A zero-trust MLOps architecture enforces authentication, least privilege and continuous monitoring using Microsoft Entra ID, Azure Key Vault, and Private Link. Metadata in Azure SQL Database drives automated security configuration through tables for access policies, secret references, network rules, and audit logs. Access policies map roles to precise permissions for pipeline operations, secret references centralize credentials in Key Vault, network rules establish Private Link and firewall restrictions, and audit logs record activity for compliance. The design emphasizes encryption, network isolation, metadata-driven orchestration, and persistent auditing to protect sensitive data, models, and inference workloads.
Zero-trust means trusting nothing by default every user, service and data flow has to prove itself. For MLOps, where sensitive data and proprietary models are in play, this is non-negotiable. I built the architecture around three principles: Verify everything. Authenticate every access with Microsoft Entra ID. Keep permissions tight. Use metadata to assign only what's needed. Assume the worst. Encrypt data, isolate networks and monitor relentlessly.
I added new tables to handle the security setup: Access_Policies. Defines who gets what access say, data scientists running inference or analysts viewing outputs. Secret_References. Points to Azure Key Vault for credentials and tokens, keeping sensitive data out of scripts. Network_Rules. Sets up Private Link endpoints and firewall rules for services like Databricks. Audit_Logs: Tracks every action for compliance and auditing.
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