Proactive Automotive Threat Intelligence and Risk Mitigation

Features

• Real-time monitoring and analysis of the latest threats
• Identification of vulnerabilities in in-vehicle and connected components, tackling the problem of unknown vulnerabilities.
• Immediate actions to counter identified threats, reducing response time to incidents.
• Collaboration with law enforcement to neutralize threats, mitigating the impact of sophisticated threat actors.
• Helping companies keep up with evolving laws.

API Security Monitoring and Mitigation

Features

• Continuous monitoring for suspicious activity, addressing the issue of increased API attacks.
• Identification and mitigation of API-related threats, reducing data breach incidents.
• Protects integration with cloud services, IoT devices, and mobile applications, solving integration vulnerabilities.
• Real-time response to detected threats, minimizing operational disruptions.

Automotive Cybersecurity Detection and Response (V-XDR)

Features

• Uses machine learning to identify anomalies, addressing the problem of undetected sophisticated threats.
• Combines several layers of security, providing comprehensive protection across complex vehicle systems.
• Easy deployment without in-vehicle agents, solving resource constraints for smaller companies.
• Works with in-vehicle sensors, telematics, FOTA, and IT/OT systems, ensuring seamless security integration.

Vehicle Telematics and Sensor Data Security

Features

• Telematics Data Monitoring: Continuous monitoring for anomalies, addressing data integrity issues.
• Sensor Data Security: Protects data integrity from vehicle sensors, reducing risks of tampering.
• Secure Transmission: Ensures secure data transmission, preventing unauthorized access.
• Anomaly Detection: Identifies suspicious activities or data patterns, mitigating operational risks.

Fleet and Application Security Optimization

Features

• Fleet Monitoring: Continuous monitoring of connected vehicle fleets, addressing operational inefficiencies.

• Application Security: Ensures security of connected applications, reducing vulnerabilities.

• ML-Powered Anomaly Detection: Utilizes machine learning to detect performance and security issues, facilitating data-driven decision-making.