How do you design clustering systems with governance and policy enforcement layers?

Governance layers enforce rules on data usage, feature selection, and model deployment in clustering pipelines.

In enterprise ML platforms, governance ensures that clustering models comply with business, legal, and ethical constraints. This includes restricting sensitive attributes, enforcing feature whitelists, and validating model outputs before deployment. Policy engines sit above the pipeline and block or modify jobs that violate constraints. Audit logs track every decision for compliance purposes.