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I learned to code in BASIC on a Commodore 64 in the early 1980s. BASIC was actually a pretty neat high-level abstraction of assembly language, which itself was an abstraction sitting on top of machine code! Since then, I’ve seen a steady stream of higher-level abstractions - relational databases, object-oriented languages, package dependencies, open source libraries, cloud-native development, containers, serverless - that have made software developers more productive, and helped us build ever more powerful applications with them.

AI is the latest abstraction layer that sits on top of existing technology, making the underlying components more powerful and easier to use. A huge amount of R&D and startup capital in the past few years has gone towards exploiting this new abstraction.

The newest and best ideas naturally attract top talent and funding. Companies and startups building cool new AI-powered apps have their choice of tech stack. And many of them are creating their new software development tool chains around Cloudsmith, which is very satisfying to Cloudsmithers.

, who built an AI-powered platform pathologists use to diagnose cancers. They manage binary packages in Cloudsmith that they use to build for a wide variety of target environments. Or 

, who builds industrial computer vision applications that improve quality and productivity, using Cloudsmith to manage binary artifacts that go into their build pipelines. 

 provides a machine learning platform for document processing and automation, built on Cloudsmith for internal development and external distribution of containers and packages. And 

, who have built an AI-powered due diligence platform, using Cloudsmith as their single source of truth for binary artifacts.

Companies that build critical infrastructure for use by other AI companies are also gravitating to Cloudsmith. Take 

, who helps organizations use, manage, and govern AI metadata with their enterprise-grade context platform. They chose Cloudsmith to make sure their artifacts are reliably and securely distributed to enterprise developers. Or 

, who distributes their SDKs and runtimes via Cloudsmith for delivery of AI-driven perception solutions to robotics and computer vision tool companies. Then there’s 

, an open source AI search platform used to develop and run large-scale applications, combining big data, vector search, machine-learned ranking, and real-time inference to enable AI applications like retrieval-augmented generation, recommendation, and intelligent search at enterprise scale. Cloudsmith supports distribution for Vespa, streamlining delivery for their open source community as well as enterprise users.

And then it’s companies who deliver AI directly to the hands of end users. 

 calls itself “the world’s most private AI,” assisting users in real time across a wide range of personal tasks. They use Cloudsmith to build and distribute packages across the software supply chain.

Cloudsmith is best known for our enterprise-grade artifact management platform, which controls and secures the software supply chain for the world’s most demanding Fortune 500 and Global 2000 organizations. But there’s a reason we also show up so often as the platform of choice for innovative AI startups and spinoffs. These companies are typically run by lean teams of seasoned software engineers. They know what they need, and they want to do it right the first time. They want developer-friendly, well-documented tools that just work.

 to manage their Hugging Face models and datasets, and we’re increasingly powering our own development of new features with AI tools. But it’s also notable that the world’s most interesting AI companies are also choosing Cloudsmith - not just to manage ML models, but to power their entire software supply chain. That’s a vote of confidence that makes us proud to be Cloudsmithers.

Glenn Weinstein

The platform of choice for AI companies

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 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.

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.

Cloudsmith’s new ML Model registry support

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.

: Require approvals, license checks, and compliance gates before a model or dataset is deployed.

: Every artifact is versioned, hashed, and immutable, so tampering can be detected and blocked.

: Capture where a model or dataset came from, how it has changed, and what depends on it.

: Manage ML models (PyTorch, TensorFlow, ONNX, and more) alongside other binary artifacts in the same repositories.

: 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.

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.

Alison Sickelka

VP of Product

Extending Supply Chain Governance to AI and ML Artifacts

Exploring AI and the future of the software supply chain

AI assistants will shape the future of the software supply chain, and today, we’re sharing a glimpse of a powerful idea in motion: 

 server that connects large language models (LLMs) like ChatGPT and Claude directly to your software supply chain using the emerging 

Imagine managing policies, surfacing security insights, or orchestrating package workflows with a simple natural language prompt. No dashboards, no scripting, just a simple question or instruction that delivers the right end result. If this sounds interesting, you should 

This isn’t a launched product (yet), but it’s a clear signal of where we think Cloudsmith is headed: toward 

 that make it easier to control your software supply chain.

is a standard way to make information available to LLMs. Similar to an API, MCP provides a documented, standardized way for a client to integrate services from external sources.

MCP enables AI assistants to query and interact with software supply chain data, so it creates a foundation for smarter workflows and more accessible control over artifact management.

In essence, adding MCP capabilities to Cloudsmith offers up an entirely new interface to your software supply chain, with AI agents and LLMs working together to achieve your goals. We're exited to see MCP becoming a flexible new point of integration between our customers' third-party services and Cloudsmith.

MCP server demo

Our MCP prototype leverages Cloudsmith’s API-first architecture to provide programmatic access to developers through their AI assistant. Through standardized MCP requests, you can ask questions about your software supply chain or take safe, controlled actions - like managing policies or promoting packages - directly within Cloudsmith. This opens up conversational workflows with the Cloudsmith platform.

For example, you might ask your AI assistant to identify what package is trending up in usage month over month or see all of the versions of a particular package in use across your workspace. In the future, your AI assistant will be able to review your organization’s compliance and security requirements and recommend or create a set of policies in Cloudsmith’s Enterprise Policy Manager based on that input.

Building this prototype was a good reminder of a few fundamentals. First, an API-first foundation makes it easier to experiment, so it’s good that Cloudsmith is built truly API-first. Second, the data running through platforms like Cloudsmith can offer valuable insights when it’s made accessible to clients in lots of different ways. We’re giving developers and AI assistants simpler ways to ask questions, take actions, and get things done.

There’s still plenty to figure out, but MCP is already making artifact management and software supply chain management more intuitive and straightforward, especially at enterprise scale.

 for select customers and partners. If you're exploring the intersection of DevOps, AI, and supply chain security, we’d love to work with you. 

Prototyping an MCP Server

As Generative AI (GenAI) reshapes the software development landscape, the risks and complexities around managing what gets built, where it comes from, and how it’s secured are growing just as fast. The 

Cloudsmith 2025 Artifact Management Report

 dives into this shift, offering critical insights into how teams are adapting their infrastructure and software supply chain security practices in response to the AI-generated code.

42% of developers using AI report that at least half of their codebase is now generated by AI tools

. Where in 2024 this might have been considered a theoretical shift, in 2025 we need to accept that this is not a practical reality reshaping how software is written. AI isn't just a helper on the sidelines; it’s actively contributing to the core of modern applications and transforming the day-to-day workflow of developers. While the rapid rise of GenAI is often seen as a leap forward in productivity and innovation, it also introduces new security risks and software supply chain vulnerabilities that can’t be ignored.

Attackers Are Targeting the Software Supply Chain

Adversaries are increasingly shifting their focus from traditional infrastructure attacks to targeting the software supply chain itself. Rather than breaching networks directly, they’re infiltrating the ecosystem through techniques like 

 (the creation of malicious packages with names deliberately designed to mimic legitimate ones).

These deceptive packages are easy to overlook, especially in fast-paced development environments. Developers, often under tight deadlines and relying heavily on AI-assisted tooling to manage dependencies, may unknowingly pull in these malicious components without thorough vetting. The result is a stealthy but highly effective method for attackers to introduce vulnerabilities directly into production code.

AI and automation: a double-edged innovation

Survey respondents anticipate that GenAI-driven automation will bring both benefits and complications, which should accelerate development workflows while also straining infrastructure with unpredictable behaviors and dependency complexities. Tools like 

 promise greater speed and efficiency in coding, but they also raise new red flags for security teams. As automation becomes more deeply embedded in the development process, the potential for AI-generated code to introduce unknown risks or unstable dependencies grows, which challenges the long-held assumptions about trust and reliability in the software pipeline.

These concerns are further amplified by the widespread adoption of Large Language Model (LLM)-based technologies. While LLMs can significantly boost developer productivity, they also pose new threats (such as recommending non-existent or harmful packages). When asked whether AI would worsen open-source 

 risks like typosquatting or dependency confusion, nearly 

a third warning of a significant rise in exposure

These evolving development patterns are expanding an already vulnerable software supply chain. Without rigorous artifact validation and integrity checks, developers face growing risks - not just from external attackers, but also from the very tools designed to help software development teams move faster.

To address these emerging threats, modern artifact management solutions must embed intelligent access controls and offer end-to-end visibility into artifact provenance. With dynamic access control policies, organizations can ensure only authorized users and processes can interact with sensitive assets, reducing the likelihood of unauthorized changes or malware insertion. Coupled with robust policy-as-code frameworks, enterprises can establish and enforce security protocols that adapt as threats evolve, especially in environments where AI-generated code is becoming commonplace.

Where in the SDLC are AI-generated risks coming from?

While the speed and efficiency benefits of using AI in software development are clear, the oversight is not.

Only 67% of developers surveyed review AI-generated code before every deployment

, leaving large portions of production code potentially unvetted. This is quickly becoming a growing vulnerability in the software supply chain. This behaviour, driven by a desire to move faster, is dramatically expanding the attack surface. AI isn’t just introducing new code, it’s also introducing new risks, often at scale. Traditional concerns like artifact integrity, dependency management, and 

 (Software Bill of Materials) are being compounded by AI’s ability to rapidly consume and reuse unknown or untrusted code.

41% of respondents identified code generation as the riskiest point of AI influence

, yet practices around review and trust remain inconsistent:

66% said they only trust AI-generated code after manual review.

Just 20% fully trust AI output without extra scrutiny.

59% apply additional reviews to AI-generated packages, but only 16% treat them like any other package, without further checks.

The Need for Secure Artifact Management in the AI Era

This uneven approach is happening against a backdrop of expanding AI usage: alarmingly, 

86% of organizations have seen more AI-influenced dependencies in the last year, with 40% seeing a significant increase

29% of teams feel very confident in their ability to detect malicious code in open-source libraries

, which is the very ecosystem where AI tooling tends to source its suggestions. The threats, according to those surveyed, range from sensitive data leakage to the inclusion of compromised dependencies. These risks are amplified when developers blindly trust AI-generated code or package suggestions without security oversight.

These findings point to a critical inflection point. AI is becoming a core contributor to the software stack, but we haven’t fully adapted our trust models, tooling, or policies to match this new reality. Relying on developers to manually spot every risk (especially under time pressure) is not sustainable.

What’s needed is a secure checkpoint for AI-assisted development:

Automatically-enforced policies to catch unreviewed or untrusted AI-generated artifacts.

Artifact provenance tracking to distinguish between human-authored and AI-authored code.

Integration of trust signals directly into the development pipeline, so that reviews become automatic rather than an optional decision.

Download the 2025 Cloudsmith Artifact Management report

Explore the latest trends, insights, and best practices for securing your software supply chain and managing AI-generated code responsibly.

Cloudsmith Blog

The platform of choice for AI companies

Extending Supply Chain Governance to AI and ML Artifacts

Prototyping an MCP Server

AI is now writing code at scale - but who’s checking it?