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This research addresses the expanding security risks of agentic AI by proposing a novel framework to audit network traffic and ensure agent alignment with user intent. The approach leverages complex-valued hypersparse matrices alongside specialized database and graph libraries to create clear records of autonomous agent interactions within virtual environments. For practitioners, this work highlights critical infrastructure needs for monitoring and securing the growing ecosystem of multi-agent systems.
Agentic Network Traffic Monitoring
arXiv:2609.32778v1 Announce Type: new Abstract: As the use of agentic artificial intelligence increases in nearly every industry, there exists a widening attack surface. It is necessary to monitor agents to ensure that agents are acting in a way that is aligned with the users intent. Auditing an agent's network traffic provides a clear record of the agent interactions. This work presents a novel approach to monitoring the network traffic of agentic systems using complex valued hypersparse traffic matrices by integrating DBOS (DataBase OS), the OneSparse PostgreSQL database, and the GraphBLAS math library. To develop these concepts an agentic simulator was constructed, allowing a varying numbers of AI agents to collectively survey a virtual environment using different strategies. The resulting network traffic matrices enable easy monitoring of the AI agents.
Abstract:As the use of agentic artificial intelligence increases in nearly every industry, there exists a widening attack surface. It is necessary to monitor agents to ensure that agents are acting in a way that is aligned with the users intent. Auditing an agent's network traffic provides a clear record of the agent interactions. This work presents a novel approach to monitoring the network traffic of agentic systems using complex valued hypersparse traffic matrices by integrating DBOS (DataBase OS), the OneSparse PostgreSQL database, and the GraphBLAS math library. To develop these concepts an agentic simulator was constructed, allowing a varying numbers of AI agents to collectively survey a virtual environment using different strategies. The resulting network traffic matrices enable easy monitoring of the AI agents.
Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Databases (cs.DB); Distributed, Parallel, and Cluster Computing (cs.DC); Networking and Internet Architecture (cs.NI)
arXiv:2609.32778 [cs.AI]
(or arXiv:2609.32778v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.32778 arXiv-issued DOI via DataCite (pending registration)
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