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The data available in the network traffic fromany Government buildingcontains a significant amount ofinformation. An analysis of the traffic canyield insightsand situational understanding about what is happening inthebuilding. However, the use of traditional network packet inspection, eitherdeep or shallow, is useful for only a limited understanding of the environment,with applicability limited to some aspects of network and security management.If weuse AI/ML based techniques to understand the network traffic, we can gainsignificant insights which increase our situational awareness of what ishappening in the environment.At IBM, we have created a system which uses acombination of network domain knowledge and machine learning techniques toconvert network traffic into actionable insights about the on premiseenvironment. These insights include characterization of the communicatingdevices, discovering unauthorized devices that may violate policy requirements,identifying hidden components and vulnerability points, detecting leakage ofsensitive information, and identifying the presence of people and devices.Inthis paper, we will describe the overall design of this system, the majoruse-cases that have been identified for it, and the lessons learnt whendeploying this system for some of those use-cases

Using AI/ML to gain situational understanding from passive network observations