloudflare’s open-source decision model. Collecting evidence to understand what each alert means requires a lot of time. Even in a highly sophisticated Security Information and Event Management (SIEM), too much is still…
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