A new research project called NetMoniAI shows how AI agents might reshape network monitoring and security. Developed by a team at Texas Tech University, the framework brings together two ideas: distributed monitoring at the edge and AI-driven analysis at the center.
The work is still research stage, but it gives CISOs a sense of what could be possible if agentic AI systems make their way into enterprise environments. The project is open source, so it is also something the community can test and build on.
Central controller architecture
A layered design for detection and correlation
The system is built around two layers. At the node level, lightweight agents sit on individual machines and watch local network traffic. These agents look for anomalies and pass along findings. They can use language models to help classify events and generate human readable summaries.
On top of that sits a central controller. It collects agent reports and looks for patterns that cross the network. If one node notices something strange, the controller can check if other nodes are seeing similar signs. The idea is to give a picture of what is happening across the environment while still letting local agents act independently.
Early results show speed and scalability
The team tested the framework in two ways. First, they ran it on a small physical testbed where network conditions could be degraded. In this setting, the system was able to detect anomalies and classify traffic within about five seconds. Second, they ran simulations with up to 50 nodes, including scenarios with denial of service and reconnaissance attacks. In those tests, local agents spotted unusual traffic and the controller tied those observations together to confirm coordinated threats.
For CISOs, the main point is that the design handled both small scale and larger scenarios without introducing major delays. It also offered interpretability through a dashboard and chatbot that could explain what the system was seeing.
Why hybrid monitoring could change SOC operations
Network monitoring has long faced a trade-off. Packet level inspection offers detail but is hard to scale. Flow based monitoring scales better but can miss fast moving threats. Most organizations still depend on central log analysis and rule sets, which struggle to adapt and often produce false positives. NetMoniAI is an attempt to combine the strengths of these approaches with more autonomy.
If such systems develop further, they could help SOC teams by cutting down on redundant alerts and surfacing distributed attacks that are hard to catch with siloed monitoring. They could also offer more natural explanations of what is happening, which matters when CISOs need to brief other executives.
Corey Nachreiner, CISO at WatchGuard, said hybrid monitoring could have an impact in practice. “Many real-world attacks start very local, affecting one server or workstation, but then expand to affect wider enterprise networks. An AI-based, hybrid system would be good at detecting anomalies early on the initial victim system. The central agent could then correlate additional anomalies from scans and follow-on attacks, giving defenders multiple chances to break the attack chain,” he explained.
Challenges in moving from lab to enterprise
The research is promising, but there are limits. The framework has not been tested in production scale enterprise networks where traffic volumes, policy constraints, and regulatory requirements make things more complex. The reliance on large language models introduces questions about cost, latency, and accuracy. There is also the broader concern of how much autonomy security teams are comfortable giving AI agents.
Pallavi Zambare, co-author of the research, acknowledged those challenges. “Moving NetMoniAI from lab tests and simulations to real enterprise networks comes with several hurdles. Scalability, integration with existing SOC tools, trust and explainability, and regulatory compliance all have to be addressed before wide deployment. The first organizations that could benefit are those with distributed infrastructures but limited staff, such as mid-sized enterprises or managed service providers,” she said.
Zambare also stressed that the system is not designed to replace human decision-making. “Analysts retain final authority, while the framework provides structured reports, summaries, and policy recommendations that speed up context and correlation. This human-in-the-loop approach balances AI scale with accountability.”
The promise and risk of agentic AI adoption
Nachreiner agreed that caution is needed. “Adding agentic AI to the equation will increase SOC speed to respond, but it also means new infrastructure like APIs and connectors become potential attack surfaces. Organizations should adopt quickly at least for proof of concept projects, but they must also pay close attention to how AI agents connect to privileged systems and watch for vulnerabilities in the supporting technology.”
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