Integrating Machine Learning (ML) into safety-critical automotive systems pre-sents new challenges. As automotive technologies evolve, standards like ISO 26262, which regulate the functional safety of electrical and electronic sys-tems in vehicles, must be updated to address the complexities of modern ML sys-tems. Drawing on a recent peer-reviewed IEEE Access publication co-authored with Emil Gracic and Gregor Pawelke, Padma Iyenghar outlines a systematic ap-proach to enhancing ISO 26262 by introducing ML-specific life cycle phases and testing methods to ensure the safety and reliability of ML-driven systems.