Cybersecurity | Wallaroo
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© 2019 by Wallaroo Labs, Inc.
Made with  in NYC.

Cybersecurity

Cybersecurity use cases come in many forms, but almost surely involve large volumes of data and a need to mointor data, apply algorithms to highlight or identify risks or anomolies based on context in real-time.  

We can help.

Example Use Case

Authorization & Authentication

A common use-case in offices, factories, hospitals, and businesses of all kinds, is making sure that all the devices that are connected to a network are properly authorized to do so.

 

Organizations need to be able to respond quickly and intelligently to the detection of any anomaly, be it the detection of device failure, unauthorized access, or suspicious communications. 

Challenge

There are three main technical challenges to overcome.

1. Volume of real-time data to be collected and processed. A medium size facility can have thousands of devices, and generate huge amounts of data to analyze. Often there is a combination of data types, some of this can be audio, video, which can require compute-intensive workloads.

 

2. Need both data science to develop models and analysis and real-time anomaly detection. While different technologies and frameworks can be used to attack both these tasks, that approach causes extra complexity.

3. Everything can't be in the cloud. Due to privacy, security, latency, and cost issues, it often doesn't make sense to ship all the data to the cloud for processing. An ideal set up allows for pre-processing at the facility, with additional analysis in the cloud, including analytics that combines data across a client's numerous facilities.

How Wallaroo Helps

Wallaroo provided easy solutions to all of these challenges

 

Wallaroo allows for complex application workflows and data pipelines that can combine different sources of data to build a complete picture of what's going on.

 

Wallaroo can easily scale to accommodate increasing sources and volumes of data, and the same framework can be used for python data science analysis, for real-time detection, and for processing both at the edge and in the cloud. That means reduced complexity,  lower costs, and much better scalability.