CONCEPT Cited by 1 source

ML governance at scale

Definition

ML governance at scale is the architectural discipline of embedding compliance, access control, and data classification enforcement directly into the ML execution layer — rather than relying on separate approval workflows, manual reviews, or external policy-enforcement points that become bottlenecks as team count and workflow volume grow.

The key insight: governance that lives outside the execution path gets bypassed under velocity pressure. Governance embedded in the platform is the only governance that scales with the organization.

Multi-layer enforcement typically includes:

1. User identity layer — who can initiate/manage workflows
2. Domain layer — what data a workflow can access based on its declared purpose (experimentation vs. production)
3. Column/field layer — granular classification at the data element level with automatic propagation

Seen In

• sources/2026-06-10-atlassian-architecting-scalable-ml-platforms — ML Studio's three-layer compliance framework (user identity, domain-level, column-level) serving 100+ teams with 900k+ governed datasets; "without slowing down innovation"

(Source: sources/2026-06-10-atlassian-architecting-scalable-ml-platforms)