AI is already being used inside safety-critical systems, whether teams are comfortable admitting it or not. Driver assistance functions, predictive maintenance, and perception pipelines increasingly depend on learned behavior. What has not kept pace is the ability to explain, with confidence, how those systems behave once they are deployed.
For engineers working under functional safety and regulatory constraints, that gap is becoming uncomfortable. Traditional verification methods assume deterministic software. AI systems are not deterministic in the same way. They respond to data, shift as conditions change, and can expose failure modes that are difficult to reproduce after the fact.
Keysight Technologies is addressing that specific pressure with the release of AI Software Integrity Builder. Rather than positioning AI validation as a one-time exercise during development, the focus is on what happens after a model leaves the lab and starts interacting with the real world.
When Validation Ends Too Early
Most AI validation effort still happens before deployment. Training data is reviewed, performance metrics are checked, and models are signed off against controlled test scenarios. That process works well enough until conditions change. Sensors age. Input distributions shift. Edge cases appear that were never present in the original dataset.
When behavior drifts in a safety-critical system, engineers need more than accuracy numbers. They need to understand what the model is responding to and whether those responses still fall within acceptable bounds. That is where many current workflows fall short.
Regulatory frameworks are beginning to reflect this reality. Standards define what must be demonstrated in terms of safety and explainability, but they offer little guidance on how teams should maintain that evidence over time. In practice, this leaves engineers stitching together tools and hoping the resulting picture is coherent enough to defend.
Treating AI Behavior as an Engineering Signal
Keysight’s approach treats AI behavior as something that can be measured and observed continuously, rather than trusted once and left alone. Dataset analysis, model inspection, and inference behavior are linked together so engineers can see how decisions are formed and how they change in deployment.
The emphasis is not on optimizing models, but on understanding them. By looking at how inference behaves under real operating conditions, teams can identify deviation early and decide whether it represents acceptable variation or emerging risk.
That distinction matters in safety-critical environments, where the question is rarely “does it work?” and more often “can we explain why it behaved that way?”
A Shift in How AI Is Managed
The broader implication is subtle but important. AI in safety-critical systems is no longer just a development challenge. It is a lifecycle problem. Tools that stop at release leave too much unanswered once systems are in the field.
For engineers responsible for long-term safety and compliance, the value here is not another validation step. It is the ability to treat AI systems as observable, evolving parts of the product, rather than opaque components that are only understood in hindsight.
Learn more and read the original announcement at www.keysight.com
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