Network Detection and Response (NDR) has long carried a reputation for generating excessive alerts, often overwhelming security teams with raw data. This perception stems from early deployments that required extensive manual tuning to prevent flooding SIEM systems with noise. Organizations that lacked the time or expertise for proper configuration reinforced NDR’s image as a source of alert overload.
How Agentic AI Changes NDR Operations
The introduction of agentic AI in NDR platforms marks a shift in how security teams handle network data. Instead of merely presenting raw traffic logs, these AI systems autonomously fetch data, triage alerts, and perform correlation and initial analysis. This automation handles the repetitive work that previously consumed analyst hours. The key insight is that the high data volume once seen as a liability now becomes an asset. AI can ingest and analyze thousands of data points simultaneously, turning what was considered noise into fertile ground for actionable signals.
Impact on Threat Detection and Analyst Workflow
With AI processing large data volumes, analysts can focus on the most critical threats. The system pieces together complete, correlated narratives from network data, surfacing prioritized detections that might otherwise go unnoticed. These include anomalous connections tied to failed logins, suspicious DNS queries, or unusual file access patterns. Each detection comes with the necessary network evidence for immediate context. While NDR should still be tuned to ignore truly meaningless noise, agentic AI’s correlation capabilities reduce the need for the manual tuning that once defined NDR deployments. This evolution allows teams to catch threats earlier, triage faster, and pursue fewer false positives.
Source: The Hacker News
