AI in Networking

A variety of AI tools and technologies are employed when optimizing network performance, automating operations, and strengthening security with AI. These tools and technologies include the following.

• Machine learning (ML) analyzes vast amounts of data to recognize patterns. Networking use cases that employ ML technologies include identifying anomalies in network traffic and learning from historical data to predict congestion or potential failures and avoid performance bottlenecks.
• Large language models (LLMs) are a subset of generative AI that use vast datasets and deep learning techniques to generate, synthesize, and manipulate text. AI networking use cases that leverage LLMs are expanding rapidly, especially in areas where human language and technical configuration intersect. These use cases include simplifying and automating network configurations, where LLMs can translate natural language input into structured configuration commands or templates. Other use cases are code generation and streamlining the creation and updating of network documentation.
• Machine reasoning (MR) goes beyond pattern recognition to apply logic-based decision-making to complex, dynamic environments such as networks. MR technologies use acquired knowledge to model a series of possible steps and determine the optimal course of action, enabling AI systems to move from passive monitoring to active, intelligent management. Networking use cases include recommending remediation strategies for congestion issues or vulnerabilities.

Following are the primary ways AI is used to provide insights and enhance performance, security, and efficiency in key networking function areas:

• Network automation. AI is used to handle routine and repetitious operations such as configuration, management, and optimization to increase efficiency and reduce human error. Instead of manually configuring each router or switch, AI systems can automatically apply and validate configuration changes across distributed environments.
• Predictive analytics and insights. AI models can analyze vast quantities of data about network health, user behavior, and traffic patterns to anticipate bandwidth needs and performance issues. AI can predict when a link will become congested or when a device is likely to fail based on historical patterns and current trends.
• Performance monitoring. AI can detect patterns that signal performance degradation quickly and provide detailed insights into the root cause of the problem, such as a specific device, access point, switch, or router. AI enhances real-time monitoring by identifying patterns and deviations that signal potential network issues, often before human operators notice.
• Network security. AI can analyze threat patterns to proactively deliver insights and identify and resolve threats faster to recognize zero-day attacks. A credential stuffing attack can be recognized by a sudden spike in failed login attempts from a suspicious region, even if the attack uses previously unknown methods.
• Intelligent routing and scaling. An AI-enabled network can dynamically balance loads and optimize resource allocation to efficiently manage high traffic. During peak usage, AI can reroute traffic through underutilized links or spin up additional network resources to maintain performance.

As organizations adopt AI infrastructure to support AI workloads and applications, they face new demands on their network environments. These workloads require massive bandwidth, low latency, and real-time responsiveness, far beyond what traditional networking solutions were designed to handle. To keep pace, modern networking must evolve, and applying AI to optimize network performance is a natural and necessary response.

For example, use AI to improve traffic management to support high-capacity and low-latency data transport required for AI workloads. AI-powered network systems can monitor real-time traffic patterns, detect congestion points, and dynamically adjust routing and bandwidth allocation. This ensures optimal performance across compute clusters and data pipelines, maximizing throughput of AI data traffic while minimizing latency.

AI can also automate intelligent network segmentation to align network segments with AI clusters. AI infrastructure often involves isolated GPU clusters dedicated to specific workloads. AI can automatically analyze compute workloads, traffic patterns, and application behavior, then create or adjust network segments to align with workload needs. It can also dynamically assign or reallocate GPU resources to those segments based on demand and load balancing goals.