How H2O Enforces AI Governance and Compliance
- Observability
- H2O MLOps
- Enterprise h2oGPTe
- H2O Driverless AI
This lesson walks through how H2O's platform enforces governance through role based access control at the workspace level, so data scientists, ML engineers, validators, business users, and admins each see only what their role permits, backed by Kubernetes RBAC. It covers monotonicity constraints, which bake regulatory logic into how a model relates features to its target, and governance metadata tags that flag a model's risk level to trigger approval steps. The lesson also covers VPC and airgapped deployments, audit logging, and automated guardrails that block non compliant generative AI outputs.
Key takeaways
- How role based access control enforced through Kubernetes RBAC limits what each user role can see or do
- What monotonicity constraints do by encoding a required feature relationship directly into model training
- Why governance metadata tags on a model can trigger extra approval steps for high risk use cases
- How automated guardrails enforce content safety and bias policies before a generative AI output reaches a user
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