Connecting Evidence across the Lifecycle for Automotive AI

Artificial Intelligence (AI) is rapidly becoming a foundational technology in modern Software-Defined Vehicles (SDV). From perception and decision-making to driver assistance and automation, AI is increasingly responsible for functions that influence vehicle behavior and safety. As a result, organizations face a critical challenge: how can confidence in AI-enabled systems be justified beyond individual test results?

Traditional AI validation activities provide important evidence. Engineers evaluate datasets, validate models, assess robustness, analyze explainability, and perform system-level testing. Each of these activities generates valuable insights into the behavior and performance of an AI system. However, successfully completing individual validation activities does not automatically establish trust in the overall system.

Trust requires more than isolated validation results. It depends on understanding how evidence generated throughout the lifecycle contributes to broader claims about safety, reliability, performance, and intended AI behavior. As AI systems evolve through development, verification, deployment, and operation, large amounts of evidence are created across different teams, tools, and engineering disciplines. Without clear relationships between these results, stakeholders may struggle to understand how individual findings support confidence in the system as a whole.

This challenge has contributed to the emergence of AI assurance as a complementary discipline to AI validation. While validation activities produce evidence, assurance focuses on building confidence by demonstrating how that evidence supports trust-related claims. Establishing justified trust requires not only generating evidence but also maintaining traceability, context, and relationships between evidence sources throughout the system lifecycle.

Keysight AI Software Integrity Builder addresses this challenge through a connected evidence approach. The solution helps organizations connect validation results generated across datasets, models, functions, systems, and operational environments. By transforming disconnected validation results into connected evidence, engineering teams can better understand how individual validation activities contribute to larger assurance objectives and overall system confidence.

Keysight’s lifecycle-driven AI Assurance framework enables organizations to maintain visibility across evidence sources, identify gaps in assurance reasoning, and establish stronger links between validation activities and trust claims. Rather than focusing solely on the outcome of individual tests, connected evidence provides the foundation for understanding why a system should be trusted and how confidence can be maintained as the system evolves over time.

This video explains how Keysight AI Software Integrity Builder helps organizations transform validation evidence into a structured foundation for trustworthy AI throughout the lifecycle.