The Alert Firehose Finally Meets Its Match

In discussions with cybersecurity professionals regarding Network Detection and Response (NDR), common terms like "Noisy" or "Too much data" frequently arise. However, teams utilizing NDR systems enhanced with agentic AI capabilities report a different reality: they are leveraging these systems to identify threats earlier, expedite triage processes, and significantly reduce false positives. The prevailing negative perception finds its roots in historical issues, but it is essential to recognize that NDR technology has rapidly progressed, evolving far beyond its past challenges.

The origins of noise

NDR systems have historically provided analysts with extensive visibility into network traffic, encrypted session behaviors, and protocol anomalies. However, this visibility often presented itself as raw data rather than actionable intelligence.

Certain deployments demanded considerable manual adjustment during setup to avoid overwhelming Security Information and Event Management (SIEM) systems. Organizations that either lacked the resources or the understanding of this necessity contributed to NDR's reputation as a source of constant alerts.

NDR with agentic AI turns noise into narrative

Agentic AI offers the ability to autonomously gather data, triage alerts, and conduct correlation and preliminary analysis, thereby managing the repetitive and time-consuming tasks that had previously inundated analysts. Interestingly, the high volume of data that once posed challenges for unrefined NDR systems has transformed into a strategic advantage. The capacity of AI to process and analyze thousands of data points simultaneously allows analysts to uncover actionable insights, including connections among low-severity or less conspicuous activities that would typically go unnoticed by most Security Operations Center (SOC) teams. This capability enables the detection of significant anomalies that may have otherwise slipped through the cracks.

With AI managing data volume and routine tasks, analysts can concentrate on the most pressing threats. NDR integrated with agentic AI constructs a comprehensive, correlated narrative from network data, highlighting a prioritized selection of detections such as unusual connections linked to failed login attempts, suspicious DNS queries, or atypical file access. Each alert is accompanied by relevant network evidence, providing analysts with necessary context for immediate action.

While NDR systems still require calibration to filter out genuinely "meaningless" noise, the correlation abilities of agentic AI mitigate the necessity for extensive manual adjustments, as the AI can identify and automate the enhancement of detection processes.

Comparing NDR without and with agentic AI

Let's explore a scenario without the integration of agentic AI. Over a typical 24-hour period, suppose your NDR system identifies 847 network anomalies, with machine learning models categorizing 312 as potentially malicious. At this point, analysts must manually sift through these alerts, likely dismissing many as false positives and ultimately identifying only four detections requiring attention.

Now envision the same scenario, but with agentic AI performing the triage. The AI correlates alerts, evaluates the supporting evidence, and derives conclusions. It then provides analysts with four prioritized detections for further review, each furnished with pertinent evidence and recommended response actions. For instance, it might ascertain that a DNS anomaly is related to a new process on an endpoint, pinpoint a compromised identity, and correlate tactics, techniques, and procedures (TTPs) with Cobalt Strike beacons. Advanced NDR solutions even allow analysts to investigate the reasoning behind the AI's conclusions, ensuring full transparency. The analysts can swiftly engage with the prioritized detections as they proceed with their investigations.

Operational deployment

While agentic AI streamlines many aspects of NDR operation, it does not completely negate the need for correct deployment. There are three crucial areas that ensure NDR becomes a reliable asset rather than a disruptive force: establishing a baseline, maintaining ongoing tuning, and integrating SOC functions.

Baselining

NDR systems feature detection engines capable of generating alerts immediately upon deployment. However, certain methods, such as anomaly detection, necessitate the platform operating for a time to establish a baseline of normal network behavior. During this phase, the system observes typical traffic patterns, recognized server and endpoint activities, and expected devices. The automation of this process by most NDR platforms aids in distinguishing routine operations from genuine threats and identifying malicious traffic. Ongoing tuning refines this baseline; when false positives occur, analysts can classify and eliminate these from the alert queue, thereby retraining the detection algorithms and minimizing noise.

Staying tuned

Network environments are dynamic. The introduction of new applications, cloud workloads, unrecognized devices, and AI-driven data flows can alter the established baseline; failing to update this baseline can lead to an uptick in false positives. Regular tuning helps maintain NDR alignment while AI assists in detecting emerging patterns before they escalate into disruptive noise.

SOC integration

Data generated by NDR can enhance other systems within an AI-enhanced SOC, resulting in more refined outputs. This distinction is crucial in addressing the noise issue: when AI operates with high-fidelity data, it can more accurately differentiate between genuine threats and false positives.

A recent report underscored the significant impact of data quality, revealing that one type of data improved Capture The Flag (CTF) test scores by over 350%. In this analysis, the same high-quality data vastly increased accuracy (95% compared to 26%) and yielded nearly 300% more incident response findings compared to standard log formats. Across various test runs, leading-edge AI models performed comparably, emphasizing that data quality had a more substantial effect on security results than the choice of models.

This high-quality data can also enrich other AI-driven SOC tools and AI-integrated SIEM systems. Organizations aiming to optimize their systems often strategically leverage APIs and detection feeds, allowing the NDR AI to handle the correlation of alerts before they reach other platforms, further diminishing noise prior to analyst review.

The bottom line

Myths tend to persist because they are easily reiterated. The narrative surrounding "noisy NDR” is being rapidly replaced by advanced AI capabilities that correlate data at scale, which effectively:

• Manages extensive data volumes
• Establishes contextual understanding
• Identifies critical signals lost amid the noise
• Diminishes reliance on manual tuning
• Realigns analyst priorities toward addressing high-severity threats

Proper deployment addresses the remaining considerations. The result is a refined NDR system that enhances visibility, accelerates response times, and empowers the SOC to keep pace with network demands.