Building an Agentic SOC on a Stream

Security operations centers increasingly face an investigation-capacity problem rather than a detection problem, as high volumes of low-priority alerts, particularly DLP and cloud signals, often go unreviewed despite offering opportunities for persistent attackers. The described Agentic SOC addresses this challenge with a streaming, AI-driven investigation pipeline in which a central triage coordinator delegates evidence gathering to specialized agents, an adversarial evaluator tests conclusions for gaps, and a curated knowledge base preserves relevant lessons from prior cases. Rather than building agents for each detection source, the system organizes them by evidence domain and uses configurable source data and self-updating context to reduce maintenance. Duplicate alerts are filtered before investigation, critical alerts receive priority routing through Confluent Cloud, Apache Flink, and Kafka-based streaming infrastructure, and telemetry on latency, cost, quality, and agent behavior feeds back into the system for real-time monitoring. Automation may close only low- and medium-severity alerts judged benign, while high-severity cases always require human approval. Over a reported 30-day period involving roughly 4,700 alerts, the pipeline investigated all alerts, escalated about 5%, and surfaced more than 250 true positives, allowing analysts to focus on cases requiring human judgment.

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