{"id":50001,"topic":"ai","source":"Cisco Blogs","title":"AI Supply Chain Provenance Explorer for Responsible AI Governance - Cisco Blogs","url":"https://blogs.cisco.com/ai/supply-chain-provenance-explorer","url_hash":"4a9c2607b7c84e574900325380caf1a6abd6ec82","author":"","summary":"AI Supply Chain Provenance Explorer for Responsible AI Governance Cisco Blogs","content":"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?\nA 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?\nIn 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.\nThat is why we are excited to launch AI Supply Chain Provenance Explorer, 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.\nWhy Transparency and Visibility Matter\nModern AI supply chains are rarely linear. 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.\nThis 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.\nNone of these signals necessarily disqualifies a model, but each can change the questions an organization should ask or investigate before deployment.\nWhat Provenance Explorer Brings Together\nCisco AI Supply Chain Provenance Explorer helps organizations evaluate this critical component of the AI supply chain, and each model entry can include several categories of information:\n- 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.\n- 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.\n- 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 run-time behavioral fingerprinting.\n- Licensing information and usage restrictions, including common limitations such as attribution requirements, non-commercial terms, geographic restrictions, modification constraints, or prohibited use cases.\n- Security assessments, including ClamAV-based scanning of model repository files, the number of files scanned, and reported vulnerability findings.\nThe information is regularly updated and verified by experts on Cisco AI Threat & Security Research team and with partners at Project VAIL.\nBuilding on Our Broader Provenance Work\nAI 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 a comprehensive malware scanning and threat intelligence sharing tool.\nEarlier this year, we introduced 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, 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.\nThose 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.\nFor 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.\nResponsible AI Starts with Knowing What You Are Using\nAs 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.\nExplore AI Supply Chain Provenance Explorer today at https://provenance.aidefense.cisco.com\n* 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.","image_url":"https://blogs.cisco.com/gcs/ciscoblogs/1/2026/07/ChatGPT-Image-Jul-29-2026-01_37_24-PM-1024x768.png","lang":"en","published_at":"2026-07-30T12:57:37+00:00","fetched_at":"2026-07-30T19:15:04+00:00","status":"read","starred":0,"extract_state":"ok","summary_auto":"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?","cluster_id":null,"extract_retries":0,"extract_error":null,"contract_version":"news_item.v1","format_contract_version":"news_item_formats.v1","dedup_url":"https://blogs.cisco.com/ai/supply-chain-provenance-explorer","quality_profile":{"profile_version":"extraction_quality.v2","bucket":"high","confidence":0.9,"failure_kind":"none","retryable":false,"retry_after_attempts":0,"reason":"High confidence: full text extraction produced 6378 characters.","operator_guidance":{"severity":"ok","recommended_action":"trust_full_text","next_step":"Use the extracted full text as the primary article source.","operator_label":"Ready","can_retry":false,"can_use_summary":false,"diagnostics_required":false},"content_depth":{"contract_version":"content_depth.v1","category":"full_text","label":"Full text","has_full_text":true,"has_summary":true,"content_length":6378,"summary_length":227,"usable_text_length":6378,"source_field":"content"},"legacy_collapsed":false,"signals":{"extract_state":"ok","extract_error":null,"extract_retries":0,"content_length":6378,"summary_length":227}},"news_item":{"id":50001,"canonical_url":"https://blogs.cisco.com/ai/supply-chain-provenance-explorer","source_url":"https://blogs.cisco.com/ai/supply-chain-provenance-explorer","title":"AI Supply Chain Provenance Explorer for Responsible AI Governance - Cisco Blogs","source_name":"Cisco Blogs","author":null,"published_at":"2026-07-30T12:57:37+00:00","locale":"en","topic":"ai","tags":[],"rss_summary":"AI Supply Chain Provenance Explorer for Responsible AI Governance Cisco Blogs","full_text":"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?\nA 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?\nIn 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.\nThat is why we are excited to launch AI Supply Chain Provenance Explorer, 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.\nWhy Transparency and Visibility Matter\nModern AI supply chains are rarely linear. 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.\nThis 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.\nNone of these signals necessarily disqualifies a model, but each can change the questions an organization should ask or investigate before deployment.\nWhat Provenance Explorer Brings Together\nCisco AI Supply Chain Provenance Explorer helps organizations evaluate this critical component of the AI supply chain, and each model entry can include several categories of information:\n- 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.\n- 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.\n- 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 run-time behavioral fingerprinting.\n- Licensing information and usage restrictions, including common limitations such as attribution requirements, non-commercial terms, geographic restrictions, modification constraints, or prohibited use cases.\n- Security assessments, including ClamAV-based scanning of model repository files, the number of files scanned, and reported vulnerability findings.\nThe information is regularly updated and verified by experts on Cisco AI Threat & Security Research team and with partners at Project VAIL.\nBuilding on Our Broader Provenance Work\nAI 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 a comprehensive malware scanning and threat intelligence sharing tool.\nEarlier this year, we introduced 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, 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.\nThose 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.\nFor 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.\nResponsible AI Starts with Knowing What You Are Using\nAs 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.\nExplore AI Supply Chain Provenance Explorer today at https://provenance.aidefense.cisco.com\n* 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.","excerpt":"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?","extraction":{"state":"ok","confidence":0.9,"error":null,"explanation":"High confidence: full text extraction produced 6378 characters.","diagnostics_url":"/api/diagnose?url=https%3A//blogs.cisco.com/ai/supply-chain-provenance-explorer","quality_profile":{"profile_version":"extraction_quality.v2","bucket":"high","confidence":0.9,"failure_kind":"none","retryable":false,"retry_after_attempts":0,"reason":"High confidence: full text extraction produced 6378 characters.","operator_guidance":{"severity":"ok","recommended_action":"trust_full_text","next_step":"Use the extracted full text as the primary article source.","operator_label":"Ready","can_retry":false,"can_use_summary":false,"diagnostics_required":false},"content_depth":{"contract_version":"content_depth.v1","category":"full_text","label":"Full text","has_full_text":true,"has_summary":true,"content_length":6378,"summary_length":227,"usable_text_length":6378,"source_field":"content"},"legacy_collapsed":false,"signals":{"extract_state":"ok","extract_error":null,"extract_retries":0,"content_length":6378,"summary_length":227}}},"display_formats":["compact","card","full","digest_section","json"]},"daily_stack_record":{"title":"AI Supply Chain Provenance Explorer for Responsible AI Governance - Cisco Blogs","url":"https://blogs.cisco.com/ai/supply-chain-provenance-explorer","summary":"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?","source":"Cisco Blogs","date":"2026-07-30T12:57:37+00:00","content":"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?\nA 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?\nIn 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.\nThat is why we are excited to launch AI Supply Chain Provenance Explorer, 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.\nWhy Transparency and Visibility Matter\nModern AI supply chains are rarely linear. 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.\nThis 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.\nNone of these signals necessarily disqualifies a model, but each can change the questions an organization should ask or investigate before deployment.\nWhat Provenance Explorer Brings Together\nCisco AI Supply Chain Provenance Explorer helps organizations evaluate this critical component of the AI supply chain, and each model entry can include several categories of information:\n- 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.\n- 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.\n- 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 run-time behavioral fingerprinting.\n- Licensing information and usage restrictions, including common limitations such as attribution requirements, non-commercial terms, geographic restrictions, modification constraints, or prohibited use cases.\n- Security assessments, including ClamAV-based scanning of model repository files, the number of files scanned, and reported vulnerability findings.\nThe information is regularly updated and verified by experts on Cisco AI Threat & Security Research team and with partners at Project VAIL.\nBuilding on Our Broader Provenance Work\nAI 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 a comprehensive malware scanning and threat intelligence sharing tool.\nEarlier this year, we introduced 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, 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.\nThose 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.\nFor 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.\nResponsible AI Starts with Knowing What You Are Using\nAs 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.\nExplore AI Supply Chain Provenance Explorer today at https://provenance.aidefense.cisco.com\n* 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.","confidence":0.9,"diagnostics_url":"/api/diagnose?url=https%3A//blogs.cisco.com/ai/supply-chain-provenance-explorer","quality_bucket":"high","failure_kind":"none","retryable":false,"quality_reason":"High confidence: full text extraction produced 6378 characters.","quality_profile":{"profile_version":"extraction_quality.v2","bucket":"high","confidence":0.9,"failure_kind":"none","retryable":false,"retry_after_attempts":0,"reason":"High confidence: full text extraction produced 6378 characters.","operator_guidance":{"severity":"ok","recommended_action":"trust_full_text","next_step":"Use the extracted full text as the primary article source.","operator_label":"Ready","can_retry":false,"can_use_summary":false,"diagnostics_required":false},"content_depth":{"contract_version":"content_depth.v1","category":"full_text","label":"Full text","has_full_text":true,"has_summary":true,"content_length":6378,"summary_length":227,"usable_text_length":6378,"source_field":"content"},"legacy_collapsed":false,"signals":{"extract_state":"ok","extract_error":null,"extract_retries":0,"content_length":6378,"summary_length":227}},"tags":[]},"fallback_formats":["markdown","json","html"],"actions":{"read":"/item/50001","export_markdown":"/api/items/50001/export?format=markdown","export_json":"/api/items/50001/export?format=json","diagnose":"/api/diagnose?url=https%3A//blogs.cisco.com/ai/supply-chain-provenance-explorer"},"formats":{"full":{"id":50001,"title":"AI Supply Chain Provenance Explorer for Responsible AI Governance - Cisco Blogs","url":"https://blogs.cisco.com/ai/supply-chain-provenance-explorer","source":"Cisco Blogs","author":null,"published_at":"2026-07-30T12:57:37+00:00","locale":"en","topic":"ai","tags":[],"excerpt":"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?","full_text":"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?\nA 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?\nIn 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.\nThat is why we are excited to launch AI Supply Chain Provenance Explorer, 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.\nWhy Transparency and Visibility Matter\nModern AI supply chains are rarely linear. 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.\nThis 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.\nNone of these signals necessarily disqualifies a model, but each can change the questions an organization should ask or investigate before deployment.\nWhat Provenance Explorer Brings Together\nCisco AI Supply Chain Provenance Explorer helps organizations evaluate this critical component of the AI supply chain, and each model entry can include several categories of information:\n- 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.\n- 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.\n- 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 run-time behavioral fingerprinting.\n- Licensing information and usage restrictions, including common limitations such as attribution requirements, non-commercial terms, geographic restrictions, modification constraints, or prohibited use cases.\n- Security assessments, including ClamAV-based scanning of model repository files, the number of files scanned, and reported vulnerability findings.\nThe information is regularly updated and verified by experts on Cisco AI Threat & Security Research team and with partners at Project VAIL.\nBuilding on Our Broader Provenance Work\nAI 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 a comprehensive malware scanning and threat intelligence sharing tool.\nEarlier this year, we introduced 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, 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.\nThose 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.\nFor 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.\nResponsible AI Starts with Knowing What You Are Using\nAs 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.\nExplore AI Supply Chain Provenance Explorer today at https://provenance.aidefense.cisco.com\n* 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.","reading_time_min":4,"extraction":{"state":"ok","confidence":0.9,"error":null,"explanation":"High confidence: full text extraction produced 6378 characters.","diagnostics_url":"/api/diagnose?url=https%3A//blogs.cisco.com/ai/supply-chain-provenance-explorer","quality_profile":{"profile_version":"extraction_quality.v2","bucket":"high","confidence":0.9,"failure_kind":"none","retryable":false,"retry_after_attempts":0,"reason":"High confidence: full text extraction produced 6378 characters.","operator_guidance":{"severity":"ok","recommended_action":"trust_full_text","next_step":"Use the extracted full text as the primary article source.","operator_label":"Ready","can_retry":false,"can_use_summary":false,"diagnostics_required":false},"content_depth":{"contract_version":"content_depth.v1","category":"full_text","label":"Full text","has_full_text":true,"has_summary":true,"content_length":6378,"summary_length":227,"usable_text_length":6378,"source_field":"content"},"legacy_collapsed":false,"signals":{"extract_state":"ok","extract_error":null,"extract_retries":0,"content_length":6378,"summary_length":227}}},"quality_profile":{"profile_version":"extraction_quality.v2","bucket":"high","confidence":0.9,"failure_kind":"none","retryable":false,"retry_after_attempts":0,"reason":"High confidence: full text extraction produced 6378 characters.","operator_guidance":{"severity":"ok","recommended_action":"trust_full_text","next_step":"Use the extracted full text as the primary article source.","operator_label":"Ready","can_retry":false,"can_use_summary":false,"diagnostics_required":false},"content_depth":{"contract_version":"content_depth.v1","category":"full_text","label":"Full text","has_full_text":true,"has_summary":true,"content_length":6378,"summary_length":227,"usable_text_length":6378,"source_field":"content"},"legacy_collapsed":false,"signals":{"extract_state":"ok","extract_error":null,"extract_retries":0,"content_length":6378,"summary_length":227}},"actions":{"read":"/item/50001","export_markdown":"/api/items/50001/export?format=markdown","export_json":"/api/items/50001/export?format=json","diagnose":"/api/diagnose?url=https%3A//blogs.cisco.com/ai/supply-chain-provenance-explorer"}},"digest":{"id":50001,"title":"AI Supply Chain Provenance Explorer for Responsible AI Governance - Cisco Blogs","url":"https://blogs.cisco.com/ai/supply-chain-provenance-explorer","source":"Cisco Blogs","topic":"ai","published_at":"2026-07-30T12:57:37+00:00","excerpt":"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?","quality_bucket":"high","quality_reason":"High confidence: full text extraction produced 6378 characters.","reading_time_min":4,"cluster_id":null},"card":{"display_title":"AI Supply Chain Provenance Explorer for Responsible AI Governance - Cisco Blogs","subtitle":"Cisco Blogs · 2026-07-30","summary":"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…","badges":["quality:high"],"links":{"read":"/item/50001","original":"https://blogs.cisco.com/ai/supply-chain-provenance-explorer","diagnose":"/api/diagnose?url=https%3A//blogs.cisco.com/ai/supply-chain-provenance-explorer"},"quality_warning":null},"export":{"title":"AI Supply Chain Provenance Explorer for Responsible AI Governance - Cisco Blogs","url":"https://blogs.cisco.com/ai/supply-chain-provenance-explorer","summary":"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?","source":"Cisco Blogs","date":"2026-07-30T12:57:37+00:00","content":"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?\nA 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?\nIn 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.\nThat is why we are excited to launch AI Supply Chain Provenance Explorer, 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.\nWhy Transparency and Visibility Matter\nModern AI supply chains are rarely linear. 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.\nThis 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.\nNone of these signals necessarily disqualifies a model, but each can change the questions an organization should ask or investigate before deployment.\nWhat Provenance Explorer Brings Together\nCisco AI Supply Chain Provenance Explorer helps organizations evaluate this critical component of the AI supply chain, and each model entry can include several categories of information:\n- 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.\n- 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.\n- 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 run-time behavioral fingerprinting.\n- Licensing information and usage restrictions, including common limitations such as attribution requirements, non-commercial terms, geographic restrictions, modification constraints, or prohibited use cases.\n- Security assessments, including ClamAV-based scanning of model repository files, the number of files scanned, and reported vulnerability findings.\nThe information is regularly updated and verified by experts on Cisco AI Threat & Security Research team and with partners at Project VAIL.\nBuilding on Our Broader Provenance Work\nAI 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 a comprehensive malware scanning and threat intelligence sharing tool.\nEarlier this year, we introduced 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, 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.\nThose 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.\nFor 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.\nResponsible AI Starts with Knowing What You Are Using\nAs 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.\nExplore AI Supply Chain Provenance Explorer today at https://provenance.aidefense.cisco.com\n* 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.","confidence":0.9,"diagnostics_url":"/api/diagnose?url=https%3A//blogs.cisco.com/ai/supply-chain-provenance-explorer","quality_bucket":"high","failure_kind":"none","retryable":false,"quality_reason":"High confidence: full text extraction produced 6378 characters.","quality_profile":{"profile_version":"extraction_quality.v2","bucket":"high","confidence":0.9,"failure_kind":"none","retryable":false,"retry_after_attempts":0,"reason":"High confidence: full text extraction produced 6378 characters.","operator_guidance":{"severity":"ok","recommended_action":"trust_full_text","next_step":"Use the extracted full text as the primary article source.","operator_label":"Ready","can_retry":false,"can_use_summary":false,"diagnostics_required":false},"content_depth":{"contract_version":"content_depth.v1","category":"full_text","label":"Full text","has_full_text":true,"has_summary":true,"content_length":6378,"summary_length":227,"usable_text_length":6378,"source_field":"content"},"legacy_collapsed":false,"signals":{"extract_state":"ok","extract_error":null,"extract_retries":0,"content_length":6378,"summary_length":227}},"tags":[],"format_contract_version":"news_item_formats.v1"}}}