  The data available in the network traffic fromany Government building
contains a significant amount ofinformation. An analysis of the traffic can
yield insightsand situational understanding about what is happening inthe
building. However, the use of traditional network packet inspection, either
deep 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 gain
significant insights which increase our situational awareness of what is
happening in the environment.At IBM, we have created a system which uses a
combination of network domain knowledge and machine learning techniques to
convert network traffic into actionable insights about the on premise
environment. These insights include characterization of the communicating
devices, discovering unauthorized devices that may violate policy requirements,
identifying hidden components and vulnerability points, detecting leakage of
sensitive information, and identifying the presence of people and devices.In
this paper, we will describe the overall design of this system, the major
use-cases that have been identified for it, and the lessons learnt when
deploying this system for some of those use-cases
