SafeRide Technologies: Fixing the Real Safety and Security Threat to Connected Vehicles

Along the trails of technology evolution and its penetration across various industries, threats and vulnerabilities arise for which defense methods are developed in a catch-up to patch-up fashion. Given the maturity and critical role of the transportation industry in managing physical assets, hidden vulnerabilities are death valleys. While there simply cannot be any room for security loopholes in the connected vehicle industry, the truth is, most threats are still unknown. The conventional security solutions that are built on the principles of protection against known threats only give a partial answer, leaving the automotive businesses at risk. “The background to founding SafeRide was the vision to provide a holistic security solution that is based on the 20 years of experience we gathered in the cybersecurity and AI domains,” says Yossi Vardi, CEO and Co- Founder at SafeRide. Israel-based SafeRide Technologies is one among those entities sworn-in to secure the emerging realms of connected transportation. The company’s team comprising experienced cybersecurity pioneers has positioned SafeRide on the high ground to edge out security mishaps.

SafeRide’s vSentry^TM suite offering adopts a three-pronged approach to provide holistic cybersecurity architecture for vehicle threat prevention and corresponding discovery of vulnerabilities and anomalies. The vSentry^TM In-Vehicle module—which can be installed on a Telematics Control Unit or other connected ECU—is purpose-built for monitoring vehicle data sets for detection and prevention against known ‘rules-based’ threats. At the core of vSentry^TM suite is vXRay^TM the AI-powered engine for anomaly detection. vXRay™ has been built to perfection by using thousands of hours of recorded data, across different commercial vehicle models in diverse driving conditions. Building upon the data from the in-vehicle layer, vXRay^TM is capable of delivering monitoring and analytics of application behavior to impart bird’s eye visibility into vehicle operations. The data collected from the vehicles goes through different data processing methods including principle component analysis, dimension reduction, and clustering, to profile the internal states of the vehicle.