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App Design

CloudPost Networks

An IoT Security Start-up in San Jose CA

Enterprise Software Security
Lead UX / UI Partner
Tools Used:
Sketch, Axure, Zeplin, Illustrator, Google Docs


CloudPost Networks enters the IoT Security sector focused on hospital IoT. With machine learning algorithms, CloudPost is able to build individual behaviour and communication profiles of smart devices ranging from security cameras and access points to MRI scanners and smart beds.

Deviations from normal device behaviours are flagged, egregious deviations are blocked and reported based on security policies.

All communications are monitored, tracked and recorded for extensive reporting.

Executive Summaries & Reporting

One of the best ways to prove ROI is for the product to provide a top-level reporting system that executives and decision makers can, at a glance, gain insight and take action.

A total number of devices are displayed with a risk breakdown of monitored devices into risk categories. Further breakdowns of devices by alarms and categories are also displayed. In an IoT attack, devices go through 5 stages of attack severity. A report of devices in all five steps are displayed.

Smart Device Behaviour Profiles

Each device is profiled and its communication patterns are studied through machine learning, A device specific profile is developed over time and individual profile segments are recorded and broken down. Over time, a complete profile is created. Devices deviating from their profile are flagged up to the user for examination.

The Profile Segments are displayed as a dynamic and animated spiral and can be traversed by a slide line or by Next and Back movements.

Tracking Communications

Groups of device types can naturally communicate with other groups, other intranet services and to the outside internet. Focusing on one group will display the natural communication trends involving all three categories.

The graph can be rotated to give the user a better view based on connections.

Individual IoT Device Communication

In order to deep dive into individual device communications, when devices are flagged, a timeline of communications segmented by risk and communication volume is displayed for further diagnostics.

Take a Short Tour

You can see the application at work and how beautiful the application is. And if you are interested in big data, you might even consider Xcalar for your big data analytics!