Keysight Technologies has unveiled AI Software Integrity Builder, a new solution aimed at helping organizations validate, deploy, and maintain reliable AI systems in safety-critical environments such as automotive and advanced mobility. Designed for high-risk use cases including autonomous driving, the platform supports engineering teams across the entire AI lifecycle—from early development and real-world inference testing to continuous monitoring in production. By bringing these capabilities together, Keysight seeks to unify AI assurance efforts and improve confidence in AI-driven systems where failures can have serious consequences.
As AI adoption accelerates in regulated industries, companies face growing pressure to demonstrate that their systems are not only performant but also trustworthy and compliant. Keysight positions AI Software Integrity Builder as a response to this challenge, providing a structured way to generate evidence that AI models behave safely and consistently under real-world conditions. The solution is built on the company’s long-standing expertise in test and measurement, extending those principles into the domain of artificial intelligence.
Thomas Goetzl, VP and GM of automotive and energy solutions at Keysight, underscored the gap the new software is intended to address, saying, “Standards and regulatory frameworks define the objectives, but not the path to achieving a reliable and trustworthy AI deployment.” He added that Keysight applies its testing heritage to help engineering teams validate AI behaviour, align with regulatory expectations, and document safe system performance across development and deployment stages.
At a technical level, AI Software Integrity Builder focuses on transparency, robustness, and ongoing oversight. The platform examines training and operational data to surface issues related to quality, bias, and coverage gaps that could undermine model reliability. It also provides explainability features that help teams understand how and why models make specific decisions, revealing hidden correlations that may introduce risk in safety-critical contexts.
Beyond development and validation, the solution emphasizes inference-based testing under realistic conditions, allowing teams to assess how AI models perform when exposed to real-world variability. Once deployed, continuous monitoring capabilities help detect performance degradation, data drift, and emerging anomalies over time. This lifecycle-wide approach enables organizations to diagnose limitations early and track AI behaviour as systems evolve.
With AI increasingly embedded in vehicles, energy systems, and other high-stakes applications, Keysight’s latest offering reflects a broader industry shift toward rigorous AI assurance. By integrating dataset analysis, model validation, real-world testing, and continuous monitoring into a single platform, AI Software Integrity Builder aims to help enterprises move from experimentation to dependable, production-ready AI.
