As we move toward production with our AI search, security is a major concern. How do we implement Role-Based Access Control in Milvus to protect our vector embeddings? We need to ensure that only authorized users can query specific collections containing sensitive organizational information.
3 answers
Security in Milvus has improved significantly with the introduction of native RBAC. You can now create users, roles, and permissions to restrict access at the collection level. This ensures that a developer working on a public dataset cannot access sensitive vectors in a private collection. Additionally, you should always enable TLS for data in transit and integrate with an external identity provider if possible. For data at rest, rely on the encryption features of your underlying storage provider, such as AWS S3 managed keys, to ensure that your embeddings are fully protected from unauthorized access.
Are you also looking into audit logging to track who is performing searches on your most sensitive vector collections?
The ability to whitelist specific IP ranges for the GRPC and REST endpoints provides an extra layer of perimeter security.
I totally agree; combining IP whitelisting with RBAC creates a very strong defense-in-depth strategy for any sensitive AI deployment.
Audit logging is a must for compliance. By piping the logs to a tool like ELK, you can monitor for unusual query patterns that might indicate a data scraping attempt or a security breach.