SOC Prime has introduced DetectFlow Enterprise, a new solution designed to bring real-time threat detection directly into the data ingestion layer. By transforming traditional data pipelines into detection pipelines, the platform allows security teams to identify and respond to threats much earlier in the data processing workflow. As organizations continue to generate massive volumes of security telemetry, this new approach helps teams analyze threats in real time before the data even reaches downstream systems such as SIEM, EDR, or enterprise data lakes.
Traditionally, many security tools rely on post-ingestion analysis, which means threat detection often occurs only after data has been stored and processed by security platforms. However, SOC Prime’s DetectFlow Enterprise shifts this process upstream. By running thousands of Sigma-based detections directly on live streaming data, the platform enables organizations to identify suspicious activities instantly while data is still in motion. As a result, security teams gain earlier visibility into potential attacks, which significantly improves response times and reduces the likelihood of successful breaches.
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The solution leverages Apache Flink to analyze real-time data streams, including Kafka pipelines, allowing organizations to run tens of thousands of detection rules simultaneously with millisecond-level mean time to detect (MTTD). Consequently, security teams can detect, tag, enrich, and correlate threat data while it is still flowing through the pipeline. This early-stage analysis also allows organizations to enrich security telemetry before sending it to downstream platforms for further investigation and storage.
In addition, DetectFlow Enterprise enables teams to execute large-scale detection capabilities directly within streaming environments. Security analysts can run thousands of rules on live data streams while maintaining real-time visibility into events across multiple log sources. Because the platform correlates these events during the ingestion phase, it can uncover complex attack chains much earlier than traditional tools. Therefore, organizations can identify meaningful threats faster while significantly reducing the noise and false positives that often overwhelm security teams.
Another key advantage of DetectFlow Enterprise is its ability to perform correlation before the data reaches SIEM platforms. Normally, running large-scale detection rules within SIEM systems can create performance and cost challenges due to heavy data ingestion requirements. However, by analyzing full telemetry streams earlier in the pipeline, organizations can evaluate security events against thousands of detection rules without overloading downstream infrastructure. This approach not only improves detection efficiency but also helps control operational costs associated with data processing and storage.
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Furthermore, the platform is powered by SOC Prime’s Detection Intelligence dataset, which has been developed through more than a decade of continuous threat research and detection engineering. Using the Flink Agent component, DetectFlow automatically assembles detection results, event data, and active threat intelligence context. This data is then processed using AI-powered analysis to provide security teams with clearer insights into ongoing threats and potential attack patterns.
As a result, organizations can identify high-confidence attack chains more effectively, improve investigative workflows, and accelerate incident response. The platform essentially transforms raw telemetry streams into actionable security intelligence before the information reaches traditional security monitoring tools.
“Attack Chains take events your pipelines already tagged with Sigma rules and correlate them around active threats, grouping related matches into a single incident narrative. On top of solving the detection volume, we’ve added the comprehensive threat report that stitches up correlated logs into a detected threat intel.
– Andrii Bezverkhyi, Founder & CEO of SOC Prime”
Overall, DetectFlow Enterprise represents a shift toward streaming-first cybersecurity detection, where threats are analyzed in real time at the earliest stages of data processing. As cyber threats continue to grow in scale and complexity, solutions that move detection closer to the data source will play an increasingly critical role in helping organizations respond faster and defend their infrastructure more effectively.
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