Is your data living on the edge? Let’s talk Edge Computing.

February 22, 2018 Amy Gregory, Product Marketing Director

man on the edge of a rock with nothing below

There’s a new buzzword making the rounds (like we needed another one). Move over Digital Transformation, and welcome Edge Computing. While it’s being credited with the power to deliver speed, security and cost-savings to organizations, is it really all that? Let’s break it down…

What Is Edge Computing?

Research firm IDC defines edge computing as a “mesh network of microdata centers that process or store critical data locally and push all received data to a central data center or cloud storage repository, in a footprint of less than 100 square feet.” In other words, it involves processing data at the edge of the network where it is created, instead of routing it through data centers or clouds.

Fog Computing vs. Edge Computing

Another term related to edge computing is fog computing. It’s important to understand the distinction.

“Fog” refers to the network of connections between edge devices and the cloud, while “edge” represents what happens near or on the edge devices (the endpoints). Fog computing includes edge but uses the network to get edge data where it’s going.

What’s Driving Edge Computing?

More organizations are adopting large-scale IoT deployments with Gartner estimating enterprise use of IoT devices will grow to 7.5 billion by 2020. Transporting data to the cloud from IoT devices for management and analysis is costly, inefficient and can impact latency.

Many organizations simply don’t have the connectivity to support sending large amounts of data to and from the cloud. With its on-device approach, edge computing addresses these limitations by performing the computing and analytics on the device itself — eliminating the need for data transport.

Edge Computing Advantages and Challenges

Edge computing gives applications that rely on machine learning an advantage by increasing data process efficiencies. By doing away with the need for device-to-cloud data trips, on-device machine learning makes applications more responsive and robust. Machine-learning-driven capabilities like facial profile recognition or voice recognition is quicker and more efficient when done on-device.

By maintaining sensitive data at the source rather than sending it to the cloud, edge computing also offers security advantages. The less data in various locations, the fewer the opportunities for cybercriminals to access it and compromise it. Countering this is the argument that the vulnerability of edge devices to compromise is a risk to data kept at the edge.

The takeaway here is that mitigating IoT security risks requires work, planning and vigilance. A good IoT strategy includes a robust plan for keeping your system secure.

According to the Hewlett Packard Enterprise study, The Internet of Things: Today and Tomorrow, eighty-four percent of IoT adopters say they have experienced at least one IoT security breach with malware, spyware and human error the most common culprits. Ninety-three percent of executives expect IoT security breaches to occur in the future.

How do you build an IoT strategy that drives the speed to market you desire but simultaneously keeps your data safe? Simple – an upfront strategy that takes into considerations the inherent risks is a must. At OneNeck, our security team is ready to help you assess your security risk and work with you to develop a realistic strategy to keep you secure and get the most from your data on the edge.

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