One of the things that makes connected vehicle cybersecurity particularly challenging is scale. If a researcher or attacker discovers a remotely exploitable vulnerability in software running on a particular vehicle model, that flaw potentially exists in thousands of...
Key takeaways AI in the automotive industry now sits in three distinct places: inside the product, inside the development pipeline, and inside the decisions engineers make. Each carries a different risk profile. Established, narrow-scope AI, such as driver monitoring,...
Key takeaways Automotive compliance is usually captured as a snapshot at release or audit time, but resilience is a culture sustained across the full lifecycle and into incident response. Traceability tends to break first because it feels like paperwork, and the cost...
Key Takeaways The real challenge in autonomy has shifted from building impressive prototypes to proving software-defined vehicles are safe and secure in the real world. Centralized vehicle architectures boost capability but create new systemic risks that demand...
Connected vehicles are getting smarter by the model year, but also noisier, more exposed, and harder to secure. Cars include dozens of computers and sensors, plus cameras, microphones, and wireless connections that constantly observe what is happening inside and...
Key Takeaways: The U.S. Connected Vehicle Rule focuses on software origin and provenance Open-source software is largely exempt, while commercial and proprietary components are in scope SCA tools lack the visibility needed for Connected Vehicle compliance Software...
