Your ML platform team maintains a shared Vertex AI Pipelines training pipeline. The training component runs in project 'ml-training-prod' but must read TFRecord data from a GCS bucket owned by the data engineering team in a separate project, 'de-datalake-prod', and write model artifacts back to a bucket in 'ml-training-prod'. During pipeline execution, the training step fails with a 403 error when accessing the source data. The pipeline runs under a custom service account 'pipeline-runner@ml-training-prod.iam.gserviceaccount.com'. What is the correct way to resolve this while following least-privilege principles?