Ask cybersecurity experts about Network Detection and Response (NDR) and you may still hear that it’s too noisy and uses too much data. But if you ask teams running NDR that include agent AI capabilities, you’ll hear that they are actually using it to detect threats earlier, triage faster, and reduce false positives. The persistence of old grievances is partly due to the persistence of reputations and partly because NDRs evolved faster than stories.
origin of noise
Deploying NDR provides analysts with constant visibility into network traffic, encrypted session behavior, and protocol anomalies. But visibility often comes as raw material rather than finished intelligence.
Some systems required extensive manual tuning during deployment to prevent SIEM overload. Organizations that couldn’t invest at the time (or didn’t know its importance) helped solidify NDR’s reputation as a “warning firehose” or “noisy.”
NDR with agent AI turns noise into story
Agentic AI autonomously ingests data, prioritizes alerts, performs correlation and initial analysis, and handles time-consuming and repetitive tasks that otherwise would otherwise be buried by analysts. There is an unexpected development here. The amount of data that once could overwhelm your team if NDR wasn’t properly coordinated is now a strategic asset. Because AI can ingest and analyze thousands of data points simultaneously, “noise” can provide a rich basis for finding actionable signals, such as connections between low-severity, informational, or other unobtrusive activities that most SOC teams don’t have the ability to combine. The system can surface detections that might otherwise have been missed.
AI handles large amounts of data and tedious tasks, freeing up analysts to focus on key threats. Powered by agent AI, NDR combines fully correlated stories from network data to uncover a prioritized set of detections, such as anomalous connections related to failed logins, suspicious DNS queries, and anomalous file access. Each detection is delivered with network evidence that analysts need for immediate context.
While NDRs must be tuned to ignore true “meaningless” noise, agent AI’s correlation capabilities also reduce the need for manual tuning, which some NDR deployments have struggled with in the past, by identifying and automating detection improvements.
NDR comparison without and with agent AI
Start without agent AI. Imagine that in a typical 24-hour window, your NDR system detects 847 network anomalies and your ML model flags 312 as potentially malicious. Currently, analysts step in and manually triage and investigate these, possibly ignoring many as false positives. Ultimately, there are four detections that need to be addressed.
Now imagine the same window and the same number of anomalies for which the agent AI handles triage. Connect alerts and reasons based on evidence and draw conclusions. Next, present the analyst with four prioritized detections to review. Each is accompanied by relevant evidence and recommended response actions. For example, we determine that a DNS anomaly correlates with a new process on the endpoint, flag compromised identities, and match TTP patterns to Cobalt Strike beacons. Advanced NDR also allows analysts to see under the hood how the AI reached its conclusions for complete transparency. Analysts can simply select a high-priority detection to begin their review.
Operational development
Agentic AI still does not completely eliminate the need for proper implementation. Three key areas contribute to NDR becoming a trusted partner rather than a noisy neighbor: baseline setting, staying informed, and SOC integration.
base lining
NDR has a detection engine that can generate alerts immediately, but some methods, such as anomaly detection, require the platform to run for a period of time to establish a baseline of normal network operation. During this period, observe typical traffic flows, known server and endpoint activity, and expected devices. Most NDR platforms already automate this process, helping the system distinguish between routine operations and true threats and identify malicious traffic. Tuning is done based on that baseline. When false positives occur, analysts can classify them and remove them from the alert queue, helping to retrain detections and further reducing noise.
Please stay tuned
Networks change. New applications, cloud workloads, unknown devices, and AI-driven data flows can change the baseline, and as the baseline ages, false positives can increase. Regular tuning keeps the NDR calibrated and allows the AI to identify emerging patterns before they turn into noise.
SOC integration
NDR data can fuel other systems within the AI-powered SOC, and better fuel means cleaner results. This is important for noise issues. When AI has high-fidelity data to work with, it can better differentiate between true threats and false positives.
As an example, a recent report demonstrated how important data quality is in that one type of data improved CTF test scores by more than 350%. In this report, the same data resulted in improved accuracy (95% vs. 26%) and nearly 300% more IR results compared to common log formats. Across test runs conducted during the study, Frontier AI models performed at comparable levels. This means that data quality, rather than model selection, had a significant impact on security results.
This same data can power connections to other AI SOC tools, AI-powered SIEMs (like CrowdStrike’s Charlotte), and local models through MCPs. Organizations that get the most out of their systems use APIs and discovery feeds strategically to let NDR AI perform correlations on alerts before they reach other platforms, further reducing noise before they get into the analyst queue.
conclusion
Myths often persist because they are easy to repeat. The “NDR is noisy” narrative is rapidly being replaced by AI designed to correlate at scale:
Create context to process volume Find signals lost in noise Reduce reliance on manual tuning Shift analyst focus to higher severity threats
Proper deployment takes care of the rest. What emerges is NDR, which provides greater visibility, faster response, and ultimately empowers the SOC to keep pace with the network.
Corelight network discovery and response
Trusted to defend the world’s most sensitive networks, Corelight’s Network Detection & Response (NDR) platform combines deep visibility, agent AI, and advanced behavioral and anomaly detection to help SOCs discover new and rapidly changing threats. Learn more about Corelight here.
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