As the development of AI tools accelerates, organizations are under increasing pressure to move models from prototype to production securely and with scalability.
Behind the scenes, managing AI models is fraught with opaque processes, missing metadata, conflicting dependencies, and untraceable artifacts.
That is why 73% of organizations report lacking full confidence in their ability to track and secure AI components within their software pipeline, according to our 2025 Software Supply Chain State of the Union.
JFrog VP and CTO MLOps.
This is a fundamental problem that needs solving, not just for compliance and security, but also for reliability and speed.
To address these challenges, a new open standard has emerged: Model Context Protocol (MCP). MCPs aim to bring clarity, control, and confidence to the often-chaotic world of AI model development.
What is MCP and why does it matter?
AI models do not operate in isolation, they are the product of complex pipelines involving data, code, dependencies, and human decision-making.
Today, however, most organizations treat the final model as a black box, with little visibility into how it was built or whether it can be trusted.
That’s where MCP comes in. It’s an open, vendor-neutral standard designed to capture the full picture behind an AI model, not just the model itself.
From the data and code that shape it to the environment it runs in, MCP packages everything into a signed, digital file that computers can easily digest, ensuring traceability, integrity, and reproducibility across the model’s lifecycle.
In other words, MCP brings the same rigor and transparency we expect from modern software supply chains into the world of AI. As pressure mounts around responsible AI, from regulators, customers, and the public, that shift isn’t just helpful, it’s essential.
Why does MCP matter right now?
The adoption of AI in production environments is exploding, and existing tooling has failed to keep up with the specific needs of machine learning (ML) and large language model (LLM) workflows.
Traditional DevOps pipelines weren’t built to track ephemeral experiments, scattered datasets, or the dozens of packages and GPU drivers that ML workloads depend on. This creates significant blind spots.
In fact, according to our report, 94% of organizations use open-source software in their production environments, including in AI workloads, but nearly 60% admit they lack confidence in their ability to track the origin and security of AI-related packages and models.
That’s where MCP steps in. By offering a consistent, secure, and automation-ready protocol for model management, MCP makes it possible to reproduce results by capturing the full runtime context, scale deployments across cloud, on-prem, or edge environments, and enforce compliance through integrated license checks and vulnerability scanning.
It also streamlines collaboration across teams by serving as a single source of trust for all model-related assets.
By embedding AI workflows into existing software supply chain practices, MCP enables teams to reduce risk, accelerate delivery, and build AI applications that are as trustworthy as they are powerful.
In short, it's a foundational step toward making AI models first-class citizens in enterprise software development.
What is the role of MCP in the software supply chain?
There is a growing realization that models are more than just code, they're the new binaries.
Like software packages, models are compiled outputs from upstream work: datasets, configurations, dependencies, and training logic. Yet in many organizations, they remain loosely managed and disconnected from standard DevOps and MLOps pipelines.
To change that, forward-thinking teams are starting to treat AI models with the same discipline as any other software artifact.
This means capturing the full context of the model lifecycle; where it came from, how it was built, what it depends on, and how it should be deployed or governed.
That’s the aim of MCP. By defining a consistent structure for tracking and sharing model components, MCP helps close the gap between experimental AI work and scalable, secure deployment.
It supports integrations with popular open-source frameworks like MLflow, Hugging Face, and LangChain, allowing developers to work in familiar environments while layering in the governance needed for production use.
Organizations adopting MCP are now able to:
- Ingest and manage models from diverse tools and training platforms
- Apply policy-based governance, including license compliance and vulnerability scanning
- Track provenance, version history, and dependencies with confidence
- Distribute models securely across cloud, on-prem, or edge environments through unified pipelines
How does MCP help move AI from experimentation to enterprise?
Most AI projects today lack the basic hygiene that software teams take for granted: clear provenance, dependency tracking, version control, and security checks. This gap makes it nearly impossible to reproduce results, manage risk, or respond to regulatory pressure.
MCP offers a way forward by bringing structured, automated traceability to AI models, from the base model and training data to the runtime environment and security status, borrowing from decades of best practice in the software supply chain.
That means fewer unknowns when models are reused, updated, or audited, and more confidence when deploying AI into customer-facing or regulated environments.
By integrating seamlessly with existing CI/CD and development workflows, MCP does not just promise responsible AI, it makes it practical.
What’s next for MCP?
MCP is still under active development, with ongoing improvements to its specification and tooling. The protocol is supported by multiple open-source AI communities and is increasingly seen as the foundation for secure, reproducible, and enterprise-ready AI.
As organizations work to deploy AI systems responsibly, MCP provides a structured way to track and manage models, drawing on established best practices from the software supply chain, like version control, provenance tracking, and security checks.
Because just like with traditional software, AI models without context are a liability. With MCP, they can become secure, manageable assets, ready for production.
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