What is AI-native networking?

What is AI-native networking?

AI-native networking refers to computer networking systems that are conceived and developed with artificial intelligence (AI) integration as a core component to enable simpler operations, increased productivity, and reliable performance at scale.

Unlike systems where AI is added as an afterthought or a “bolted on” feature, AI-native networking is fundamentally built from the ground up around AI and machine learning (ML) techniques.  

As with all modern AI systems, AI-native networking systems are designed to learn from data, adapt to new situations, and improve over time. This continuous learning capability is a fundamental characteristic, allowing the system to become more efficient and effective as it gathers more data and experiences.

AI-native networks that are trained, tested, and applied in the correct way can anticipate needs or issues and act proactively, before the operator or end user even recognizes there is a problem. This saves IT and networking teams time, resources, and reputations, while simultaneously enhancing operational efficiency and improving overall user experiences.

Why does AI-native networking matter?

From digital transformation to high-profile AI initiatives to explosive user and bring-your-own-device (BYOD) growth, networks are experiencing tremendous and ever-growing pressure and focus. Given IT budgets and constraints related to skills availability and other factors, the combination of complexity and unpredictability of traditional networks can be a growing liability.

AI-native networking simplifies and streamlines the management of these complex networks by automating and optimizing operations. These networks dynamically adjust and scale to meet changing demands and resolve issues without requiring constant human intervention. By optimizing performance based on user behavior and preferences, they ensure seamless and enhanced experiences.

Removing traditional networking limitations, such as manual, outdated processes and poor user experiences enables organizations to innovate and experiment with new business models, services, and technologies that require robust and adaptive network infrastructure.

What are the benefits of AI-native networking?

Adopting AI-native networking offers a wide range of benefits, including:

Enhanced efficiency and performance

AI algorithms can optimize network traffic routes, manage bandwidth allocation, and reduce latency. This results in faster and more reliable network performance, which is especially beneficial for bandwidth-intensive applications like video streaming, large-scale cloud computing, and supporting AI training and inference processes.

Predictive maintenance and downtime reduction

By anticipating issues before they happen, AI-native networks can schedule maintenance proactively, reduce unexpected downtime, and fix issues before they impact end users. This is especially crucial for businesses where network availability directly impacts operations, revenue, and reputation.

Improved security

With the capability to analyze vast amounts of network data in real-time, AI-native networks allow for the early detection of anomalies and potential security threats. This proactive approach to security helps in thwarting cyberattacks and protecting sensitive data.

Cost savings

Automating network management tasks reduces the need for manual intervention, which can lead to significant cost savings in terms of labor and operational expenses. Additionally, predictive maintenance can prevent costly emergency repairs and downtime.

Scalability and flexibility

AI-native networks can adapt to changing demands without the need for manual reconfiguration. This scalability ensures that the network can handle increasing loads and new types of devices seamlessly.

Enhanced user experiences

AI-native networks optimize network performance based on user behavior and preferences, ensuring continuously exceptional experiences for IT operators, employees, consumers, and users of public internet services.

How AI-native networking works

Good AI starts with the right data. For an AI-native network to be most effective, it needs to not only collect vast quantities of data, but also high-quality data. Bad data, or the wrong data, can lead to inaccurate or biased responses. This collected data includes traffic patterns, device performance metrics, network usage statistics, security logs, real-time wireless user states, and streaming telemetry from routers, switches, and firewalls.

The collected data is analyzed using ML algorithms. These algorithms are trained to recognize patterns and anomalies in the data. Learning from the network's behavior over time, they develop and improve, which helps in making more accurate predictions and decisions.

Applying explainable AI processes and methods allows users to understand and trust the results and output created by the system’s ML algorithms. It’s key to providing insights into how data is being utilized and evidenced for its output.

Based on the analysis and trustworthiness of the data, an AI-native network can provide the right real-time response. The decision-making process is dynamic and occurs in real-time, allowing the network to adapt quickly to changing conditions. Potential responses include:

• Predictive modeling: By predicting future network states or potential issues, it can forecast traffic spikes or identify weak spots in the network that are susceptible to failure or attacks.
  
• Self-optimization: With AI-native networks, if the AI detects that a particular route often becomes congested at certain times, it can preemptively reroute traffic to maintain optimal performance.
  
• Proactive maintenance and self-healing: The network can identify and diagnose issues before they cause significant problems, like predicting hardware failures. It can also take corrective actions automatically, such as rebooting a malfunctioning device or switching to backup systems.
  
• Security enhancement: When a potential threat is detected, the network can implement security protocols, such as isolating affected network segments or blocking malicious traffic.
  
• User experience management: AI-native networking can tailor the network performance to meet user demands, adjusting priorities and resources based on user behavior and preferences.

AI-native networking use cases

AI-native networking finds its application in a variety of use cases across different industries. These use cases typically fall into one of two categories: AI for networking and Networking for AI.

AI for networking

AI-native networks can continuously monitor and analyze network performance, automatically adjusting settings to optimize for speed, reliability, and efficiency. This is particularly useful in large-scale networks like those used by internet service providers or in data centers.

By predicting network failures or bottlenecks before they occur, AI-native networks can prompt preemptive maintenance, reducing downtime and improving service reliability. This is crucial for critical infrastructure and services like hospitals, emergency response systems, or financial institutions.

AI-native networking can detect unusual patterns indicative of cyber threats or breaches. This includes identifying and mitigating DDoS attacks, malware, or unauthorized access attempts, crucial for protecting sensitive data in sectors like banking, government, and defense.

Networking for AI

Unique traffic patterns, cutting-edge applications and expensive GPU resources create stringent networking requirements when performing AI training and inference. AI-native networking systems help deliver a robust network with fast job completion times and excellent return on GPU investment.

AI-native networking and Juniper Networks

Juniper Networks built the industry’s first AI-Native Networking Platform from the ground up to take full advantage of the promise of AI. This AI-Native Networking Platform delivers the industry’s only true AI for IT operations (AIOps) with unparalleled assurance in a common cloud — end-to-end across the entire network. From real-time fault isolation to proactive anomaly detection and self-driving corrective actions, it provides campus, branch, data center, and WAN operations with next-level predictability, reliability, and security.

Enterprises rely on the Juniper platform to significantly streamline ongoing management challenges while assuring that every connection is reliable, measurable, and secure. They are also building highly performant and adaptive network infrastructures that are optimized for the connectivity, data volume, and speed requirements of mission-critical AI workloads.

It all started with a strategic pivot to an experience-first approach that focuses on asking the right questions to deliver the best experiences for both network operators and end users. Its ability to deliver the right experiences is built on three fundamental pillars: 1) the right data, 2) the right real-time responses, and 3) the right infrastructure.

The right data

Juniper starts by asking the right questions to capture the right data that assesses networking down to the level of each user and session. With over 7 years of reenforced learning, robust data science algorithms, and relevant, real-time telemetry from all network users and devices, it provides IT with accurate and actionable information.

The right real-time responses

Juniper provides IT operators with real-time responses to their network questions. Customizable Service Levels with automated workflows immediately detect and fix user issues, while the Marvis Virtual Network Assistant provides a paradigm shift in how IT operators interact with the network. Marvis answers IT questions in natural language as a human would.

The right infrastructure

From devices to operating systems to hardware to software, Juniper has the industry’s most scalable infrastructure, underpinning and supporting its AI-Native Networking Platform. The true cloud-native, API-connected architecture is built to process massive amounts of data to enable zero trust and ensure the right responses in real time.

Juniper laid the foundation for its AI-Native Networking Platform years ago when it had the foresight to build products in a way that allows the extraction of rich network data. By using this data to answer questions about how to consistently deliver better operator and end-user experiences, it set a new industry benchmark.