Fleet management software should adapt at the same rate as connected cars, Chevin says
09 August 2017
Author: Daniel Puddicombe
Chevin Fleet Solutions has claimed that future fleet software must be ready to respond to new technologies as smart vehicles "begin to flood the market".
The fleet management software company added that connected cars are "becoming increasingly popular among British drivers", and that it is important fleet software adapts at the same rate in order to support increased levels of data.
The company's statements are in response to the UK Government issuing new guidelines earlier this week, with the firm's managing director Ashley Sowerby saying that fleet software providers "will also need to step up a gear" in order to keep data safe.
According to the government, it is feared criminals could target smart vehicles to access personal information, steal cars that use keyless entry systems or take control of vehicles for malicious reasons.
It added the legislation, which is due to be put before parliament, "will put Britain at the centre of the new technological developments in smart and autonomous vehicles while ensuring safety and consumer protection remain at the heart of the industry."
The new guidance dictates that manufacturers will need to design-out cyber threats as part of new vehicle development work.
"There are many important factors businesses need to consider when managing their fleet information, the first of which should be whether their software system is adequately secure," Sowerby said. "Official accreditations formally acknowledge that providers are committed to data protection issues, and software companies should also be striving to achieve these accreditations if they don't already have them in place."
He added: "Hierarchical user permissions within a system are also essential in order to control who has access to certain levels of data. Drivers, for example, will not need access to the same level of intelligence as fleet managers, and authorisation should depend on job role versus data sensitivity."
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