被害状況

攻撃者

被害状況

攻撃者

脆弱性

脆弱性

脆弱性

被害状況

攻撃者

脆弱性

1. 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.
2. 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.
3. Agentic AI autonomously fetches data, triages alerts, and performs correlation and initial analysis, handling the time-consuming, repetitive work that used to bury analysts. The data volume that once could overwhelm teams if the NDR wasn't appropriately tuned, has become a strategic asset.
4. 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.
5. 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%. Data quality, not model choice, had the greater impact on security outcomes.

脆弱性

脆弱性