Modern networks are larger, more complex, and more critical than ever before. With more devices connecting every day and cyber threats growing in sophistication, you are under pressure to monitor environments that often feel unmanageable. AI-powered network monitoring tools have emerged to help address this challenge.
But not every solution delivers the same value. This article highlights the types of platforms available in 2025, explains how they approach monitoring differently, and shows what executives should consider before making adoption decisions.
Vendors in this space combine infrastructure, application, and network monitoring with AI engines that provide root-cause analysis and predictive alerts. Their value is in unifying complex environments, though cost and implementation can be significant for smaller teams.
Some platforms specialize in real-time threat detection. They use AI-driven “attack signal intelligence” to correlate behavior patterns, automate triage, and reduce time to detection. These systems are valuable for organizations that prioritize advanced threat defense.
Cloud-based monitoring solutions often emphasize usability and speed of deployment. They feature automated topology mapping, traffic analysis, and intuitive dashboards. While they can deliver fast wins for smaller organizations or MSPs, they may lack the depth needed for highly complex environments.
There are also open-source platforms that provide flexibility and scale for technically mature teams. These can handle high data volumes and integrate widely, but they require internal expertise and strong governance to be effective.
AI-powered monitoring has moved past early experimentation and is becoming part of the foundation for resilient organizations. Choosing wisely requires perspective and a clear framework for evaluation.
At Galson Research, we apply the Technology Prioritization Index (TPI) and insights from our expert network to help you understand where technologies stand, what is already proven, and what remains exploratory. This clarity gives boards the confidence to make informed decisions about monitoring, security, and technology adoption.
It analyzes live traffic, spots anomalies, automates threat detection, and prioritizes responses.
Identifying unusual behavior such as lateral movement or data exfiltration before it becomes a breach.
Beyond network defense, AI upgrades surveillance systems with real-time video or anomaly detection using data-driven learning.
AI monitors network and physical environments continuously, flags suspicious activity, and automates alerting and triage.
Faddom. (2023). AI in network security: Use cases, challenges and best practices. Faddom. https://faddom.com/ai-network-security-use-cases-challenges-and-best-practices/
Palo Alto Networks. (n.d.). The role of artificial intelligence in security automation. Palo Alto Networks. https://www.paloaltonetworks.com/cyberpedia/role-of-artificial-intelligence-ai-in-security-automation
Syracuse University, School of Information Studies. (2023). AI in cybersecurity. Syracuse University. https://ischool.syracuse.edu/ai-in-cybersecurity/
TechRadar. (2023a). LogicMonitor review. TechRadar. https://www.techradar.com/pro/logicmonitor-review
TechRadar. (2023b). Auvik review. TechRadar. https://www.techradar.com/pro/auvik-review
TechRadar. (2023c). OpenNMS review. TechRadar. https://www.techradar.com/pro/opennms-review
TechRadar. (2024). Smarter networks in the agentic AI revolution. TechRadar. https://www.techradar.com/pro/smarter-networks-in-the-agentic-ai-revolution
Tripwire. (2023). Understanding AI in network security. Tripwire. https://www.tripwire.com/state-of-security/understanding-ai-network-security
Wikipedia contributors. (2023a). Darktrace. In Wikipedia. https://en.wikipedia.org/wiki/Darktrace
Wikipedia contributors. (2023b). Dynatrace. In Wikipedia. https://en.wikipedia.org/wiki/Dynatrace