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1、The cybersecurity landscape is constantly evolving,with new threat continuously emerging.While effective in detecting some attacks through predefined patterns,signatures,or indicators associated with malicious activities,traditional Network Detection and Response(NDR)solutions are struggling to keep

2、 pace with the sophistication of modern attacks.To address these challenges,NDR solutions must evolve to become more intelligent,automated,and proactive.This whitepaper explores the limitations of traditional NDR and the transformative potential of AI-driven threat detection and response.Moving Beyo

3、nd Traditional Network Detection and Response with MixModewww.mixmode.aiW H I T E PA P ERAdvancing Network Detection and ResponseThe Limitations of Traditional NDRWhile effective in some cases,traditional Network Detection and Response(NDR)solutions struggle to keep pace with the sophistication of m

4、odern cyberattacks.These limitations stem from their reliance on outdated technologies and approaches.False PositivesTraditional NDR solutions often generate a high volume of false positives,which can overwhelm security teams and divert their attention from genuine threats.These false positives can

5、be caused by legitimate network traffic that matches known patterns of malicious activity or by errors in the detection algorithms.Manual InvestigationManually investigating alerts is a time-consuming and error-prone process.Security teams must manually analyze each alert,determine whether it repres

6、ents a genuine threat,and take appropriate action.This process can be slow and inefficient,especially in the face of large-scale attacks.Lack of ContextTraditional NDR solutions often struggle to provide the necessary context to understand a cyberattacks full scope and impact.This lack of context ca