ISO 26262 and ASIL implications when AI participates in simulation

When AI participates in a safety-adjacent simulation workflow, the first question is not whether the model is clever. The first question is what evidence the workflow now relies on, and whether that evidence remains traceable to the engineering decision being made.

ISO 26262 and ASIL language can pull discussion toward formal process quickly, but the practical distinction starts earlier. Teams need to separate productivity assistance from assistance that influences safety argumentation, requirements confidence, verification planning, or release decisions.

A summarizer that helps an engineer browse a run log is different from a system that recommends closing a requirement, changing test coverage, or accepting a simulation result. The closer the tool gets to safety-relevant judgment, the more the team has to inspect its role in the evidence chain.

Traceability is the central review surface. What inputs were read, what assumptions were used, what outputs were generated, who reviewed them, what was retained, and how could a later reviewer reconstruct the path from artifact to decision?

Tool confidence also depends on failure visibility. If an AI layer silently drops context, overstates certainty, confuses run families, or invents a rationale, the workflow needs a way to catch that failure before it becomes part of the engineering record.

The strongest evaluation posture is to define boundaries before the demo. Which tasks are advisory only? Which outputs may enter retained evidence? What human review is required? What artifacts are logged? What happens when the tool cannot cite support?

AI can still be useful in safety-adjacent work, but usefulness is not the same as authority. The system earns trust by making review easier, lineage clearer, and uncertainty more visible while keeping safety responsibility with the engineering organization.