Securing Automotive AI Systems Through Proactive Red Teaming
A Framework for Verifiable Trust in Safety-Critical Machine Learning
As machine learning becomes integral to vehicle safety systems, traditional validation methods fall short. ADAS and autonomous driving functions require new assurance strategies that address both ML model vulnerabilities and emerging adversarial threats.
The risk? Hidden ML failures that traditional testing can't detect, discovered only after deployment.
The solution: Proactive red teaming integrated into your development lifecycle.
Beyond Traditional Validation.
This white paper presents Critical Software's framework for permanent adversarial testing in automotive AI. You'll learn how leading manufacturers are shifting from reactive validation to continuous security assurance.
What makes this approach different
Uncovers edge cases before real-world exposure
Addresses SOTIF requirements through systematic adversarial analysis
Aligns with UNECE NATM (New Assessment/Test Method) standards
Embeds security as a continuous process, not a final gate
What's Inside This White Paper
Why automotive machine learning systems fail in ways traditional software doesn't
Common blind spots in perception, prediction, and decision-making models
How adversarial attacks exploit ML weaknesses in safety-critical contexts
The Proactive Red Teaming Framework
Systematic methods for disciplined adversarial testing throughout development
How to integrate SOTIF (Safety of the Intended Functionality) analysis with ML assurance
Implementing UNECE's New Assessment/Test Method for ongoing validation
Building a culture of continuous security assurance in automotive organizations
Practical Implementation
Critical Software's proven framework for verifiable AI trust
How to establish permanent red teams within automotive development workflows
Metrics and KPIs for measuring ML system resilience
Roadmap for shifting from compliance-driven to risk-driven AI assurance
Who Should Read This
Functional safety managers overseeing ADAS/AD development
ML engineers and data scientists working on automotive perception systems
Cybersecurity leads responsible for AI/ML system hardening
Systems architects designing software-defined vehicle platforms
Compliance officers navigating UNECE R155/R156 and ISO 21434 requirements