Improve network security by leveraging unsampled flow data and machine learning for proactive threat detection and early intervention.
Network operations teams often find themselves in a reactive cycle, struggling to address security threats and performance issues after disrupting user experience. A lack of comprehensive visibility into network traffic—exacerbated by unsampled data limitations and blind spots, especially in cloud environments—can leave organizations vulnerable. However, combining high-fidelity NetFlow data with machine learning-powered analytics enables teams to identify and mitigate security risks before they escalate proactively.
This eBook explores the common challenges in detecting network issues early and the transformative role of unsampled data and machine learning. It outlines the critical benefits of solutions like ElastiFlow, which provide real-time analysis, comprehensive visibility across all network egress points, and advanced anomaly detection. From identifying unusual traffic patterns to mitigating insider threats, readers will gain actionable insights into improving network observability and security posture.
With best practices for using NetFlow data effectively, this guide empowers organizations to prevent disruptions, reduce troubleshooting times, and strengthen their network defenses. Learn how to proactively approach network security and stop problems before they impact users.