Unlocking the Ultimate Source of Truth in Cloud Security—Network Data

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Presented by

Dan Frey, Marketing Manager // Guy Raz, Sales Engineer, ExtraHop // Peggy Bresnick-Kendler, Contributing Editor, Dark Reading

About this talk

Learn how virtual taps from AWS, Azure, and Google Cloud enable network detection and response (NDR) solutions to help secure cloud environments with agentless and immutable monitoring and threat detection capabilities. Invisible to attackers, NDR solutions sit out of band and analyze network traffic streams to provide crucial information about devices, users, and potential attacks that other security tools simply can’t. Best-of-breed NDR also augments security personnel visibility into SSL/TLS 1.3 encrypted traffic and ML-driven threat detection that alerts only on items that matter while providing intelligence to remediation and forensic workflows. Attend this event and you'll learn how: - Cloud provider packet mirroring features enable better cloud security - Network detection and response puts information into proper context for accurate alerts - NDR, EDR and SIEM form the SoC visibility triad in the cloud
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The prevention and protection model of cybersecurity isn’t working: between the cloud, IoT, and the sheer pace of change, the enterprise is no longer built to be walled in. This channel provides educational webinars to help SecOps (SOC) and NetOps (NOC) teams, from CIOs and CISOs to analysts and practitioners, change their perspective in order to identify, investigate, and respond to threats across the modern attack surface. We explore how cloud-native network detection and response (NDR) provides the complete visibility, real-time threat detection, and intelligent response you need to secure your hybrid environment. You’ll also find product information about ExtraHop Reveal(x) which enables you to: Eliminate blind spots: Cover 100% of your hybrid environment, Detect what matters: Find threats 95% faster, and Act quickly: Respond to breaches 70% faster. Learn more at www.extrahop.com