We want to integrate Gemini 1.5 Pro into our internal application using Vertex AI. However, we have strict compliance requirements regarding data privacy. How do we ensure our proprietary data used for tuning or RAG (Retrieval-Augmented Generation) isn't used by Google to train their foundation models, and what are the best security guardrails to implement?
3 answers
Google is very clear in their Vertex AI privacy commitment: your data is never used to train their global foundation models. To be safe, ensure you are using VPC Service Controls to create a security perimeter around your Vertex resources. For RAG, use Vertex AI Search and Conversation, which handles the grounding of data within your own tenant. You should also implement IAM roles strictly, giving "Vertex AI User" only to those who need it, and use Customer-Managed Encryption Keys (CMEK) for your underlying storage.
Are you planning to use Adapter Tuning or Distillation, and how are you managing your API keys to prevent unauthorized access to the model endpoints?
Check out the Model Garden in the console. It has pre-configured notebooks that show exactly how to set up private endpoints for Gemini.
The Model Garden is fantastic. It really simplifies the deployment of LLMs while keeping everything under your organization's administrative control.
Instead of API keys, Deborah's point about IAM is key—use Service Accounts for application-to-model communication. For the tuning part, start with Prompt Engineering and Vector Search before jumping into full model tuning. It’s often more secure and much cheaper. Vertex AI Vector Search is highly performant for keeping your proprietary context separate but accessible.