Endpoints security can mitigate cyber attacks
TEAMWORK The manufacturing industry is good at machine learning and deep learning, and if you combine enough of those two processes, you could get to AI
As a result of cyberattacks increasing rapidly in terms of their severity and complexity in the manufacturing industry, companies have to look into manufacturing endpoint-security as a line of defence, says cybersecurity solutions provider Kaspersky enterprise cybersecurity adviser Lehan van den Heever.
Endpoint security is the practice of securing endpoints or entry points of end-user devices such as desktops, laptops and mobile devices from being exploited by malicious actors and campaigns.
Endpoint protection systems are designed to quickly detect, analyse, block and contain attacks in progress. There are various types of endpoint security such as Internet of Things (IoT) security, antivirus solutions, application and network access control, uniform resource locator filtering and browser isolation.
He says cyberattacks have become mainstream “because of their increase in popularity”, and as these attacks evolve, their devastation has become worse.
He mentions that manufacturing companies face the challenge of civil and electrical engineers not wanting to put anything into the operating systems that may cause any kind of disruption to processes, as cybersecurity solutions have a reputation of disrupting operating systems.
Despite the risks that cyberattacks pose on intellectual property in the manufacturing industry, Van den Heever highlights that there are advantages that automation, machine learning and artificial intelligence (AI) provide for manufacturers in this regard.
The advantages include repeat processes in the manufacturing process being done by machines as opposed to people, which “will not only eliminate human error and reduce cyberattack risks but also increase the speed of the production process”, adds Van den Heever.
However, such processes will not happen overnight – it is a gradual transition over a few years, he notes.
“The manufacturing industry is good at machine learning and deep learning, and if you combine enough of those two processes, you could get to AI; however, that is still a long way away.”
Although many benefits for the business can be perceived as a downside for humans, Van den Heever says he is “quite pro machine learning and AI” because “every time humans are pushed into a corner, we tend to survive and find new ways of using our skills”.
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