How AI Governance Reduces Risk in Software Supply Chain Security
7 minute read time
Key Takeaways:
- Securing the software supply chain now requires AI-specific controls, including visibility into models, datasets, and dependencies.
- Regulatory environments especially the EU AI Act are increasing pressure on organizations to demonstrate transparency and accountability in AI usage.
- AI Bills of Materials (AIBOMs) are emerging as a way to inventory models, datasets, and dependencies to improve transparency, compliance, and trust.
- Treat AI governance and compliance like modern software supply chain security. Adopt practices like tracking provenance, integrating security into CI/CD, and automating AI governance risk checks to build trustworthy systems.
Artificial intelligence (AI) is rapidly transforming software development, accelerating innovation, streamlining processes, and opening the door to entirely new capabilities.
But as with any transformative technology, AI introduces new risks. From opaque training data to compliance challenges, organizations now face an urgent question: How do we secure and govern AI in the software supply chain?
This blog explores why AI demands a new approach to software supply chain governance, outlining the unique risks it introduces and how organizations can apply proven security and practices to manage AI effectively.
What Is AI Governance in the Software Supply Chain?
AI governance in the software supply chain refers to the set of practices, policies, and controls that ensure AI systems are developed, deployed, and maintained in ways that are secure, transparent, and compliant from end to end. It builds on traditional software supply chain governance to address the unique requirements of AI systems, incorporating models, datasets, and associated tooling alongside traditional dependencies.
Effective AI governance helps organizations understand what AI assets are in use, where they originated, how they are behaving, and how risks are being managed throughout the lifecycle. At its core, AI governance means extending visibility and control over every model in the AI software supply chain.
What Are The Biggest AI Supply Chain Security Risks Right Now?
As AI adoption accelerates, key themes are emerging at the intersection of AI and software security, shaping the future of AI in supply chains and introducing new challenges in managing AI governance risks effectively. Understanding these risks is essential to securing the software supply chain.
Lack of Visibility Into AI Models
One of the most significant challenges in AI governance and compliance is simply knowing which models and datasets are in use and where they came from. Unlike typical software libraries where source and versioning are clearly tracked, AI models often lack clear provenance, making it difficult to assess trustworthiness, detect compromised assets, or understand legal and licensing obligations tied to their use.
Regulatory and Compliance Pressure
Global and domestic regulations, such as the EU AI Act, are rapidly evolving and placing new obligations on organizations to demonstrate transparency, accountability, and fairness in AI development. Compliance isn’t just about avoiding fines, it’s about demonstrating to customers, partners, and auditors that AI systems are governed with the same rigor as other critical software components.
AI Model Integrity Risks
As with any open source component, AI models are susceptible to poisoning, tampering, and other integrity attacks. When sourced from public repositories, these AI models can be manipulated to introduce malware and vulnerabilities into your code. These risks can remain undetected until the application is deployed in production, undermining trust and security.
Scaling AI Governance and Compliance
The speed and scale of AI experimentation exacerbate security challenges. Manual oversight doesn’t scale when teams are iterating on AI models and datasets at a faster velocity than working with traditional software. Leveraging automation will be essential to maintain control without slowing down progress.
Together, these forces highlight why organizations must act now to ensure that AI adoption is both secure and compliant, laying the groundwork for a future of innovation built on trust.
Why Are Open Source Models a Bigger Supply Chain Risk?
Developers have long relied on open source components that greatly assist with development velocity, but also introduce vulnerabilities, licensing complexities, and operational risks. AI models provide a similar breakthrough in software development, offering immense potential while introducing comparable risks.
What Makes Open Source AI Models Risky?
Open source AI models inherit the same challenges as traditional open source components, but with added layers of uncertainty:
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Data provenance: Unlike a logging library, AI models are trained on vast datasets that may contain bias, malicious data, or copyrighted material.
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Non-determinism: AI outputs aren't consistent — asking the same question twice may yield different answers. This unpredictability complicates risk assessment.
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Security vulnerabilities: Models and their supporting frameworks can be tampered with, just like any other dependency in the supply chain.
The bottom line? Organizations need the same level of rigor for AI models as traditional open source components, if not more.
Why Is AI Model Visibility the Hardest Governance Challenge?
One of the biggest obstacles organizations face is simply knowing what AI models are in use. Security teams often aren't involved in model development or deployment, leaving them in the dark about what models exist, where they came from, and what risks they introduce.
Provenance matters not only for security, but also for licensing obligations. Just like open source software, many models have usage restrictions that teams may overlook. Without visibility, AI governance and compliance gaps widen quickly.
The Regulatory Landscape: EU Leads, U.S. Follows
Governments worldwide are stepping in to shape AI governance in software supply chains. The European Union (EU) is leading the charge with the EU AI Act, the first comprehensive legislative framework for AI. It requires organizations to demonstrate transparency, fairness, and accountability in AI applications, especially when they’re used in critical decisions like hiring, lending, or healthcare.
For global organizations, this patchwork of requirements makes compliance even more complex. Whether you're building in the U.S. or abroad, if your AI models touch the EU market, you'll need to align with EU regulations.
To navigate this rapidly evolving regulatory environment, global organizations must invest in AI governance solutions that can scale alongside both innovation and compliance demands.
Enter the AI Bill of Materials (AIBOM)
One concept gaining traction as a solution is the AI Bill of Materials (AIBOM). Modeled on the software bill of materials (SBOM), an AIBOM provides a detailed inventory of an AI model's components, dependencies, datasets, and provenance. Its goal is simple but powerful: transparency.
How Does an AIBOM Reduce AI Supply Chain Risk?
An AIBOM promises to help organizations:
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Identify vulnerabilities and dependencies within models.
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Track licenses and obligations tied to models and datasets.
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Prove compliance with emerging regulations.
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Build trust with customers and regulators through transparency.
However, AIBOMs are still in their infancy. Much like SBOMs after the 2021 U.S. Executive Order, the industry still defines standards and use cases. The next frontier will be improving the quality of data within AIBOMs, ensuring they contain accurate, actionable information, rather than a simple list of components.
How Does DevSecOps Secure the AI Supply Chain?
AI isn't just about models. It's about the entire stack that surrounds them. From vector databases to observability tools to frameworks like PyTorch and LangChain, every new component is another link in the software supply chain.
To manage this complexity, DevSecOps practices are essential. By integrating automated AI security software validation into CI/CD pipelines, organizations can:
- Catch vulnerabilities and licensing issues early.
- Ensure compliance artifacts (like SBOMs and AIBOMs) are generated consistently.
- Maintain auditable, repeatable processes for regulators and customers.
- Free developers to focus on innovation instead of manual security checks.
Why is Automation Essential for AI Builds?
Automation is particularly critical as models grow larger. Unlike pulling down a small Java library, downloading an AI model can involve gigabytes of data. Without proper caching and optimization, build times can soar, slowing development and increasing costs.
Automation helps teams manage this complexity by ensuring models are cached, versioned, and reused consistently across builds, rather than repeatedly downloaded from external sources. It also enables policy enforcement and security checks to run automatically within CI/CD pipelines, reducing the risk of unapproved or vulnerable models entering production. As AI development scales, automation becomes the only practical way to maintain speed, reliability, and strong AI governance in your software supply chain without placing additional burden on development teams.
Building a Stronger Foundation Rooted in AI Governance
As AI adoption accelerates, organizations can't afford to treat governance as an afterthought. The AI governance risks — bias, data leakage, and regulatory noncompliance — are too significant. But with risks also come opportunities.
By adopting AIBOMs, embracing DevSecOps, and leaning on automation, organizations can build not only secure but also trustworthy AI systems. The result is a stronger foundation for innovation, where developers can focus on what they do best, while governance happens seamlessly in the background.
AI governance solutions help bring visibility, control, and automation to how models are managed across the AI software supply chain. By centrally governing approved models, enforcing usage policies, and optimizing how models are stored and reused, organizations can reduce operational friction while strengthening security. These controls create an auditable foundation for AI governance and compliance, helping teams demonstrate responsible AI use while securing the software supply chain at scale.
Want to dive deeper into these themes of governance, risk, and the future of AI in software chains? Watch the full webinar recording of AI Fireside Chat: Governance, Risk & Managing the Future of Software Security.
Aaron is a technical writer on Sonatype's Marketing team. He works at a crossroads of technical writing, developer advocacy, software development, and open source. He aims to get developers and non-technical collaborators to work well together via experimentation, feedback, and iteration so they ...
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