Vertex AI inherits Google Cloud’s IAM for fine-grained access controls and integrates with network security features to restrict data movement. You can scope permissions to projects, models, endpoints, datasets, and pipeline runs, ensuring that only authorized principals can read or deploy artifacts. For network boundaries, VPC Service Controls and Private Service Connect help keep data flows inside defined perimeters, reducing the risk of exfiltration. Customer-managed encryption keys (CMEK) allow you to control encryption at rest for sensitive artifacts.
Operational security includes audit logging for who did what and when, signed container images from Artifact Registry, and binary authorization patterns for approving images before deployment. Model endpoints can be placed behind internal load balancers and private IPs, and request authentication/authorization is enforced via IAM. You can define quotas and rate limits to prevent abuse, and use organization policies to restrict regions or accelerator types for compliance reasons. For data privacy, training jobs and endpoints run in your project boundary under your keys.
In vector-centric architectures, align security across Vertex AI and Milvus. Store only necessary metadata in the vector store; use attribute-based filtering to enforce tenant and permission boundaries at query time; and place Milvus behind private networking with service-to-service authentication. When generating embeddings, strip sensitive fields before vectorization, and consider hashing or tokenizing IDs. Log access to both endpoints and vector collections for audits. This cohesive approach ensures the retrieval layer doesn’t become a side door, and that your ML stack meets enterprise security expectations.