Intermediate

Access Control

Implement role-based access control across your ML infrastructure, from data lakes and model registries to training clusters and inference endpoints.

RBAC for ML Systems

ML systems require fine-grained roles that reflect the different responsibilities in the ML lifecycle:

Role	Data Access	Training	Model Registry	Deployment
Data Engineer	Read/Write	None	None	None
ML Engineer	Read	Execute	Read/Write	Read
ML Reviewer	Read (sampled)	Read logs	Approve/Reject	None
ML Ops	None	None	Read	Deploy/Rollback
Security Auditor	Read metadata	Read logs	Read all	Read logs

Model Registry Permissions

The model registry is a critical control point. It determines which models can be deployed to production:

YAML - Model Registry Policy

# model-registry-policy.yaml
policies:
  model_registration:
    allowed_roles: ["ml-engineer"]
    required_metadata:
      - training_data_hash
      - training_config_hash
      - evaluation_metrics
      - security_scan_results

  model_approval:
    allowed_roles: ["ml-reviewer", "ml-lead"]
    required_checks:
      - robustness_test_passed
      - backdoor_scan_clean
      - bias_audit_completed
      - minimum_two_reviewers

  model_deployment:
    allowed_roles: ["ml-ops"]
    prerequisites:
      - model_approved
      - signature_verified
      - deployment_target_approved

Principle of Least Privilege

Apply least privilege at every layer of your ML stack:

Data access: Grant access to specific datasets, not entire data lakes. Use column-level and row-level security for sensitive data.
Compute access: Training jobs should have time-limited credentials that expire when the job completes.
Model access: Inference services should only load specific approved model versions, not browse the entire registry.
API access: External consumers should have scoped API keys that restrict which models, endpoints, and operations they can access.

Authentication Patterns

Service-to-Service

Use mTLS or service mesh authentication between pipeline components. Each service has its own identity certificate for mutual authentication.

Human Access

Require SSO with MFA for all human access to ML infrastructure. Use short-lived tokens rather than long-lived credentials.

API Consumers

Issue scoped API keys with rate limits, IP restrictions, and usage quotas. Rotate keys regularly and revoke on any security concern.

⚠

Common mistake: Many teams give ML engineers broad administrative access "to move fast." This creates risk of accidental or malicious model deployment, data exposure, and audit trail gaps. Implement RBAC from the start; retroactive implementation is much harder.