AI for networking FAQs
What are examples of AI for networking in use?
Among the uses in networking, AI can reduce trouble tickets and resolve problems before customers or even IT recognize the problem exists. Event correlation and root cause analysis can use various data-mining techniques to quickly identify the network entity related to a problem or remove the network itself from risk. AI is also used in networking to onboard, deploy, and troubleshoot campus fabrics in greenfield scenarios, making Day 0 to 2+ operations easier and less time consuming.
How does AI transform networking?
AI plays an increasingly critical role in taming the complexity of growing IT networks. AI enables the ability to discover and isolate problems quickly by correlating anomalies with historical and real-time data. In doing so, IT teams can scale further and shift their focus toward more strategic and high-value tasks and away from the resource-intensive data mining required to identify and resolve needle-in-the-haystack problems that plague networks.
What AI for networking solutions does Juniper offer?
Marvis Virtual Network Assistant is a prime example of AI being used in networking. Marvis provides natural language processing (NLP), a conversational interface, prescriptive actions, and Self-Driving Network™ operations to streamline operations and optimize user experiences from client to cloud. Juniper Mist wired, wireless, and WAN assurance cloud services bring automated operations and service levels to enterprise campus environments. Machine-learning (ML) algorithms enable a streamlined AIOps experience by simplifying onboarding; network health insights and metrics; wired, wireless, and WAN service-level expectations (SLEs); and AI-driven campus fabric management.
What is AI for networking and security?
With so many work-from-home and pop-up network sites in use today, a threat-aware network is more essential than ever. The ability to quickly identify and react to compromised devices, physically locate compromised devices, and ultimately optimize the user experience are a few benefits of using AI in cybersecurity. IT teams need to protect their networks, including devices they don’t directly control but must allow to connect. Risk profiling empowers IT teams to defend their infrastructure by providing deep network visibility and enabling policy enforcement at every point of connection throughout the network. Security technologies are constantly monitoring not only the applications and user connections in an environment, but also the context of that behavior and whether it is acceptable use or potentially anomalous and rapidly identifying malicious activity.