Automakers are currently faced with the challenge of developing an engaging in-vehicle experience that surpasses their rivals with a focus on rapid innovation. At its core, this innovation is primarily about connectivity through sophisticated and advanced data and algorithms. 

As light-duty and medium-duty vehicles generate significantly more data daily, and with the advent of high-speed 5G communication, in-vehicle edge computing has become critical to ensure that connected vehicles function at scale to provide quicker and improved performance.

As a result, vehicles can harness data from multiple OEMs and sources scattered in the ecosystem, to be utilized by OEMs, insurers, fleet companies, and smart cities/municipalities. However, all this connectivity also means that security and data precautions must be considered. 

Connected Vehicles Have a Wealth of Data yet to Be Unlocked

Owing to the vast size and breadth of the automotive industry, numerous OEMs are involved in manufacturing connected vehicles and medium-duty trucks as well as a wide variety of models from each OEM.

Further, with more than 100 sensors inside each vehicle, tracking everything from driver behavior, vehicle performance, and component lifecycle, the scope and scale of data sources truly becomes exponential.

And while all this data can come in handy for various applications, it can be challenging to keep secure. It could become very difficult to enforce conventional cyber security standards with such a complicated automotive supply chain. 

Protecting vehicles from cyber threats becomes increasingly challenging with each additional connection, embedded or telematics system, and data-collecting sensor. The software and technology industries have had a lot to learn when it comes to implementing stringent security norms. 

Modern cars have great promise in leveraging all this data, and it is possible that more reliable and regulated cybersecurity protocols may be implemented in the production of vehicles in the near future. 

Some vehicles may be even more susceptible to these threats because they are built with even more data possibilities in mind. In response to a global movement toward decarbonization, ACE mobility (autonomous, connected, and electric) has been growing in popularity. While this is good news for the environment, it can raise new issues with data privacy and security.

An average ICE (internal combustion engine) vehicle today has about 100 million lines of code. With more autonomous and electric vehicles expected on our roads in the future, there will be a steep rise in the number of lines of code for ACE vehicles, and consequently in the scope for security issues.

Electric Vehicles Are Designed With Data in Mind

Electric vehicles gain from in-vehicle systems that can assess and enhance energy output, battery usage, and charging capabilities, in addition to the safety features that improve each year.

Since software controls most safety features, the powertrain, and even the battery functionality, cyber security flaws may threaten several aspects of the vehicle. A big open target for cyber predators is the vehicle itself, which is more connected than ever, more dependent on technology than ever, and more anticipated by the world than ever. It also has an internal network of components connected to the outside world.

But such vehicles will be more connected than ever, more dependent on technology than ever, and more anticipated by the world than ever. They will also house an internal network of components and sensors connected to the outside world via the cloud.

They could produce terabytes of data daily, which could be analyzed to identify usage patterns and applied on a larger scale to make cities and infrastructures safer and more effective. 

In this setting, machine learning (ML) also performs exceptionally well. ML-driven solutions give analysts the ability to identify the connections between events over time and across various hosts, users, and networks by giving them a broad understanding of the activity surrounding the assets they oversee.

Contextual information can be provided by ML to lower the risks and potential costs of a breach. Professionals in the mobility industry need to partner with experts with a thorough understanding of machine learning to become skilled in designing secure smart vehicle solutions.

What’s Possible to Help Prevent Incidents

While it is impossible to foresee every cybercriminal attack scenario, it is obvious that data privacy will be a key concern. The lives and privacy of the people it serves must be protected, and policymakers must ensure that the system governing the next generation of transportation does so.

There is a heightened need for all nations to adopt data privacy and security norms, which not only lay the foundation for improved data and vehicle security, but also make way for safer innovations.

In fact, once the security of connected vehicles is standardized, doors open for the standardization of many extra features within cars. This makes it much simpler and quicker for exciting innovation to spread throughout the entire industry.

It's possible that start-ups and smaller businesses won't always have access to the data and technology needed to support their innovative concepts for next-gen, sustainable automotive solutions.

In these circumstances, platforms with adaptable, secure technology to support such players offer opportunities for larger industry players to share data insights.

This will shorten and de-risk innovation, enabling both start-ups and established players to market great ideas more quickly. The automotive industry is accelerating its capabilities as it keeps developing and utilizing data platforms, open-source software, and cloud providers.

Vehicles are still machines, even with added intelligence. And any interference could undermine security. Various opportunities also become available when the proper systems are in place to protect user data. The potential of smart vehicles can only be fully realized once the security of connected vehicles and the corresponding data has been effectively addressed.