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Juniper Mist Premium Analytics is a comprehensive network analytics solution that provides organizations with valuable insights into their network performance and user experience. The primary use case of this product is to enable IT teams to proactively monitor, troubleshoot, and optimize their network infrastructure.
The most valuable functionality of Juniper Mist Premium Analytics is its AI-driven insights and recommendations. Leveraging machine learning algorithms, it analyzes vast amounts of network data to identify anomalies, predict potential issues, and suggest remediation actions. This proactive approach helps organizations minimize network downtime, improve user experience, and optimize network performance.
By providing real-time visibility into network performance, Juniper Mist Premium Analytics helps organizations quickly identify and resolve network issues. It offers detailed analytics on network traffic, application performance, and user behavior, allowing IT teams to pinpoint the root cause of problems and take immediate action. This not only reduces troubleshooting time but also enhances overall network reliability.
Furthermore, Juniper Mist Premium Analytics enables organizations to optimize their network infrastructure. It provides insights into network capacity utilization, application usage patterns, and user behavior, allowing IT teams to make informed decisions about network design, resource allocation, and capacity planning. This helps organizations to ensure that their network is efficiently utilized and can support the growing demands of their users.
Flowmon is a professional tool for effective network troubleshooting, performance monitoring, capacity planning, encrypted traffic analysis and cloud monitoring. Instead of just the red/green infrastructure status, it helps NetOps teams to understand user experience while keeping the amount of data noise and analytical work to a minimum. Flowmon is a part of the Kemp product portfolio.
Splunk User Behavior Analytics is a behavior-based threat detection is based on machine learning methodologies that require no signatures or human analysis, enabling multi-entity behavior profiling and peer group analytics for users, devices, service accounts and applications. It detects insider threats and external attacks using out-of-the-box purpose-built that helps organizations find known, unknown and hidden threats, but extensible unsupervised machine learning (ML) algorithms, provides context around the threat via ML driven anomaly correlation and visual mapping of stitched anomalies over various phases of the attack lifecycle (Kill-Chain View). It uses a data science driven approach that produces actionable results with risk ratings and supporting evidence that increases SOC efficiency and supports bi-directional integration with Splunk Enterprise for data ingestion and correlation and with Splunk Enterprise Security for incident scoping, workflow management and automated response. The result is automated, accurate threat and anomaly detection.
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