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Keysight Rolls Out Software to Validate Safety-Critical AI
Keysight Rolls Out Software to Validate Safety-Critical AI
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Keysight Rolls Out Software to Validate Safety-Critical AI

The solution answers a core question around how AI systems make decisions and whether they behave safely once deployed.

Published Updated

Key takeaways

  • Keysight launches AI Software Integrity Builder to validate AI in safety-critical applications.
  • The software addresses regulatory scrutiny and enhances trustworthy AI deployment across its lifecycle.
  • It supports real-world inference testing and ongoing monitoring to detect performance issues.
  • The tool aids in generating evidence for regulatory compliance and ensuring safe AI behavior.
  • It analyzes data quality to identify bias and recommends improvements for future AI model iterations.
5 min

Keysight Technologies has launched AI Software Integrity Builder, a software solution to validate and maintain AI-enabled systems in safety-critical environments, including automotive.

The company said the tool addresses rising regulatory scrutiny, complex AI development cycles and the need for trustworthy deployment across the AI lifecycle. The solution supports AI development, real-world inference testing and ongoing monitoring to detect data drift and performance issues. 

Keysight said the launch aims to help engineering teams shift from isolated testing methods to a unified AI assurance strategy, particularly for high-risk applications such as autonomous driving.

Thomas Goetzl, VP and GM of automotive and energy solutions at Keysight, said, “Standards and regulatory frameworks define the objectives, but not the path to achieving a reliable and trustworthy AI deployment.” He added that the company combines test and measurement expertise with AI validation to support safety evidence and regulatory alignment.

It helps engineering teams generate evidence of regulatory conformance and ensure safe behaviour during deployment, as standards such as ISO/PAS 8800 and the EU AI Act demand explainability and validation.

The company said the software analyses data quality to identify bias and gaps, explains model decisions to uncover hidden correlations and tests inference behaviour under real-world conditions. It also recommends improvements for future model iterations, essentially answering a core engineering question around how AI systems make decisions and whether they behave safely once deployed.

The solution also spans dataset analysis, model validation, inference-based testing and continuous monitoring. As per Keysight, this approach allows teams to diagnose dataset and model limitations while tracking how models perform in operational settings.

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