description Vertex AI Security Features Overview
Vertex AI is Google Cloud's machine learning platform that provides tools for building, deploying, and scaling AI models. Its security features include data encryption at rest and in transit, identity and access management controls, and virtual private cloud (VPC) network isolation. The platform complies with various industry standards and regulations, including HIPAA, GDPR, and SOC 2, making it suitable for enterprise applications with strict data protection requirements. These security measures are designed to protect sensitive data and models throughout the machine learning lifecycle, from development to production deployment.
help Vertex AI Security Features FAQ
Does Google use Vertex AI customer prompts to train shared models?
Google Cloud's enterprise terms and Vertex AI data-governance documentation govern how customer data is handled, and prompts are not simply public training material. Organizations should still verify the terms for the exact model, grounding service, region, and preview feature they enable.
Can Vertex AI resources be isolated from the public internet?
Vertex AI supports controls including VPC Service Controls and private networking patterns for supported services. VPC Service Controls adds a context-based perimeter beyond IAM and is intended to reduce data-exfiltration risk.
Can an organization use its own encryption keys with Vertex AI?
Supported Vertex AI features can use customer-managed encryption keys through Cloud KMS. CMEK availability varies by model and operation, so Google's feature-specific security matrix must be checked before designing a compliance boundary.
How is access to Vertex AI models and agents controlled?
Google Cloud IAM roles and service accounts control who or what can invoke, deploy, or administer resources. Audit Logs, Access Transparency where supported, and least-privilege service accounts provide additional evidence and control around enterprise access.
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