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+# Frontispiece
+
+## About the Guide
+CycloneDX is a modern standard for the software supply chain. It has been ratified as [ECMA-424](https://ecma-international.org/publications-and-standards/standards/ecma-424/) by Ecma International.
+
+The content in this guide results from the work of the CycloneDX AI/ML (Artificial Intelligence/Machine Learning) Working Group with continuous community feedback and input from leading experts in the field. This guide would not be possible without valuable feedback from peers at the CycloneDX Industry Working Group (IWG), the CycloneDX Core Working Group (CWG), the many CycloneDX Feature Working Groups (FWG), Ecma International Technical Committee 54, and a global network of contributors and supporters.
+
+## Copyright and License
+
+![license](../../../images/license.svg)
+
+Copyright © 2026 The OWASP Foundation.
+
+This document is released under the [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/). For any reuse or distribution, you must make clear to others the license terms of this work.
+
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+</div>
+
+First Edition (Revision 1), 10 June 2026
+
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+
+| Version       | Changes         | Updated On | Updated By                    |
+|---------------|-----------------|------------|-------------------------------|
+| First Edition, r1 | Updates to appendices and examples| 2026-06-10 | CycloneDX AI/ML Working Group |
+| First Edition | Initial Release | 2026-03-03 | CycloneDX AI/ML Working Group |
+
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+# Preface
+
+Welcome to the Authoritative Guide series by the OWASP Foundation and OWASP CycloneDX. In this series, we aim to provide comprehensive insights and practical guidance, ensuring that security professionals, developers, and organizations alike have access to the latest best practices and methodologies.
+
+At the heart of the OWASP Foundation lies a commitment to inclusivity and openness. We firmly believe that everyone deserves a seat at the table when shaping the future of cybersecurity standards. Our collaborative model fosters an environment where diverse perspectives converge to drive innovation and excellence.
+
+In line with this ethos, the OWASP Foundation has partnered with Ecma International to create an inclusive, community-driven ecosystem for the development of security standards. This collaboration empowers individuals to contribute their expertise and insights, ensuring that standards like CycloneDX reflect the collective wisdom of the global cybersecurity community.
+
+One standout example of this model is OWASP CycloneDX, which has been ratified as an Ecma International standard and is now known as ECMA-424. By leveraging the strengths of both organizations, CycloneDX serves as a cornerstone of security best practices, providing organizations with a universal standard for software and system transparency.
+
+As you embark on your journey through this Authoritative Guide, we encourage you to engage actively with the content and join us in shaping the future of cybersecurity standards. Together, we can build a safer and more resilient digital world for all.
+
+---
+
+Andrew van der Stock  
+Executive Director, OWASP Foundation
+
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+# The Innovative History of OWASP CycloneDX
+
+OWASP CycloneDX has carved a legacy steeped in innovation, collaboration, and a commitment to openness. OWASP
+continues to advance software and system transparency standards, prioritizing capabilities that facilitate risk reduction.
+
+![Timeline](../../../images/CycloneDX-History-Timeline.svg)  
+_Source: [https://tc54.org/history](https://tc54.org/history)_
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+# Acknowledgements
+
+The CycloneDX Authoritative Guide to AI/ML-BOM represents the collective effort and expertise of many individuals and organizations committed to advancing transparency and security in artificial intelligence and machine learning systems.
+
+## Primary Contributors
+
+This guide was developed by the **CycloneDX AI/ML Working Group**, whose members dedicated countless hours to research, discussion, writing, reviewing and refining the content to ensure it meets the highest standards of technical accuracy and practical applicability.
+
+We would like to specifically acknowledge the following individuals for their much appreciated contributions:
+
+- Matt Rutkowski, IBM, CycloneDX Project Maintainer, Chair AI/ML Work Group
+- Steve Springett, Chair of CycloneDX Standard, Founder of Ecma TC54, Chair of OWASP Global Board of Directors
+- Jan Kowalleck, CycloneDX Project Co-Lead
+- Michael Boone, NVIDIA
+- Jessica Butler, NVIDIA
+- Pratyusha Maiti, NVIDIA
+- Saquib Saifee, IBM
+- Colin Gigool, IBM
+- Aliza Heching, IBM
+- Nagalakshmi Satyanarayanan, IBM
+- Pavel Shukhman, Reliza
+
+
+## Community Support
+
+We extend our gratitude to the broader CycloneDX community for their invaluable feedback and contributions:
+
+- **CycloneDX Core Working Group (CWG)** - for technical guidance and specification alignment
+- **Ecma International Technical Committee 54 (TC54)** - for standardization support and governance
+
+
+## Open Source Spirit
+
+This guide embodies the open-source ethos of the OWASP Foundation and CycloneDX project. We thank all contributors, reviewers, and users who continue to improve and evolve this resource through their feedback and participation.
+
+---
+
+*The development of this guide would not have been possible without the collaborative spirit and dedication of the global CycloneDX community.*
+
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+# Introduction
+
+CycloneDX is a modern standard for the software supply chain. At its core, CycloneDX is a general-purpose Bill-of-Materials (BOM) standard capable of representing software, hardware,  services, and other types of inventory.
+
+CycloneDX is notably an OWASP flagship project, has a formal standardization process and governance model through [Ecma Technical Committee 54](https://tc54.org), and is supported by the global information security community.
+
+## What is an AI/ML-BOM?
+
+An AI/ML-BOM (Artificial Intelligence/Machine Learning Bill of Materials) is a CycloneDX BOM document designed to address the unique complexities and risks of AI/ML systems. It  provides a detailed inventory of all components, configurations, and processes involved in the development, training, deployment, and hosting (i.e., via hardware/software stacks and frameworks) of a machine learning model.
+
+The primary purpose of an AI/ML-BOM is to ensure transparency, traceability, security, and compliance throughout an ML model's lifecycle.
+
+
+### Why AI/ML-BOMs are Important
+
+AI/ML-BOMs address critical challenges in the machine learning supply chain:
+
+- **Security & Vulnerability Management**: Help  identify security risks, such as malicious (open-source) models or vulnerable dependencies, before they are integrated into production applications.
+
+- **Governance & Compliance**: Provide documentation for audits or formal informational requests based upon requirements from emerging global AI regulations such as the [European Union's Cyber Resilience Act (EU CRA)](https://www.european-cyber-resilience-act.com/), including specifics for AI models and systems from the complementary [EU AI Act](https://artificialintelligenceact.eu/), as well as for voluntary, guidance-focused frameworks such as the [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework).
+
+- **Risk Mitigation**: Enable teams to track data lineage, helping to identify and eliminate potential data quality issues, privacy risks, or unwanted biases that could affect the model's performance and fairness.
+
+- **Reproducibility & Explainability**: Show adherence to software development lifecycle best practices by providing a detailed record of components and training processes such that developers are able to reproduce models (via training from datasets) and their benchmarks in order to validate claims of model accuracy and adherence to ethical considerations.
+
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+# Core Concepts and Considerations
+
+## Key Components of an AI/ML-BOM
+
+An AI/ML-BOM typically documents the identifying elements, architecture, components, and its supply chain along with any configurations and developmental or executional considerations, inclusive of the following areas:
+
+- **Model identifiers**: Identifying information such as the model's [Package URL (PURL)](https://tc54.org/purl/) (e.g., from Huggingface `pkg:huggingface/distilbert-base-uncased@043235d6088ecd3dd5fb5ca3592b6913fd51602`) or other domain-specific identifiers within other registries. In CycloneDX 2.0, identifiers are asserted by a named party using the component's `identifiers` array.
+
+- **Model metadata**: Descriptive details such as the model's name, version, license, developer, purpose, use cases, architecture, (hyper)parameters and any additional identifying elements.
+
+- **Parties**: The organizations, people, systems (e.g., AI agents), and personas involved with the model, each with one or more explicit roles (e.g., `manufacturer`, `supplier`, `publisher`, `end-user`). These include the model's developers and distributors as well as its intended users and any groups that may be affected by it.
+
+- **Model architecture**: Description of the composition of the model's neural network including configurations, layers, input/output parameters, attention mechanisms, etc. used at network processing stages. CycloneDX 2.0 describes architecture using a controlled vocabulary of architecture families, structural features, and runtime topologies.
+
+- **Datasets**: Description of datasets, as CycloneDX data components, used for training and testing of the associated model. This includes data sources, selection criteria, acquisition methods, preprocessing steps, and more.
+
+- **Tokenizers and prompt templates**: Descriptive details of specific tokenizers (e.g., libraries, files, configurations) and prompt templates used to train and/or interact with the model during runtime.
+
+- **Hardware, software & frameworks**: A list of all hardware and software components including libraries, packages, frameworks (e.g., TensorFlow, PyTorch, Huggingface), along with specific versions and associated licenses used in aspects of the model's lifecycle. This informational category may also include operational and application aspects of models (perhaps as agents) used within compositional frameworks and workflows, along with the protocols used for communication.
+
+- **Training & testing details**: Information about the computational environment and systems (software, hardware, operating system, and GPUs) used for training or evaluation along with necessary configurations, hyperparameters, and evaluation metrics.
+
+- **Intended use, limitations & risks**: Documentation of the model's intended use cases and users, known technical limitations, safety guardrails, and the ethical, fairness, and other risks (including their harms, benefits, and mitigations) associated with the model.
+
+- **Environmental impacts**: Documentation of the resource needed to train or execute the model which have an environmental impact or cost (e.g., data center energy and water cooling cost details).
+
+> **Note**: This guide describes CycloneDX 2.0. Readers familiar with the CycloneDX 1.x `modelCard` object will find that its information is distributed in 2.0 across the model component's `modelProperties` and `measurements`, its `parties`, and the BOM-level `definitions` (use cases), `risks`, and `perspectives`. Where helpful, sections of this guide note the corresponding CycloneDX 1.7 fields.
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