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Role of AI & ML In Network Automation & Security Monitoring

Every network administrator needs networking tools in order to troubleshoot difficulties related to diverse network conditions. Since not all tools can solve every problem, a variety of tools are needed to resolve different situations. Whether you're managing complex enterprise networks or troubleshooting performance issues, using the best networking tools & software for network engineers can make all the difference. With 2025 bringing new advancements and challenges, selecting the right solutions is more important than ever.

In this blog we have compiled a list of 7 best networking tools & softwares for network engineers which will make you productive & efficient. Whether you're an IT professional or an aspiring network engineer, these tools will enhance your workflow, improve security and optimize network performance.


Table of Content:

  1. What is the Need of AI for Network Automation?
  2. How Machine Learning Helps In Security Monitoring
  3. Importance of Network Automation
  4. Effective AI & ML Techniques for Network Automation
  5. Future Trends in AI Driven Network Automation
  6. Case Studies
  7. Conclusion


What is the Need of AI for Network Automation?

As cloud computing, IoT devices, and cybersecurity concerns increase, networks are getting more complicated. This complexity cannot be handled by conventional network management techniques. Artificial Intelligence (AI) can be very helpful in network automation as it can help in-

1. Saves Time to Work on the Strategic Work

Managing large networks manually takes a lot of time and is prone to mistakes. The current employees will have more time for strategic work and managing business innovations and enhancements when some network activities are automated with the help of AI.

2. Quick Problem Detection
AI-powered systems can analyse network traffic patterns to identify problems in real time. This guarantees a seamless and continuous network experience by detecting issues before they can have an impact on users.

3. Improves Security Measures
Cyberattacks are becoming more common, and hackers are using advanced tactics to access networks. AI is more effective than human administrators at spotting questionable activity, preventing possible dangers, and responding to attacks more quickly.

4. Smart Traffic Management
AI can analyse network traffic and optimise bandwidth utilisation, resulting in improved speed and efficiency. It can prioritise important processes like video conferencing while handling less critical jobs in the background.


How Can Machine Learning Helps In Security Monitoring

1. Finding Network Threats

Machine learning continuously scans the network's behaviour for irregularities in order to identify risks. To find important instances, machine learning engines analyse vast volumes of data almost instantly. These methods make it possible to identify policy violations, unknown malware, and insider risks.

2. Secured Browsing
In order to help users from visiting harmful websites, machine learning can identify "bad neighbourhoods" on the internet. By analysing Internet behaviour, machine learning automatically detects attack infrastructures that are set up for both emerging and existing threats.

3. Endpoint Malware Protection
ML Algorithms can identify malware that has never been seen before trying to harm endpoints. It uses the characteristics and actions of known malware to identify new dangerous files and activity.

4. Protect Cloud Data
By identifying threats and dangers in cloud apps and platforms, detecting location-based abnormalities, and analysing suspicious cloud app login activities, machine learning for security monitoring can safeguard productivity.


Importance of Network Automation

Managing networks nowadays is difficult due to newer technologies, regulatory rules, and evolving business needs. This clarifies why network automation is so crucial in the modern world. IT managers now have to make sure that the stability, availability, and dependability of network services provided to their end users are not impacted by the network's agility and evolution. However, a significant portion of the admin team's time and energy is consumed by the manual labour that frequently follows the tedious task of fixing human error in this duty. Network automation is essential since this can have a major effect on the team's productivity.


Network automation makes it possible for network modifications and other IT processes to be configured, managed, tested, and deployed with ease. It helps guarantee that your network is responsive to business requirements and increases the effectiveness of your network administration staff.
Some of the major benefits of network automation are:
  • Less time spent on monotonous work which eliminates chances of human mistake
  • Takes entire control on network configurations.
  • Tracks, initiates, or reverts changes to your network.
  • Keeps your company in compliance with a simplified automation strategy.
  • Increases the reliability of networks and strengthens security.


Effective AI & ML Techniques for Network Automation

Some AI/ML approaches have shown exceptional efficacy in network automation:

  • Natural Language Processing (NLP)
NLP makes it possible for computers to fully understand and analyse human language. NLP can be applied to network automation to examine user commands, configuration files, and network logs. This can automate configuration modifications, troubleshooting, and even creating reports using natural language queries.
  • Reinforcement Learning
Network operations can be dynamically optimised via reinforcement learning. For instance, in order to reduce latency and increase productivity, an algorithm might be trained to modify routing protocols in response to real-time traffic patterns.
  • Anomaly Detection
Unsupervised learning algorithms can examine system logs, performance data, and network traffic patterns to find anomalies that differ from typical behaviour. These irregularities might point to equipment malfunctions, security breaches, or other possible problems, enabling quick action and mitigation.
  • Predictive Maintenance
To forecast possible equipment breakdowns or performance bottlenecks, past network data is analysed using time-series analysis and forecasting models. Organisations may greatly increase network uptime and reliability by proactively resolving these problems before they cause interruptions.



Future Trends In AI Driven Network Automation

The trend of network automation is unmistakably moving towards greater autonomy and intelligence. We can expect networks that are not just self-regulating but also self-optimizing and self-healing as AI and ML technologies advance. It is getting more and more possible to operate networks with little or no human involvement.

Future networks may be able to automatically anticipate and prevent every possible problem, resulting in more seamless operations and more dependable and continuous services. This development will probably reinterpret the duties of network managers and may result in new innovations in infrastructure and network setups.

To sum up, the widespread use of AI and ML in network automation signals the beginning of a new era in network administration. With the help of these technologies, networks are becoming more intelligent, proactive, and resilient systems. The consequences for businesses and customers are enormous, promising not only increased operational efficiency but also considerably improved user experiences.




Case Study #1 Cisco AI Network Analytics


Cisco's AI Network Analytics, a division of its DNA Centre, offers insights into network performance through machine learning. It assists network managers in anticipating problems, proactively fine-tuning the network, and optimising network performance using predictive analytics.

Cisco AI Network Analytics has been applied in a number of situations, including the identification of anomalous patterns that may point to operational issues or security risks, enabling prompt remedial action.

REWE Group improved their network management skills by implementing Cisco AI Network Analytics. The IT staff is now able to devote more time to new initiatives and innovations that are essential to business operations because of this collaboration, which has decreased the amount of time required to fix network problems.

The use of AI/ML has made managing network workloads easier, reducing the amount of time needed for everyday administration activities and emphasising important signals that point to performance or connectivity problems.



Case Study #2 Anadot


Anodot offers analytics tools driven by AI that are intended to identify irregularities in real-time data. Businesses may resolve problems and seize opportunities by using its platform's machine learning algorithms to detect deviations from typical trends.

Anodot's real-time analytics were used by LivePerson, a conversational AI platform, to track a wide range of over 2 million variables every 30 seconds throughout its global data centres. For ongoing customer data availability and round-the-clock service reliability, this implementation was essential.

Because of Anodot's AI capabilities, LivePerson can identify and address irregularities instantly, ensuring both great customer happiness and operational effectiveness.




Conclusion

AI and ML for network automation & security monitoring are giving businesses access to previously unheard-of levels of intelligence, control, and visibility. Businesses may increase network performance, strengthen security, and streamline IT operations for increased agility and efficiency by using the power of these technologies.

 We may anticipate even higher levels of automation, intelligence, and autonomy in network administration as AI advances. Although there are still obstacles to overcome, AI and ML for network automation & security monitoring have a lot of potential advantages. Businesses that adopt these technologies will be well-positioned for growth in the digital era.



FAQ's

What is Network Automation?
The process of automating both virtual and physical device configuration, management, testing, deployment, and operation within a network is known as network automation.
What is the Role of a Network Automation Engineer?
Network automation engineers are professionals who optimize network operations using programming and automation techniques. They are directly involved in every stage of the process, including design, development, configuration, implementation, and testing.
What are the Different Types of Network Automation?
There are four different types of network automation which are network monitoring, performance management, security management and configuration management.
What is Security Monitoring?
Security monitoring is the continual analysis of data to detect potential security issues. It is often referred to as security event monitoring (SEM) or security information monitoring (SIM).
Why is Security Monitoring Important?
1. The risk of data breaches can be decreased with the aid of security monitoring. 2. It can assist organisations with regulatory compliance. 3. It could decrease the chance of unwanted access. 4. It can assist organisations maintain their stability.
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