Artificial intelligence (AI) in networking refers to the application of AI principles to manage complex IT operations. It accelerates decision-making and accuracy and enables process automation. It entails integrating AI and machine learning (ML) technologies into computer networks to boost their performance, security, and management.
ML, a subset of AI, empowers computers to learn from data without requiring explicit programming. This capability expedites troubleshooting, streamlines issue resolution, and offers remediation guidance. It also generates insights to upgrade user and application experiences.
Together, AI and ML can predict and respond to problems in real-time, enhancing security by developing threat response and mitigation. AI and ML-powered network analytics customize network baselines for alerts, reducing false positives and accurately identifying issues, trends, anomalies, and root causes.
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What is automated networking?
Automated networking, also known as network automation, is a process that involves using software tools to automate network configuration, management, testing, deployment, and operation for both physical and virtual devices. Its primary objective is to maximize network efficiency.
The process increases network service availability, reduces human errors and costs, and facilitates faster connectivity. It also leverages technologies like software-defined networking (SDN) and intent-based networking (IBN) to boost network reliability and agility while allowing IT staff to focus on more strategic tasks.
8 tips for creating an AI strategy for your network
Developing a successful AI strategy for your network involves setting clear objectives, assessing data readiness, analyzing AI value, carefully considering technology selection, setting metrics, piloting projects, and continuously evaluating and refining AI outcomes to achieve your business goals and improve cloud management.
1. Set clear objectives
Define your organization’s goals and objectives for using AI in your network. What do you want to achieve with AI? It could be improving customer service, optimizing operations, increasing sales, or any other business goal.
Align your AI objectives with your overall business strategy and make sure they are realistic and measurable. This will provide a clear direction for your AI initiatives.
2. Assess data readiness
AI needs high-quality, structured data to function. Ensure that you have a solid foundation in place. Additionally, identify any gaps in your data and plan for how you will collect, preprocess, and store it for data readiness.
3. Analyze how AI can add value to your business
Evaluate how AI can make a meaningful impact on your business by considering different use cases and scenarios. Analyze how it can simplify processes, reduce costs, maximize revenue, or elevate customer experiences. Prioritize AI applications that align with your business priorities.
4. Select the appropriate technologies
You should choose the AI technologies and tools that best suit your objectives and data readiness. This may include ML frameworks, cloud-based AI services, or specialized hardware. Consider factors like scalability, compatibility with your existing systems, as well as the availability of talent and resources for implementation.
5. Establish metrics
Define key performance indicators (KPIs) and metrics that will gauge the success of your AI initiatives. Make sure to tie these metrics directly to your objectives. Your metrics could be diverse, encompassing accuracy, efficiency gains, customer satisfaction scores, an increase in revenue, or any other relevant measures of success.
6. Pilot projects
Start with small-scale pilot projects before rolling out AI solutions across your entire network. Pilots help you test the feasibility of your AI strategy and make adjustments as needed. They will allow you to learn from real-world implementation and gather valuable insights before committing significant resources.
7. Monitor and refine your AI outcomes
Track how your AI solutions perform against the established metrics. Then use this feedback to identify and address areas that need adjustment. Continually refine your AI models and strategies to boost their accuracy and effectiveness.
8. Invest in continuous learning and improvement
Encourage continuous learning in your organization by investing in the training and upskilling of your teams, focusing on AI-related certifications, skills, and technologies. Stay updated with the latest AI developments to maintain your competitive edge and adjust your AI strategy as needed.
Use cases for AI in enterprise networking
AI in enterprise networking provides a large variety of potential use cases, including opportunities to enhance efficiency, security, and network performance.
AI plays a significant role in log analysis. It effectively handles various tasks such as anomaly detection, incident response optimization, and predictive maintenance. It categorizes logs, generates data summarization, and analyzes user behavior for security purposes.
Furthermore, AI maintains compliance, aids in capacity planning, and fine-tunes performance by sifting through vast amounts of log data. This integration empowers organizations to proactively manage network health, enhance security, and make data-driven decisions with precision.
AI in advanced analytics helps enterprise networking by extracting insights from network data. It detects trends, forecasts usage, and maximizes opportunities. It also predicts maintenance issues from historical data and supports data-driven decisions with visualizations and reports. AI transforms network data into valuable information, improving efficiency, cost, and performance.
In performance monitoring, AI improves network performance and user experiences by analyzing network data to detect issues. It also predicts and prevents performance degradation from historical data. Additionally, it reports and visualizes performance metrics, allowing network administrators to make data-driven decisions.
AI in security alert management detects and responds to threats by analyzing network data. It identifies suspicious patterns and triggers alerts. It also correlates security events to show potential threats. AI fortifies cybersecurity, reduces response times, and safeguards network infrastructure.
For traffic management, AI optimizes resource allocation and reduces congestion by making dynamic routing decisions from real-time data. It also identifies traffic patterns, prioritizes critical applications, and adapts to changing conditions. Moreover, it balances load and enforces Quality of Service (QoS), delivering a seamless and responsive user experience.
Intelligent programmable automation controller (IPAC)
AI-driven Intelligent Programmable Automation Controllers (IPACs) automate and control network operations. By leveraging AI, they enhance network configuration, provisioning, and management. IPACs also support dynamic adjustments based on network conditions and user demands for optimal performance and resource allocation.
In addition, IPACs swiftly respond to threats and implement security policies. They equip organizations to achieve greater network flexibility, reliability, and security, ultimately increasing overall network efficiency.
Autonomous scanning and patching
AI-powered autonomous scanning and patching reduce the window of vulnerability and ensure prompt implementation of critical security updates, bolstering security posture. These systems continuously scan network assets, find vulnerabilities, and automatically apply patches or remediation measures without human intervention.
Autonomous scanning and patching boost resilience against evolving threats by offering a proactive defense against potential exploits and minimizing manual workload for IT teams. They make network security more robust and adaptive in the face of emerging threats.
Automated provisioning, enabled by AI, improves enterprise networking by automating the configuration, allocation, and scaling of network resources and services. It minimizes human error and increases agility in provisioning network assets. It also amends resource utilization and saves costs. Automated provisioning lets organizations meet business needs efficiently, raising productivity.
Top 5 artificial intelligence tools for networking
Artificial intelligence tools for networking include software applications that use ML algorithms, natural language processing (NLP), and data visualization to elevate network functionality, reinforce security, simplify operations, and enhance user satisfaction. Here are some of the top AI tools for networking:
Cisco DNA Center
Cisco’s Digital Network Architecture (DNA) Center utilizes AI and ML to provide advanced network automation, assurance, and analytics. It aids network administrators in adjusting network performance, identifying issues, and automating tasks.
This tool has features that help you get the network up and running faster, reduce outages and minimize business impact, deliver optimal user experience, and secure the digital enterprise. Furthermore, Cisco DNA Center lets you customize and extend your network capabilities with open APIs, SDKs, and partner applications.
IBM Security QRadar Suite
IBM Security QRadar Suite uses AI for speed and efficiency. It unifies the security analyst experience to allow for faster threat detection and response. Its AI capability helps in automating routine tasks, freeing up time for analysts to focus on more complex issues.
IBM Security QRadar also delivers advanced analytics that uncover patterns and anomalies that might indicate a security threat. This proactive approach helps in preventing potential breaches before they occur.
Juniper Mist AI
Juniper Mist AI enhances Wi-Fi experiences by automating troubleshooting, detecting anomalies, and maximizing performance. It has a cloud-based platform that collects and analyzes data from various sources, such as wireless access points, switches, routers, and firewalls.
Juniper Mist AI also has various AI-powered security and location services integrated into the Juniper Mist dashboard. It has a virtual network assistant called Marvis, which uses AI to give guidance and troubleshooting to network operators.
Fortinet FortiGuard Labs
Fortinet FortiGuard Labs is a good networking tool that uses AI because it can detect and prevent cyberattacks in real time. It has a global network of sensors that collect threat data and use AI to analyze it.
It also offers various security services that are powered by AI and integrated into the Fortinet Security Fabric. Additionally, it publishes useful resources and insights on the latest cyberthreats and how to mitigate them. It also supports a wide range of network security products, such as firewalls, VPNs, and SD-WAN.
Aruba Networking AI
Aruba Networking has real-time anomaly detection for network performance and monitors potential failures in authentication, DHCP, and Wi-Fi connectivity. Its patented ML algorithms filter out false positives.
Furthermore, Aruba Networking delivers actionable recommendations to highlight necessary changes for optimal network performance. It features a closed-loop operation for continuous self-optimization and sustainability features for better power management.
What are the benefits of AI for enterprises?
Using AI brings many benefits to enterprises, including improved decision-making, better customer experience, increased efficiency, predictive analytics, cost reduction, and innovation.
AI can provide valuable insights from data analysis, resulting in more informed and data-driven decision-making.
Better customer experience
AI-powered solutions like chatbots, personalized marketing, recommendation systems, and virtual assistants can give 24/7 personalized support, elevating customer experience.
AI can automate routine tasks, reducing human error and freeing up employees’ time to focus on more complex tasks. This leads to increased efficiency and productivity.
AI’s ability to forecast trends and patterns lets enterprises anticipate market changes and adapt accordingly.
By automating processes, AI can help lower labor and operational costs, enhancing the bottom line and leading to substantial cost savings.
AI promotes innovation by aiding in research and development, discovering new products and services, and refining existing ones. It opens up new avenues for innovation that were not previously possible.
What challenges still exist for AI in networking operations?
Despite significant advancements, some challenges continue to persist in the realm of AI for networking operations, such as data quality, interoperability, security, explainability, and scalability.
Data quality and consistency
AI models rely heavily on network data for learning and making accurate predictions. However, acquiring, labeling, and processing this data is no simple task. It can be a costly, labor-intensive, and complex process. Furthermore, the presence of noise, missing information, or irrelevant data in the network data can negatively impact the performance of AI models.
Interoperability issues in adopting AI for networking operations stem from the need for AI systems to work seamlessly with a variety of network devices, diverse communication protocols, and multiple vendors’ proprietary technologies. Incompatibility can lead to integration issues, ranging from minor inconveniences to major disruptions in network operations.
Security and privacy
AI in networking operations faces security and privacy challenges due to potential mishandling of personal data, risk of cyberattacks, ethical concerns around biased decision-making, and lack of transparency. Many AI systems need to access sensitive network data, and any compromise of this data can lead to serious security breaches.
On the privacy front, AI systems must strike a balance between accessing necessary data for better operation and protecting personally identifiable or sensitive information. Therefore, integrating AI into networking operations necessitates robust security measures and privacy-focused techniques.
Explainability and trust
Explainability and trust are key challenges for AI in networking operations. While AI systems streamline network traffic and detect anomalies, they can be complex and hard to understand. As a result, problem-solving and troubleshooting become difficult and reduce confidence in AI-driven solutions.
Moreover, AI models need to be unbiased and secure. To solve these problems, some efforts aim to create AI models that explain themselves, audit their processes, and follow clear regulations.
Some AI in networking operations still struggles with scalability. Networks become bigger and more complex, and AI systems deal with more data and devices. AI algorithms and models need to process and analyze this data fast and well. Otherwise, scalability problems can cause delays, slow responses, and system jams, which can cause bottlenecks or downtime on critical networks.
Bottom line: Adopting AI in enterprise networks
Adopting AI in enterprise networks can enhance network performance, fortify security, and modernize operations. It can also enable new capabilities such as self-healing networks, predictive analytics, and intelligent edge computing. By leveraging AI in enterprise networks, your business can gain a competitive edge, improve customer satisfaction, and increase operational efficiency.
However, it’s important to keep in mind that AI is still a nascent technology in many respects. Successfully integrating AI technologies relies on establishing a foundation of data quality, putting strong security measures in place, and ensuring seamless interoperability.
By carefully planning and diligently addressing these challenges, organizations can position themselves at the forefront of a new era in network management and security.
Learn how the best AI certifications can provide you with the knowledge and expertise to utilize the full potential of artificial intelligence in network optimization and automation. Explore our top recommended AI certifications to unlock the power of AI for improving network performance and efficiency.
Aminu Abdullahi contributed to this report.