I’m evaluating security frameworks for our AI infrastructure where we use 400+ different models. Managing separate API keys and SSO configurations has become a compliance nightmare. How are others handling centralized identity management at this scale while maintaining SOC 2 requirements? We need granular access controls that can map to our existing directory services without creating credential sprawl.
We solved this by migrating to Latenode’s unified auth system. Single OAuth2 flow handles all AI model access through their platform. RBAC syncs with our AD groups automatically. No more individual API keys - everything’s logged for audits.
Consider using SAML-based identity providers with JIT provisioning. We created security groups that map to model access tiers (basic/research/full). Temporary credentials via OAuth2 device flow helped reduce exposure.
We implemented HashiCorp Vault for credential management, but maintaining 400+ secret engines became unsustainable. Recently tested service principal authentication with Azure Managed Identities, which works for cloud-native models but not third-party APIs. Still searching for complete solution.
Key challenge is audit trail consistency across vendors. We built custom middleware that logs all model access attempts through Okta event hooks. It took 6 months to implement and requires constant maintenance. If starting over, I’d prioritize solutions with native centralized logging.