Published by Jessica Weisman-Pitts
Posted on August 3, 2023
6 min readLast updated: February 1, 2026
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Automakers are currently faced with the challenge of developing an engaging in-car experience that surpasses their rivals with a focus on rapid innovation. At its core, this innovation is primarily about connectivity through the use of sophisticated and advanced data and algorithms.
Automakers are currently faced with the challenge of developing an engaging in-car experience that surpasses their rivals with a focus on rapid innovation. At its core, this innovation is primarily about connectivity through the use of sophisticated and advanced data and algorithms.
As cars generate significantly more data every day, 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 taken into account.
Owing to the vast size and breadth of the automotive industry, there are numerous OEMs involved in the manufacturing of connected vehicles as well as a wide variety of models from each OEM. Further, with more than 100 different sensors present 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 a variety of 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.
With each additional connection, embedded or telematics system, and data-collecting sensor, protecting vehicles from cyber threats becomes increasingly challenging. The software and technology industries have had a lot to learn in the past 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 cyber security 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 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 be a threat across several aspects of the car. 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 that are 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 that will be connected to the outside world via cloud. They could produce terabytes of data every day, 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 risks and potential costs of a breach. Professionals in the mobility industry need to partner with the right experts who have a thorough understanding of machine learning to become skilled in designing secure smart vehicle solutions.
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 make sure 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, that not only lay the foundation for improved data, and in turn 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 as well as established players to market great ideas more quickly. The entire automotive industry is accelerating its capabilities as they keep 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. A variety of 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.
About The Author: Sumit Chauhan is the co-founder and chief operating officer at CerebrumX, with more than 24 years of experience in automotive, IoT, telecoms and healthcare industries. Sumit has always played the leadership role that allowed him to manage a P&L of close to US $0.5B across various organizations, such as Aricent, Nokia and Harman, enriching their domestic as well as international business verticals. As co-founder of CerebrumX, he has applied his experience in the connected vehicle data domain to deliver the automotive industry with an AI-powered augmented deep learning platform (ADLP). Sumit is also passionate about mentoring and guiding the next generation of entrepreneurs.
Data privacy refers to the proper handling, processing, and storage of personal information. It ensures that individuals have control over their data and how it is used.
Cybersecurity is the practice of protecting systems, networks, and programs from digital attacks. It involves implementing measures to safeguard data from unauthorized access or damage.
Connected vehicles are automobiles equipped with internet connectivity and various sensors that allow them to communicate with other devices and systems, enhancing safety and user experience.
Machine learning is a subset of artificial intelligence that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention.
Edge computing refers to processing data near the source of data generation rather than relying on a centralized data center. This approach reduces latency and bandwidth use.
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