VPC Service Controls can help you mitigate the risk of data exfiltration from Vertex AI. Use VPC Service Controls to create a service perimeter that protects the resources and data that you specify. For example, when you use VPC Service Controls to protect Vertex AI, the following artifacts cannot leave your service perimeter:
- Training data for an AutoML model or custom model
- Models that you created
- Requests for online predictions
- Results from a batch prediction request
Service perimeter creation
When you create a service perimeter, include Vertex AI
aiplatform.googleapis.com) as a protected service. You aren't required to
include any additional services for Vertex AI to function. However,
Vertex AI won't be able to reach resources outside the perimeter,
such as files in a Cloud Storage bucket that is outside the perimeter.
For more information about creating a service perimeter, see Creating a service perimeter in the VPC Service Controls documentation.
The following limitations apply when you use VPC Service Controls:
- Preview features are under VPC Service Controls Preview launch stage.
- For data labeling, you must add labelers' IP addresses to an access level.
- Requests to the following components are denied:
- Vertex Explainable AI
- Vertex Feature Store
- Vertex TensorBoard