AI Supply Chain Provenance Explorer for Responsible AI Governance

Cisco launched the AI Supply Chain Provenance Explorer, a public resource cataloging nearly 900 open source AI models to help organizations evaluate technical, governance, and risk-management requirements before deployment. The tool aggregates model details, provider context, provenance, security findings, licensing, and usage restrictions, addressing the complexity of modern AI supply chains where models are frequently fine-tuned, merged, or repackaged.

Open source AI has never been more accessible. The rapidly growing scale and diversity of AI models available to developers worldwide is unprecedented. The harder question begins after download: when you bring a model into your environment, what exactly are you bringing in? A repository page may tell you a model’s name, provider, and license. But responsible adoption often requires a fuller picture, knowing details such as: Where did the model come from? How has it changed? What restrictions accompany it? Are there any known security issues with the model? In an ecosystem shaped by research labs, startups, global technology companies, governments, and independent creators, access is only the first step. Responsible adoption starts with visibility. That is why we are excited to launch AI Supply Chain Provenance Explorer https://provenance.aidefense.cisco.com/ , a public resource that helps organizations evaluate open source AI models against their technical, governance, and risk-management requirements before they use them. With almost 900 open models catalogued to date, the Provenance Explorer brings model details, provider context, provenance, security findings, licensing information, and usage restrictions into one place. Why Transparency and Visibility Matter Modern AI supply chains are rarely linear https://blogs.cisco.com/ai/model-provenance-kit . Models are fine-tuned, distilled, merged, quantized, re-released, and repackaged, often many times over. Along the way, important context can be lost, simplified, or presented inconsistently. This presents practical problems for organizations. A model’s license may require attribution or limit commercial use. Its provider’s location may prompt additional regulatory, privacy, export-control, or policy review. Its lineage may be incomplete or unclear, potentially complicating incident response, compliance review, and internal approvals. Its repository may contain files that have not all been scanned or contain security issues. None of these signals necessarily disqualifies a model, but each can change the questions an organization should ask or investigate before deployment. What Provenance Explorer Brings Together Cisco AI Supply Chain Provenance Explorer https://provenance.aidefense.cisco.com/ helps organizations evaluate this critical component of the AI supply chain, and each model entry can include several categories of information: Model details, including distribution source, release and update dates, parameter count, training tokens, training compute, and downloads, details to help assess scale, complexity, and likely operational fit. Provider context, including organization’s name, headquarters location, website, and associated Hugging Face organizations to inform any relevant jurisdictional, regulatory, privacy, and export-related considerations. Model provenance and lineage information , including where a model came from and how it relates to prior models or variants over time. Rather than relying solely on self-reported lineages, we ground model relationships in similarity scores powered by Cisco Model Provenance Kit static fingerprinting and Project VAIL https://www.projectvail.com/pages/products fingerprinting run-time behavioral fingerprinting. Licensing information and usage restrictions, including common limitations such as attribution requirements, non-commercial terms, geographic restrictions, modification constraints, or prohibited use cases. Security assessments , including ClamAV-based scanning of model repository files, the number of files scanned, and reported vulnerability findings. The information is regularly updated and verified by experts on Cisco AI Threat & Security Research team and with partners at Project VAIL. Building on Our Broader Provenance Work AI Supply Chain Provenance Explorer is part of Cisco’s broader work to improve transparency, accountability, and trust across the AI ecosystem. Last year, in collaboration with HuggingFace, we advanced AI supply chain security by launching https://blogs.cisco.com/security/ciscos-foundation-ai-advances-ai-supply-chain-security-with-hugging-face a comprehensive malware scanning and threat intelligence sharing tool. Earlier this year, we introduced Model Provenance Kit https://blogs.cisco.com/ai/model-provenance-kit , an open source toolkit that helps determine whether models share a common origin by analyzing architecture metadata, tokenizer structure, and model weights. We also introduced the Model Provenance Constitution https://blogs.cisco.com/ai/model-provenance-constitution , which defines provenance as the verifiable derivation history of a model’s trained weights and establishes a rigorous standard for what counts as a provenance relationship. Those efforts address an important industry problem: provenance cannot rely on naming conventions or self-reported metadata alone. Reliable governance depends on clearer definitions, better evidence, and tools that help practitioners operationalize both. AI Supply Chain Provenance Explorer extends that work by making decision-relevant model information easier to access and interpret for a wider audience. For developers, that may mean comparing model scale, task, and licensing before choosing a baseline. For security teams, it may mean identifying models that warrant deeper review. For governance and legal stakeholders, it may mean spotting license obligations, usage restrictions, or provider considerations earlier in the process. For executive leaders, it means supporting AI adoption with better transparency into the components entering the enterprise stack. Responsible AI Starts with Knowing What You Are Using As organizations adopt more open source AI, understanding and verifying the provenance of AI models is essential for organizations seeking to responsibly deploy AI technologies. AI Supply Chain Provenance Explorer gives teams a clearer way to examine origin, transformation, ownership, licensing, and security signals before a model enters a production environment. Explore AI Supply Chain Provenance Explorer today at https://provenance.aidefense.cisco.com https://provenance.aidefense.cisco.com/ Disclaimer: Model details are presented for informational use only and do not constitute an endorsement or guarantee of performance. The Cisco AI Supply Chain Provenance Explorer is provided “as-is” without warranties of any kind. Cisco does not guarantee against, or assume liability for, out of date or inaccurate information, nor does Cisco guarantee that any evaluated model is safe, secure, or fit for your specific use case. Users are solely responsible for conducting their own independent assessment to determine the details of any model and its adequacy for their specific AI governance and security requirements.