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Governance

Governance with Model Registry, IAM, and AWS Config

Establish accountable ML controls from development to production.

What this covers

Apply this playbook to establish an auditable model registry, scoped IAM personas, and AWS Config guardrails that keep evidence flowing automatically.

Implementation trail

  • Model registry structure
  • Least-privilege personas
  • Automated compliance evidence
  • Approval workflows
  • Operational onboarding

Anchor decisions in the Model Registry

  • Create a Model Package Group per product domain so risk, product, and engineering teams review the same asset catalogue.
  • Require metadata fields for dataset version, approval ticket, and rollback plan before a package advances stages.
  • Mirror registry events to collaboration hubs (Slack, Jira) so sign-offs and deployment readiness stay synchronized.

Codify least-privilege IAM personas

  • Issue a registrar role limited to creating and updating packages, while a separate auditor role only reads artifacts.
  • Add a deployment role that pipelines assume when promoting approved packages so separation of duties remains intact.
  • Enforce MFA or workload identity federation before roles can promote models into production stages.

Automate compliance with AWS Config

  • Record all supported resource types to capture IAM, SageMaker, and S3 configuration drifts automatically.
  • Use managed Config rules (e.g., S3 encryption enabled) plus the custom registry tag rule to enforce risk-tier metadata.
  • Route non-compliant findings to the governance queue with remediation playbooks and expected resolution SLAs.

Connect CloudFormation resources to responsibilities

Deploy governance-registry-config.yaml and walk stakeholders through the highlights so they understand how the template enforces accountability.

  • Resources:
      ArtifactBucket:
        Type: AWS::S3::Bucket

    Stores versioned model packages and Config snapshots with encryption, lifecycle policies, and public access blocks baked in.

  • Resources:
      ModelRegistry:
        Type: AWS::SageMaker::ModelPackageGroup

    Creates the canonical approval surface that all lifecycle events reference.

  • Resources:
      ModelRegistrarRole:
        Type: AWS::IAM::Role

    Embeds the registrar persona with just enough privilege to advance packages while preserving separation of duties.

  • Resources:
      ModelDeploymentRole:
        Type: AWS::IAM::Role

    Illustrates the dedicated pipeline role that reads approved packages and promotes them into endpoints without registrar powers.

  • Resources:
      RegistryTagRule:
        Type: AWS::Config::ConfigRule

    Highlights the custom AWS Config rule backed by Lambda that rejects untagged models before they reach production.

Need a governance launchpad?

We establish model registries, IAM boundaries, and compliance automation so your next audit begins with complete evidence instead of manual hunting.

Kick off your control plane