If 2023 were to be assigned its own word, it’d probably be ‘AI’. Artificial intelligence and machine learning are rapidly emerging technologies that have truly taken off over the last few years. As a result, these technologies are becoming more accessible and are being incorporated into business operations across almost all sectors. In 2022, more than one in three organisations reported using AI in some form, and over 40 per cent were looking into AI to consider incorporating it into their business processes.
Defining Artificial Intelligence and Machine Learning
Before we delve into the world of AI and machine learning in relation to network automation, let’s define the two terms. Artificial intelligence is the ability of a computer to simulate tasks typically undertaken by humans. Today, most sectors use AI in some form. For example, self-driving cars, social media, digital assistants, digital maps, and autocorrect all use AI to function.
Machine learning, on the other hand, is a field of artificial intelligence that learns and adapts without following explicit instructions using algorithms and statistical models to analyse and identify patterns in data.
The Role of AI and Machine Learning in Network Automation
How exactly are AI and machine learning impacting network automation? Network automation involves automating the preparation, deployment, processes and optimisation of networks and their systems. The end goal is to create networks that adjust themselves to autonomously improve their responses to real-time patterns, configurations, and software updates, among other factors.
AI and machine learning are invaluable tools for enhancing process automation and decision-making within IT operations. Traditional human-decision making sometimes fall short when managing networks, mainly due to the sheer complexity and unpredictability of systems. However, integrating AI and machine learning can address these limitations by providing insights from historical data, which can be implemented to improve efficiency.
Automated networking can boost operational efficiency for organisations—it’s cost-effective, improves speed, and allows businesses to be agile. If there’s one thing businesses need to prioritise in the rapidly evolving IT environment, it’s tools that improve agility! By taking certain tasks away from IT teams, automated networking allows them to focus on high-level tasks such as strategic planning, security management and troubleshooting complex networking problems that may suddenly arise.
The utilisation of AI and machine learning is clearly having a positive impact. One survey revealed that the optimisation of internal business operations was the top benefit of AI for 41 per cent of respondents. Now, let’s explore how AI and machine learning are making a difference in networking in more depth.
AI Networking Applications
AI has numerous networking applications. For example, AI systems can analyse network traffic patterns and adjust network parameters automatically to optimise performance. It can also predict network failures and performance problems before they have the chance to happen and cause havoc. In May 2022, Cisco unveiled its new AI engine that proactively predicts network issues, allowing businesses to proactively manage their networks rather than react to problems when they arise.
But that’s not all—AI can boost network security by identifying and then responding to security threats in real time. For example, AI systems can detect suspicious or unusual network activity that may indicate a cyber attack, such as a traffic spike. Once the threat has been identified, the system can take measures to protect the network, such as isolating or blocking suspicious activity to ensure it can’t inflict damage.
Finally, AI can automate routine tasks to save IT teams precious time. Routine network management tasks, such as updating software or configuring network devices, can be automated to reduce the risk of human error and allow teams to spend more of their time working on high-level tasks.
Machine Learning Applications
Machine learning, a form of AI, has had a significant impact on the world of networking. For example, machine learning algorithms can analyse historical traffic data to predict future traffic patterns, enabling organisations to prepare appropriately. But machine learning’s uses do not end there. It can also be used to diagnose and resolve network problems, for example, by analysing network logs and identifying patterns that might indicate the root cause of a given issue.
Machine learning is also used to analyse traffic flows from endpoint groups and provide granular details, such as source and destination, service and protocol, to define policies and allow or deny interactions between groups of devices. Finally, the transformative technology can be used to optimise network performance by learning the best configuration settings for various network conditions.
In Summary
AI and machine learning are transforming network management by automating routine tasks, improving security and optimising network performance. As IT professionals, it’s vital to stay updated with these advancements, such as AI and machine learning, as leveraging them effectively will have major benefits for organisations across all sectors. Today, AI and machine learning are more than just buzzwords—they are powerful tools that are transforming network automation for the better.