How AI supported networks can redefine efficiency and experience

The network has always played a pivotal role in digital transformation. In the current times, it has become even more important, as everything is connected to the network, and the quality of access defines the outcome of any business initiative. With a rise in digital initiatives, the network is under severe pressure to perform adequately. Network administrators also have to manage the huge inclusion of remote workers and associated application usage. There are also issues related to latency and bandwidth costs. Security is also another critical aspect, as attacks by hackers have gone up significantly.

Given the huge complexity of current networks and the huge management challenges in ensuring service assurance, network automation is critical for ensuring a high-performance network. This can be done effectively by using AI. The purest definition of artificial intelligence (AI) is software that performs a task on par with a human expert. AI plays an increasingly critical role in taming complexity for growing IT networks. By using AI, enterprises can significantly improve the performance of their networks without much human intervention. From an infrastructure support and operations perspective, AI can be used for a host of activities. This could include network planning, network optimization, fault detection and predictive maintenance. 

By using AI, enterprises can improve the quality of their networks by analyzing and correlating traffic growth with user experience. AI can also play a big role in automated network traffic analysis. Typically, networks generate a huge amount of data, and AI can help enterprises get better insights into the data and look at major trends or anomaly detection. For example, if the AI solution detects an unusual volume of traffic, it could potentially alert the service provider or even block access, if it suspects any signs of malicious behavior. Similarly, from a capacity planning perspective, as more applications are added and more users access the network, an AI-based network solution can accurately measure network utilization from different sources and help enterprises plan adequately for the future. Depending on the applications in use, an AI-based system can dynamically provision bandwidth to ensure a seamless experience. 

As networks are typically impacted by a diverse number of factors, it becomes extremely challenging for network administrators to find out the root cause. Adjusting networks manually is complex as there are too many variables to be considered. 

AI can help in sifting through huge volumes of data and identify what factor (device type, OS, access points or switch) has caused a performance degradation or a downtime. This can help immensely in identifying the root cause and isolating a problem, as AI solutions can correlate events in real-time with data and take timely decisions to rectify issues. This data-driven approach can help in troubleshooting issues quickly. By using AI, network administrators can quickly understand the root cause of the issue, thanks to the directions or insights provided. AI systems can learn and unlearn from the key issues. For example, AI-driven networks can aggregate and analyze data before a network outage and use the data subsequently for understanding and resolving issues in a better way. In many cases, AI can correlate network related activities and resolve problems proactively before they are noticed by end users. This helps network administrators in being proactive in managing network related issues rather being bogged down by issues that can cause any performance degradation or downtime. 

In an age where networks are constantly under attack, an AI system can enable continuous checking of security vulnerabilities. An AI enabled network can perform continuous compliance monitoring, compliance reporting and security automation. The insights discovered by the AI-enabled tool can be validated against a set of pre-defined policies to confirm policy adherence. This can help in detection, alerting key people, remediation and taking counter measures. Similarly, using behavioral analysis tools, AI enabled tools can also help in analyzing the network and responding to an attack quickly -- this is extremely useful in the case of detecting zero-day attacks. More importantly, automation enabled by AI, can also help in quickly bringing up another site as part of a disaster recovery plan, if the first site is hit by ransomware or cyberattacks. In this age of zero-day vulnerabilities, it is common to find organizations scrambling to scan their networks regularly to find possible vulnerabilities. In such cases, automation can help in scanning for vulnerabilities quickly by simply running an automation script. This makes it possible for even a small team or security engineers to quickly respond to a security threat.

Key AI Technologies

Kindly keep in mind that AI is a large and growing field, with several branches. In networking, there are three subfields of AI that are most relevant. These are:

• Machine Learning (ML): In which we use data to learn patterns, so we can form inferences on new data for tasks like classification or prediction.
• Natural language processing (NLP): Which includes speech recognition and natural language understanding. NLP uses vocal and word-based recognition to make interfacing with machines easier via natural language cues and queries.
• Machine reasoning (MR): Also known as ‘problem solving,’ is the ability to dynamically react to change and by doing this, reusing existing knowledge for new and unknown problems. With machine reasoning, problems are solved in ambiguous and changing environments.

AI can help organizations move towards a world of rapid provisioning and problem resolution. Today, an AI-enabled solution can make it possible for organizations to automatically provision and setup a network without extensive manual efforts. This enables networks or devices to be easily setup in remote locations where it is difficult to provide on-site support. 

Outcomes of AI for Networking

Using AI in networking can lead to several profound benefits. Some of these include:

• Predictive user experience
• Dynamically adjust B/W
• Quick find of root cases
• Deploy virtual network assistants
• AI makes a positive impact on the world when teamed with other technologies. The convergence of IoT and AI will provide proactive trend-spotting and problem-solving to the business world. One good example of this is when AI and IoT help us predict where there might be a safety issue in a factory or mine before it becomes an accident, protecting workers and saving lives.  

As networks grow in complexity, the role of AI will increasingly become more prominent due to the ability of AI-enabled tools to quickly reduce downtime, provide insights to enable proactive maintenance and reduce operational costs.

This article has been written by Manoj Chitgopeker, Senior Director – Managed Network and Collaboration Services, NTT Ltd. in India