Secure ML Pipeline (MLOps Security)

Implementing secure CI/CD workflows for ML models: validation, signing, secrets management, and provenance.

Marcos Martín
Secure ML Pipeline

This project applies DevSecOps principles to machine learning lifecycles. It ensures model integrity and reproducibility through secure build pipelines, signed artifacts, and automated validation of datasets, models, and configurations.

Tech Stack

  • GitLab CI / GitHub Actions · Docker · DVC · MLflow
  • Hash-based model signing and reproducible environments
  • Secrets management with HashiCorp Vault

Example (Model Signing in CI)


# sign model after successful training
MODEL_HASH=$(sha256sum model.pkl | awk '{print $1}')
echo "Model hash: $MODEL_HASH"
gpg --output model.sig --sign model.pkl

Pipeline Highlights

  • Data Validation: schema checks and anomaly detection at ingestion stage.
  • Model Integrity: signing and hash verification for provenance.
  • Secrets Management: secure storage via Vault and short-lived tokens.
  • Reproducible Builds: version-pinned Docker images and MLflow tracking.

Artifacts