What is artificial intelligence (AI) for networking?
What is artificial intelligence (AI) for networking?
Artificial intelligence (AI) for networking is a subset of AIOps specific to applying AI techniques to optimize network performance and operations.
Networking systems are become increasingly complex due to digital transformation initiatives, multi-cloud, the proliferation of devices and data, hybrid work, and more sophisticated cyberattacks. As network complexity grows and evolves, organizations need the skills and capabilities of network operates to evolve as well. Talent shortages and budget constraints only add to this challenge. To overcome these challenges, organizations are adopting AI for networking to help.
Key AI for networking technologies
or AI to be successful, it requires machine learning (ML), which is the use of algorithms to parse data, learn from it, and make a determination or prediction without requiring explicit instructions. Thanks to advances in computation and storage capabilities, ML has recently evolved into more complex structured models, like deep learning (DL), which uses neural networks for even greater insight and automation. Natural language processing and understanding (NLP/ NLU), large language models (LLM), and generative AI (GenAI) are other trending AI tools that have driven recent AI advancement, particularly in the area of virtual assistants.
The role of AI in network environments.
Capabilities of AI for networking
AI in networking provides substantive value to companies in almost every industry. Here are a few ways AI networking solutions are delivering real results for customers:
- Detecting time series anomalies: AI can detect time series anomalies with a correlation that allows network engineers to quickly find relationships between events that would not be obvious to even a seasoned network specialist
- Event correlation and root cause analysis: AI can use various data mining techniques to explore terabytes of data in a matter of minutes. This ability lets IT departments quickly identify what network feature (e.g., OS, device type, access point, switch, or router) is most related to a network problem, accelerating problem resolution
- Predicting user experiences: Based on network conditions, AI can predict a user’s internet performance, allowing a system to dynamically adjust bandwidth capacity based on which applications are in use at specific times
- Recommended and self-driving actions: Advanced AI can not only identify the root cause of an issue, but also suggest actions the IT operator can take to remediate it or automatically fix the issue without human intervention. This enables maximum uptime and the best possible end user experiences
- Virtual network assistants: Virtual network assistants, powered by AI, work as a member of the IT team to quickly identify network issues, recommend actions for improved network performance, and speed documentation search
Benefits of AI for networking
AI for networking enhances both end user and IT operator experiences by simplifying operations, boosting productivity and efficiency and reducing costs. It streamlines and automates workflows, minimizing configuration errors, and expediting resolution times. By providing proactive and actionable insights, AI for networking enables operators to address network issues before they lead to costly downtime or poor user experiences. Instead of chasing down “needle-in-a-haystack problems”, IT operators get more time back to focus on more strategic initiatives.
What to look for in an AI for networking solution
Without the right AI strategy, IT simply can’t keep up with today’s stringent network requirements. Here are several technology elements that an AI solution should include:
- The right data: Any meaningful AI networking solution begins with massive amounts of quality data. AI continually builds its intelligence over time through data collection and analyses. The more diverse the data collected, the smarter the AI solution becomes. Furthermore, labeling data with domain-specific knowledge helps train AI models. For example, design intent metrics, which are structured data categories, can be used for classifying and monitoring network user experience
- The right response: Good AI for networking solutions should provide accurate insights in real time, reducing alarm fatigue by prioritizing issues and providing recommended actions for remediation. To provide the right response, an AI engine uses various AI techniques, collectively referred to as a data science toolbox, to process that data. Supervised or unsupervised ML and DL such as neural networks should be employed to analyze network data and provide actionable insights.
- The right infrastructure: A robust and scalable infrastructure is required to collect and process data and provide an insightful response. Cloud-hosted processing provides a reliable and agile infrastructure for data collection and processing that can scale to meet AI workload demands now and in the future.
Juniper’s AI Native Networking Platform
Juniper delivers on the promise of AI for networking with the industry’s first AI-Native
Juniper’s AI-Native Networking Platform provides the agility, automation, and assurance networking teams need for simplified operations, increased productivity, and reliable performance at scale.
AI for networking FAQs
What are examples of AI for networking in use?
AI for networking 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, 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?
The Marvis Virtual Network Assistant is a prime example of AI being used in networking. Marvis provides a conversational interface, prescriptive actions, and Self-Driving Network™ operations to streamline operations and optimize user experiences from client to cloud. Juniper Mist AI and cloud services bring automated operations and service levels to enterprise environments. Machine learning (ML) algorithms enable a streamlined AIOps experience by simplifying onboarding; network health insights and metrics; service-level expectations (SLEs); and AI-driven 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.
