Is edge analytics the front line of cybersecurity?

The sooner an enterprise can shut down a security problem, the better. But that depends on the availability of actionable, up-to-date information to inform the security team as they essentially race against the clock to stop the bad guys. 

That has hastened a rethink of how to more effectively sift through the massive streams of information generated nowadays to make better, and faster, decisions. One idea gaining currency is to shift the front lines of cybersecurity closer to where data is actually generated by sensors, controllers and other network devices. 

Shifting to the edge

The concept extends the idea of edge computing to data gathering and analysis. Instead of analyzing information generated by sensors at a central computing station, data would be sent through sensors and devices where security is already built in. 

The advantage is that analytics would get performed closer to the devices that actually generated the data. In theory, that allows IT to more quickly understand what’s happening with their organization’s assets and better gauge evolving threats to carry out predictive maintenance or detect security anomalies in real time. 

The emergence of Big Data as well as the spread of the Internet of Things creates myriad scenarios where the deployment of analytics algorithms on the edge can reduce response times and help security managers swarm more rapidly in case of breaches or malfunctions. Indeed, edge analytics has been described as a veritable antidote to the coming data deluge. 

Take the example of an industrial network where there might be thousands of small sensors placed throughout a production line. Manufacturing organizations that move analytics to the edge of their networks can measure and correlate information generated by those sensors in real time and alert managers to potential security or failure conditions before they spread out of control. 

Edge analytics en route 

Organizations also reap scalability benefits by lightening the load on existing enterprise data management and analytics systems. This can be especially important in situations where there’s no time to waste waiting for sensor systems to transmit data to a remote cloud. Take the question of train safety. 

General Electric’s Transportation’s Evolution Series Tier 4 Locomotive is one of the company’s most modern trains. Each locomotive, which features more than 200 sensors processing over one billion instructions per second, makes use of on-board edge computing to analyze data and apply algorithms to run more safely. 

With trains generating more data than ever, organizations need to choose what data matters most to improve performance and avoid failures. In the case of the Tier 4, the faster reaction time at the network edge can make a big difference by ensuring that what is really relevant is what gets transferred.  

Edge analytics is obviously not going to appeal to every organization. But companies that are able to effect the change and put automated, intelligent analytics at the edge will have a powerful tool to deploy against malicious hackers in the years ahead. 


Charles Cooper has covered technology and business for the past three decades. All opinions expressed are his own. AT&T has sponsored this blog post. 

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