Artificial intelligence (AI) is enhancing network performance and security in previously unimaginable ways. Jason Gintert, Chief Solutions Officer at Nitel, sheds light on how AI is transforming business networks, offering practical and visionary insights on network optimization and security.
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Enhancing Network Performance with AI
With predictive analytics and real-time optimization, Gintert says, AI allows businesses to “track and identify traffic patterns and predict future network demands.” This capacity for prediction is valuable for businesses with seasonal or cyclical demand spikes. As Gintert elaborated, “For example, retail businesses can leverage AI to predict high-traffic periods like the holiday season. AI can recommend augmenting network capacity during these times, ensuring that the network doesn’t crash or slow down under increased demand.”
In addition to predictive capabilities, AI also improves real-time network adjustments through automated network management. “Building a network that fixes congestion or packet loss, for example, in real-time without human intervention is powerful,” Gintert pointed out. It streamlines operations and ensures that networks maintain optimal performance under the most stressful conditions.
Gintert also emphasized AI’s significance in improving service quality by helping prioritize critical apps. AI-driven systems identify and categorize apps based on their importance to business operations, allowing network administrators to better allocate resources “By leveraging AI, businesses can ensure that their most critical applications receive priority bandwidth, which is paramount to maintaining optimal network performance,” said Gintert.
The Role of AI in Network Security
AI is also at the forefront of mitigating network security risks – especially in threat detection. “AI handles the vast volume of data generated by network traffic and helps detect both known and unknown threats,” Gintert explained. AI’s ability to analyze baseline network behavior and flag deviations makes it an invaluable tool for cybersecurity teams.
AI also streamlines the incident response process by automating countermeasures. Its ability to “self-heal” the network with proactive measures is a game changer. “When a threat is detected, AI systems automatically implement countermeasures to contain and mitigate the threat before it escalates,” Gintert said.
Another crucial element in AI-enhanced network security is identity verification. Gintert discussed how AI can analyze user behavior to detect anomalies in real-time: “By understanding how users typically interact with the system, AI flags suspicious activity and helps administrators take the necessary steps to secure the network.”
Key Areas for AI in Optimizing Network Solutions
AI’s applications in business networks are multifaceted. Gintert highlighted some primary areas where AI can be leveraged to optimize network solutions. First, network monitoring and analytics benefit from AI’s ability to process large datasets far more efficiently than humans. “AI can analyze vast amounts of telemetry data and quickly bubble up potential issues to human operators for further inspection,” he said. This proactive approach significantly reduces downtime and improves operational efficiency.
Security operations teams also stand to gain from AI. For instance, intrusion detection systems (IDS) are greatly enhanced by AI’s ability to detect subtle and complex attack patterns. Gintert mentioned that AI’s aggregation of global threat data makes it particularly useful in protecting against emerging risks such as zero-day attacks. “AI can identify anomalies that a human might miss, enabling businesses to respond to security threats more rapidly and effectively,” he added.
Capacity planning is another area for AI advancements. “AI helps businesses forecast future network demands by analyzing past usage patterns,” Gintert explained. This predictive capability ensures businesses can scale their network infrastructure, avoiding bottlenecks and ensuring the network operates efficiently during peak usage times.
AI-Driven Business Intelligence and Network Operations
This type of AI-driven business intelligence goes beyond network management and delves into improving customer experiences. As Gintert put it, “Businesses can not only enhance network operations but also improve customer satisfaction by tailoring their services based on real-time and historical data.” For example, AI analyzes customer behavior through Bluetooth low energy (BLE) technologies. “Retailers can track customer movements inside their stores, analyzing where they spend the most time, and adjusting their product placements and promotions accordingly,” he said.
A particularly fascinating example of AI in retail involves using predictive analytics to prepare for rush periods. “Take fast-food restaurants, for example. AI can analyze trends in real-time and predict when a rush is coming. The system can alert staff to prepare the most popular food items ahead of time, enhancing both service speed and customer satisfaction,” Gintert explained.
The Future of Self-Healing Networks
One of the most striking advantages of AI in network management is its ability to enable self-healing networks. Gintert explained that AI can proactively reset devices, reboot systems, or even shut down compromised parts of the network in case of an attack. “AI can recognize when a device is malfunctioning and go through a series of pre-programmed steps to resolve the issue automatically,” he said. This ability to self-heal can significantly reduce the need for human intervention, freeing up IT staff to focus on more complex problems.
According to Gintert, this self-healing capability is not some distant technology—it’s already in the early stages of implementation now. “We’re not too far off from seeing AI-driven networks that autonomously make configuration changes to optimize performance and security,” he said, estimating that widespread adoption of such technologies could happen within the next two to three years.
Recommendations for Network Architects
For network architects, Gintert offered some practical advice. “Start using AI now,” he said emphatically. He suggested that network architects experiment with AI tools like large language models, including ChatGPT and others, to understand how these technologies can augment human capabilities. “These tools can act as sounding boards or provide configuration recommendations. The more you engage with AI now, the better prepared you’ll be to leverage its full capabilities as it continues to evolve,” Gintert advised.
Gintert also recommended closely monitoring industry developments related to AI. “Staying informed about the latest trends and innovations in AI will be crucial for network architects as AI becomes more prevalent in business operations,” he concluded.
The potential for AI to revolutionize network performance and security is vast, and businesses that embrace these technologies will gain a competitive edge. From predictive analytics to self-healing networks, AI enables more efficient, secure, and responsive business operations.
It starts now, Jason stated. The key to success will increasingly lie in understanding and adopting AI’s capabilities. Organizations that take this proactive approach will gain a competitive edge and a powerful new means of continuously optimizing their operations.