The Alert Firehose Finally Meets Its Match  The Hacker News  May 25, 2026 Agentic AI / Threat Detection Ask a cybersecurity pro about Network Detection and Response (NDR) and you might still hear "Noisy," "Too much data." But ask the teams running NDR that includes agentic AI capabilities and you'll hear they're actually using it to catch threats earlier, triage faster, and chase fewer false positives. The old complaint lingers in part because reputations are sticky, and because NDR has evolved faster than the narrative. The origins of noise NDR deployments have always given analysts deep visibility into network traffic, encrypted session behavior, and protocol anomalies. But visibility often came as raw material, not finished intelligence. Some systems required extensive manual tuning during deployment to prevent SIEM overload. Organizations that couldn't invest that time (or didn't know how important it was) helped cement NDR's "alert firehose" or "noisy" reputation. NDR with agentic AI turns noise into narrative Agentic AI autonomously fetches data, triages alerts, and performs correlation and initial analysis, handling the time-consuming, repetitive work that used to bury analysts. Here's the unexpected twist: the data volume that once could overwhelm teams if the NDR wasn't appropriately tuned, has become a strategic asset. Because AI can ingest and simultaneously analyze thousands of data points, "noise" can become rich ground for finding actionable signals such as connections between low-severity, informational, or otherwise low profile activity most SOC teams would never have the capacity to piece together. The system can surface detections that might otherwise have been missed. With AI processing data volume and tedious tasks, analysts are freed up to focus on the top threats. NDR with agentic AI pieces together a complete, correlated story from network data and surfaces a prioritized set of detections such as an anomalous connection tied to a failed login, a suspicious DNS query, or unusual file access. Each detection is delivered with the network evidence analysts need for immediate context. NDR should still be tuned to ignore true "meaningless" noise, but agentic AI's correlation capabilities also reduce the need for the manual tuning that some NDR deployments sometimes struggled with in the past by identifying and automating detection improvements. Comparing NDR without and with agentic AI Let's start without agentic AI. In a typical 24-hour window, imagine your NDR system detects 847 network anomalies, and ML models flag 312 as potentially malicious. Now the analysts step in to manually triage and investigate these, likely dismissing a large number as false positives. Four detections eventually emerge that require action. Now picture the same window and the same number of anomalies, but with agentic AI handling triage. It correlates alerts, reasons through the evidence, and draws conclusions. It then presents the analysts with four prioritized detections to review, each with relevant evidence and suggested response actions attached. For example, it might determine that a DNS anomaly correlates with a new process on an endpoint, flag a compromised identity, and match TTP patterns to Cobalt Strike beacons. Advanced NDR even lets analysts look under the hood to see how the AI reached its conclusions, for full transparency. The analysts simply pick up the prioritized detections and begin their review. Operational deployment Agentic AI still doesn't fully eliminate the need for proper deployment. Three key areas contribute to NDR becoming a trusted partner instead of a noisy neighbor: baselining, staying tuned, and SOC integration. Baselining NDR has detection engines that can generate alerts immediately out of the box, but some methods such as anomaly detection require the platform to run for a period of time to baseline the network's normal behavior. During this period it observes typical traffic flows, known server and endpoint activities, and expected devices. Most NDR platforms already automate this process, which helps the system distinguish routine operations from true threats and identify malicious traffic. Tuning builds on that baseline. When false positives fire, analysts can classify and eliminate them from the alert queue, helping retrain the detections and further reducing noise. Staying tuned Networks change. New applications, cloud workloads, unknown devices, and AI-driven data flows can shift the baseline, and an outdated baseline can lead to more false positives. Regular tuning keeps NDR calibrated while AI can help spot emerging patterns before they turn into noise. SOC integration NDR data can fuel other systems in an AI-powered SOC, and better fuel can deliver cleaner results. This matters for the noise problem: when AI has high-fidelity data to work with, it can more accurately distinguish true threats from false positives. In one example, a recent report demonstrated just how much data quality matters, with one type of data improving CTF test scores by over 350%. In this report, the same data increased accuracy (95% vs. 26%) and delivered nearly 300% more IR findings compared to common log formats. Across test runs conducted during the study, frontier AI models performed at comparable levels, meaning data quality, not model choice, had the greater impact on security outcomes. This same data can enrich other AI SOC tools, SIEMs powered with AI (e.g., CrowdStrike's Charlotte), and connections to local models via MCP. Organizations getting the most from their systems use APIs and detection feeds strategically, letting the NDR AI handle correlation before alerts reach other platforms, further reducing noise before it ever hits the analyst queue. The bottom line Myths often persist because they're easy to repeat. The "NDR is noisy" story is quickly being replaced by AI designed to correlate at scale that: Handles the volume Creates context Finds signals otherwise lost in the noise Reduces manual tuning dependency Shifts analyst focus to high-severity threats Proper deployment handles the rest. What emerges is NDR that delivers better visibility and faster response, and fuels the SOC to finally keep pace with the network. Corelight Network Detection & Response Trusted to defend the world's most sensitive networks, Corelight's Network Detection & Response (NDR) platform combines deep visibility with agentic AI, and advanced behavioral and anomaly detections to help your SOC uncover new, fast-moving threats. Learn more about Corelight. Found this article interesting? This article is a contributed piece from one of our valued partners. Follow us on Google News , Twitter and LinkedIn to read more exclusive content we post. 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