AI pipelines are remodeling how enterprises deal with knowledge, however they’re additionally prime targets for safety dangers. In “Designing a metadata-driven ETL framework with Azure ADF,” I confirmed how metadata can streamline knowledge integration with Azure Information Manufacturing unit (ADF). With AI now on the forefront, securing these pipelines — knowledge, fashions and all — is vital.
So, I got down to construct a zero-trust structure for MLOps in Azure, utilizing Microsoft Entra ID, Azure Key Vault and Personal Hyperlink, all orchestrated with metadata. This text walks by means of my strategy, the challenges I hit and what I discovered alongside the best way. Determine 1 exhibits the structure, laying out the way it retains AI workloads locked down.
Designing a zero-trust MLOps structure
Vikram Garg
The zero-trust mindset
Zero-trust means trusting nothing by default — each consumer, service and knowledge movement has to show itself. For MLOps, the place delicate knowledge and proprietary fashions are in play, that is non-negotiable. I constructed the structure round three ideas:
