Netskope Announces NewEdge AI Fast Path: Industry-Leading Performance for Secure AI Adoption

Core Insights - Netskope has launched NewEdge AI Fast Path, which optimizes network paths for AI applications, reducing latency and costs while enhancing performance and security for enterprises adopting AI technologies [1][3]. Group 1: NewEdge AI Fast Path Capabilities - NewEdge AI Fast Path provides efficient optimization for network paths to critical AI destinations, supporting applications hosted in various cloud environments [1]. - The capabilities help minimize latency and costs, improve performance, and ensure a secure experience for teams using AI applications [1][3]. - The solution addresses the growing gap between AI expectations and reality, as only 18% of infrastructure leaders feel confident in meeting AI demands [2]. Group 2: Performance and Security Integration - The integration of security and performance is crucial, as enterprises often face trade-offs between the two due to outdated tools and inadequate infrastructure [2][3]. - Netskope's NewEdge platform allows customers to avoid these trade-offs, providing enhanced performance for demanding AI applications without compromising security [3]. Group 3: Infrastructure and Expansion - Netskope maintains direct control over its data center infrastructure, ensuring high availability and performance with industry-leading service level agreements (SLA) [4]. - The NewEdge network features over 120 data centers across more than 75 regions, capable of running a complete SASE stack at the edge [5]. - The extensive peering with over 11,000 network adjacencies and 750 unique autonomous system numbers facilitates direct paths to major AI destinations, enhancing connectivity and performance [9]. Group 4: Specific Benefits for AI Applications - NewEdge AI Fast Path optimizes inference results for enterprise users, reducing "time-to-first-token" (TTFT) for conversational AI [6]. - It accelerates complex workflows for agentic AI and optimizes Large Language Model (LLM) performance when accessing distributed data [6]. - The solution supports Retrieval-Augmented Generation (RAG) by enhancing connectivity between LLMs and external data sources for improved real-time outputs [6].