Across your organization, teams are rapidly adopting AI and machine learning. They’re pulling ML models and datasets from public sources like Hugging Face and wiring them into workflows that are now reaching production. For platform and security leaders, this creates a familiar challenge: artifacts are entering the software supply chain outside established governance and controls.

Unmanaged AI artifacts carry real risks. They can’t be traced back to their source, they don’t follow compliance processes, and they often bypass the review gates you rely on for code and containers. Left unchecked, they become blind spots and liabilities in your supply chain. Without action, AI artifacts become the weakest link in your supply chain security.

The answer is to manage ML models and datasets with the same level of control and oversight as every other artifact in your supply chain. By managing them through a unified platform—with shared repositories, consistent policy enforcement, and full observability—you ensure that ML workflows are governed by the same standards as the rest of your software supply chain.

The risks of unmanaged ML artifacts

The growing adoption of ML models and datasets introduces both opportunity and risk. On one hand, models accelerate delivery by enabling new capabilities at unprecedented speed. On the other hand, they introduce opaque dependencies, security blind spots, and compliance risks into the supply chain—areas where governance practices are still catching up.

Some risks begin earlier in the lifecycle, like poisoned training data or tampered weights during model creation, which can compromise a model before it ever reaches a registry. But the most immediate challenge for platform and security leaders is governance once these artifacts enter the supply chain. Teams often pull models directly from public sources like Hugging Face, bypassing review and scanning. Without versioning and provenance tracking, it’s nearly impossible to prove which model is in use, where it came from, or what depends on it. And without consistent policies, organizations struggle to demonstrate compliance when auditors or regulators come calling.

These risks can’t be left unmanaged. The question is how to bring ML artifacts under control.

Supply Chain Controls For ML Models

ML models and datasets are artifacts, and like any artifact, they need to be versioned, audited, and controlled with the same enterprise-grade policies you’ve put in place for packages like code libraries and container images. As organizations deliver production applications powered by AI, the priority for platform and security leaders is clear: maintain visibility, integrity, and trust in the components that make up these builds.

This is where Cloudsmith’s new ML Model registry support comes in.

A Single Source of Truth for ML Models in DevOps

Cloudsmith extends artifact management to ML models and datasets, promoting them to first-class artifacts in your supply chain. This means you can control, secure, and distribute models alongside your language libraries, containers, and other binaries, all governed by the same policies and observability already in place.

With Cloudsmith, you can:

• Enforce policy before production: Require approvals, license checks, and compliance gates before a model or dataset is deployed.
• Guarantee integrity: Every artifact is versioned, hashed, and immutable, so tampering can be detected and blocked.
• Trace provenance: Capture where a model or dataset came from, how it has changed, and what depends on it.
• Unify formats: Manage ML models (PyTorch, TensorFlow, ONNX, and more) alongside other binary artifacts in the same repositories.
• Integrate with Hugging Face: Proxy and cache models and datasets directly from the Hugging Face Hub, while continuing to use the Hugging Face SDK for training and deployment — ensuring developers keep their workflows while you enforce governance.

ML Models + Governance = DevOps Win

DevOps has always been about balancing efficiency, stability, and security. The rise of ML workflows doesn’t change that balance - it heightens the need for it. By incorporating ML models and datasets into Cloudsmith, you extend the same supply chain controls that protect your code and containers to the AI artifacts now driving production systems.

The result: faster innovation without unmanaged risk, and a single platform to govern every component that ships to production.

Learn more about Cloudsmith's ML Model Registry here.