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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.
Categories of AI-Powered Monitoring Tools
Enterprise Observability Platforms
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.
Threat Detection and Response Solutions
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-Centric Monitoring Tools
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.
Open-Source and Specialist Tools
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.
What You Should Look For in AI Monitoring Tools
- Use case alignment – How well does the tool address your strategic needs (threat detection, performance, cloud visibility, etc.)?
- ROI & automation – Does it reduce manual work and lower costs measurably?
- Ease vs. scalability – Will it serve your team now, and scale as complexity grows?
- Governance & risk – How understandable are the AI decisions, and what oversight tools are built in?
- Timeliness & future-readiness – Are you adopting at a pace that keeps you ahead, not catching up?
Final Words: How to Choose Wisely
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.
FAQs
How is AI used in network security?
It analyzes live traffic, spots anomalies, automates threat detection, and prioritizes responses.
What is a common use of AI in cybersecurity monitoring?
Identifying unusual behavior such as lateral movement or data exfiltration before it becomes a breach.
How is AI being used in safety and security systems?
Beyond network defense, AI upgrades surveillance systems with real-time video or anomaly detection using data-driven learning.
How is the AI program used for security and surveillance?
AI monitors network and physical environments continuously, flags suspicious activity, and automates alerting and triage.
Sources
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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
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