Beyond the Hype: Building a Future-Proof Foundation for the AI-Native Enterprise

We are witnessing a fundamental transformation in how software is built. The industry has moved beyond the experimental phase of Machine Learning Operations and entered a complex new reality: the era of the AI Software Supply Chain.

The adoption metrics confirm this shift is irreversible. Google reports that 90% of tech workers are now using AI as part of their daily work. Similarly, McKinsey data reveals that 88% of organizations use AI in at least one business function.

However, this rapid adoption has created a dangerous paradox. While the pressure to integrate and adopt AI is immense, the mechanisms to control it are largely absent. Organizations are rushing to innovate, but they are doing so on a fragmented foundation that lacks a system of record.

To truly future-proof your business, you cannot simply chase these trends, you must control them. You need to stop treating AI as a siloed experiment and start managing it with the same rigor, security, and governance as your traditional software supply chain.

The Challenges of AI Adoption

The primary symptom of this fragmented landscape is Shadow AI, the uncontrolled use of unmanaged models and servers that creates critical blind spots across the enterprise.

This risk manifests in three distinct ways:

• The Governance Gap: In the absence of established protocols, developers often bypass security teams to get the job done. IBM research shows that 7 out of 10 personnel use AI through personal accounts, and 4 out of 10 admit to entering sensitive information into these unmanaged assistants. With 63% of companies lacking effective AI governance policies, this behavior creates massive compliance exposure.
• The Security Threat: AI artifacts are not static files; they are executable code that can be compromised. Attackers have pivoted to target this new supply chain. Between March 2024 and March 2025, while the total number of models on Hugging Face grew 3X, the number of malicious models grew 7X.
• The Visibility Blind Spot: You cannot govern what you cannot see. While IT teams may manage a fraction (35%) of AI capabilities, the vast majority of usage happens in the dark. Models, data, and binaries are scattered across different systems, making it impossible to answer basic questions like “Where is this model running?” or “Who approved its use?”.

The Strategy: 5 Pillars for Trusted AI

The solution to these challenges isn’t blocking usage by shutting down access to external hubs like Hugging Face. That kind of approach would be unsustainable in the long run. Instead, the solution lies in creating a trusted foundation throughout the AI adoption process.

The following 5-pillar strategy bridges the gap between innovation and control, while extending trusted supply chain practices to cover the entire AI lifecycle.

1. Consolidate the Toolchain

Stop managing AI in a silo. You can eliminate the friction of fragmented tools by establishing a single system of record for both code and models. By utilizing a central registry that can proxy public repositories (like Hugging Face) and host internal models alongside your software binaries, you break down the silos between data scientists and developers.

2. Detect Hidden Assets

A robust strategy requires active detection. Many organizations already host AI artifacts, such as Python packages calling external APIs or Docker images containing models, within their repositories without realizing it. You must actively scan your existing infrastructure to identify these “hidden” AI models and unmanaged assets, turning Shadow AI into a visible, manageable inventory.

3. Centralize Governance

Once assets are visible, they must be governed. This requires a single control plane to enforce policy across the organization. You need the ability to define granular permissions: allowing a specific “Llama” model for Research projects while blocking it for Production until it passes specific security gates. This ensures that every model used is pre-approved and compliant.

4. Reduce Risk with an AI-BOM

To trust a model, you must understand its lineage. We advocate for the generation of an AI Bill of Materials (AI-BOM). This creates a tamper-proof metadata trail that links your model back to its origin, training data, and security scan results. This traceability is essential for root cause analysis and proving compliance to auditors.

5. Simplify the Path to Production

If the secure path is too difficult, developers will find a workaround. Simplify access by providing a centralized, self-service hub. Whether a developer needs to download a model package or call a model via an API, the platform should abstract the complexity of infrastructure and credential management, enabling one-click deployment and secure connections.

The Single System of Record for Trusted AI: JFrog AI Catalog

To operationalize these pillars, we introduced the JFrog AI Catalog. It acts as the centralized hub for all your AI/ML initiatives, allowing you to Discover, Detect, Govern, and Serve models from a single location.

• Curated Discovery: It provides a unified view of millions of models from Hugging Face and commercial providers (such as Anthropic, Gemini, OpenAI, etc.), allowing you to curate exactly which models are visible and allowed for your teams.
• Integrated Security: By integrating with JFrog Xray, the catalog automatically scans models for malicious code (such as pickle serialization attacks) and license violations, blocking threats before they enter your pipeline.
• Unified Deployment: It bridges the “last mile” problem by treating models like any other artifact, allowing you to deploy them to your infrastructure and generate secure tokens for inference with a single click.

Conclusion

The shift in software delivery is here, and it’s driven by AI . But AI freedom does not (and should not) have to mean losing control.

By unifying your AI development with the governed software supply chain you already trust, you can transform AI from a chaotic risk into a scalable, secure business advantage.

Ready to bring your models out of the shadows and into the light? Book a demo with our AI experts to learn more about the JFrog AI Catalog and start building your future-proof AI foundation today.