Secure software development for modern vehicles
Targeted software security practices can help overcome challenges in satisfying emerging cybersecurity standards in the automotive industry.
In the automotive industry today, software-defined vehicles (SDVs), electric vehicles (EVs), and connected and autonomous vehicles are becoming increasingly popular. As the development of vehicles with improved safety features, better operation, and enhanced user experience progresses, it is important to recognize that all of these advancements require more-advanced and complex software. And that increases the risk of vulnerabilities, which in turn increases the attack surface. Further, these vehicles contain valuable assets, making them more sought-after as targets.
Cybersecurity trends and standards
In recent years, the automotive industry has seen several new standards and regulations introduced, including ISO/SAE 21434 Cybersecurity engineering, Automotive SPICE for Cybersecurity, and UN-R155 Cybersecurity and Cybersecurity management system. As more organizations establish cybersecurity policies, processes, and activities for product development, there has been an increased maturity of cybersecurity in the industry.
To read the full article, click here
Related Semiconductor IP
- xSPI Multiple Bus Memory Controller
- MIPI CSI-2 IP
- PCIe Gen 7 Verification IP
- WIFI 2.4G/5G Low Power Wakeup Radio IP
- Radar IP
Related Blogs
- Easing software development for high-performance zonal controller based on Arm Cortex-R82AE
- Raspberry Pi Pico 2: Arm-based Development Board Delivers Higher, More Secure Performance for Commercial Applications
- Unlock early software development for custom RISC-V designs with faster simulation
- Synopsys Secures Connected Vehicles with Industry's First IP Product to Achieve Third-Party Certification for ISO/SAE 21434 Cybersecurity Standard
Latest Blogs
- The Growing Importance of PVT Monitoring for Silicon Lifecycle Management
- Unlock early software development for custom RISC-V designs with faster simulation
- HBM4 Boosts Memory Performance for AI Training
- Using AI to Accelerate Chip Design: Dynamic, Adaptive Flows
- Locking When Emulating Xtensa LX Multi-Core on a Xilinx FPGA