Artificial intelligence has become an essential part of modern software, and a lack of insight into its internal components creates significant blind spots. Similar to the concept of a Software Bill of Materials (SBOM), the AI Bill of Materials (AI BOM or AIBOM) has emerged as a foundational framework for documenting and protecting this increasingly complex supply chain.
AI BOM vs. SBOM
Both AI BOMs and SBOMs share the same overarching purpose: enabling visibility into digital supply chains. The difference lies in scope. An SBOM generally catalogs third-party libraries, versions, licenses, and known vulnerabilities within software. An AI BOM, on the other hand, expands the idea to encompass the full lifecycle of an AI model. It documents elements such as training datasets, model weights, and data augmentation methods, ensuring a much broader perspective.
Advantages of adopting an AI BOM
The primary advantage often associated with implementing an AIBOM is the transparency it delivers. Every component used to design and deploy AI systems—ranging from datasets and algorithms to frameworks, libraries, and dependencies—becomes cataloged, traceable, and auditable. This level of documentation strengthens the security posture of AI environments by exposing risks, reducing vulnerabilities, and facilitating compliance with regulatory audits. Importantly, an AIBOM can also strengthen confidence in AI initiatives, particularly within organizations that remain cautious about adoption.
Beyond transparency, an AI BOM contributes to operational efficiency. Documented components can be reused more effectively, replication of AI systems can be scaled, and cross-department collaboration is enhanced by providing shared visibility to stakeholders involved in AI projects.
How does an AI BOM enhance GenAI security?
An AIBOM addresses risks unique to artificial intelligence that go beyond conventional application vulnerabilities.
- Data leakage prevention. By cataloging training data sources and defining access controls, an AI BOM helps prevent the inadvertent use or disclosure of sensitive or proprietary data during model development.
- Adversarial risk detection. Traceability of model inputs and configurations allows the identification of weaknesses that could be exploited through adversarial examples or poisoning attacks.
- Model tampering visibility. Documenting model provenance and update history provides a mechanism for detecting unauthorized modifications, preserving integrity throughout the lifecycle.
- Guardrails against prompt injection. Clear records of how prompts are filtered and processed establish safeguards for input handling in generative AI systems, reducing the potential for prompt manipulation.
Five essential elements of an AI BOM
An AIBOM is designed to capture the full lifecycle of an AI model rather than serve as a simple list of files or dataset references. Its scope spans development, training, and deployment, providing a comprehensive record.
1. Model metadata
Key details include model architecture (e.g., transformer, convolutional neural network), training objectives, version history, and the parameters or weights applied during training. Provenance data—covering the origin of the model, its creators, and methods—plays a central role in maintaining trust and traceability. For organizations working with third-party or open-source models, metadata also provides assurance of source authenticity and licensing.
2. Datasets
Training and validation datasets should be documented with information on origins, formats, labeling methods, and preprocessing techniques. Capturing dataset details not only supports reproducibility but also highlights issues related to data quality and bias. For instance, a language model trained predominantly on English-language news may display regional or cultural skew. Transparency of datasets is increasingly mandated in regulated or high-risk environments as part of compliance and auditing processes.
3. Software and frameworks
AI systems rely on a layered stack of libraries, frameworks, and dependencies such as TensorFlow, PyTorch, scikit-learn, or Hugging Face Transformers. In the same way as an SBOM, an AI BOM should enumerate these components, including their versions and licenses. This enables security teams to identify vulnerabilities, apply necessary patches, and maintain consistency across environments.
4. Hardware and compute environment
The infrastructure used for training and inference can significantly influence performance and reliability. An AI BOM should therefore capture specifications such as GPU types, memory capacity, and operating systems. These details facilitate reproducibility and troubleshooting, particularly for models sensitive to hardware variations or those deployed across heterogeneous environments. For example, a model tuned for high-memory GPUs may encounter instability or reduced performance on resource-constrained edge devices.
5. Ethical and usage documentation
Responsible AI requires more than technical transparency. An AI BOM should also include information on usage policies, intended applications, known limitations, and ethical considerations. Such documentation supports compliance with internal governance frameworks and external standards for ethical AI. In the context of generative AI, it provides clarity on acceptable use, moderation practices, and safeguards to prevent misuse.
How to create an AI BOM
Developing an effective AI BOM requires a structured, repeatable process that integrates seamlessly with existing DevSecOps practices and delivers continuous visibility throughout the AI lifecycle. At scale, the process can be organized into the following stages:
- Define scope and objectives. Establish which models, applications, or environments will be covered and determine the regulatory requirements being addressed. Clarifying goals for visibility, compliance, and security ensures alignment with broader risk management strategies.
- Discover assets. Identify all AI-related assets across the environment, including trained models, datasets, training scripts, package managers, APIs, and third-party dependencies. Automated discovery solutions can uncover hidden dependencies and highlight unmanaged resources.
- Extract metadata. Gather detailed information from each asset—such as model parameters, dataset origins, software versions, and compute environments. This metadata provides the foundation for traceability, risk assessment, and auditing.
- Organize inventory. Arrange the collected information into a standardized, searchable format that supports filtering and analysis. When possible, align the structure with existing SBOM frameworks to promote consistency and simplify integration.
- Integrate with pipelines. Embed AI BOM generation within machine learning development pipelines so that documentation updates automatically with each new model version.
- Apply governance. Define clear policies specifying what must be included in an AI BOM and assign responsibility for its upkeep. Governance should cover access controls, review workflows, and version management to preserve model provenance and track its evolution.
- Validate and maintain. Continuously monitor for changes to ensure accuracy over time. Regular validation helps identify drift, missing details, and outdated components.
- Incorporate into SecOps. Make the AI BOM an operational element within security and deployment pipelines. Leverage it to support vulnerability management, incident response, and compliance reporting, similar to how SBOMs are integrated into secure software supply chains.
Mend.io for AI BOM creation
Mend.io strengthens the security of AI-driven applications through a proactive strategy for mitigating AI-related risks and by providing tools purpose-built for artificial intelligence systems. The platform enables comprehensive visibility into every AI component within the development pipeline, automatically detecting models, agents, RAG elements, and MCPs across applications, while generating a live, continuously updated AI BOM. In addition, it supports centralized policy enforcement, covering model usage, licensing requirements, and prompt security, with automated implementation and approval workflows to ensure consistent governance.
