This guide focuses on securing the full AI/ML lifecycle in Vertex AI using a defense-in-depth approach. It emphasizes strict IAM controls with least privilege, isolating workloads using private networking (VPC Service Controls, private endpoints), and protecting sensitive data with encryption, segmentation, and de-identification. It also highlights securing ML pipelines by scanning containers, validating inputs, and enforcing policy constraints to prevent supply chain attacks. Runtime testing like adversarial red teaming and monitoring for anomalies are also key. The takeaway is that AI security must cover data, models, pipelines, and infrastructure holistically under the shared responsibility model.

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