What is an AI Bill of Materials (AI-BOM)?
An AI Bill of Materials (AI-BOM) is a complete inventory of the assets that make up an organization’s AI ecosystem. This includes models, datasets, services, infrastructure, and third-party dependencies, along with the relationships between them.
Like a software bill of materials (SBOM), an AI-BOM can be represented using structured formats, such as extensions of existing standards like SPDX, to make AI components easier to share, audit, and reason about across teams. Unlike a simple list of assets, an AI-BOM captures how models connect to data, services, and environments, providing the traceability needed to understand how AI systems operate in practice.
By cataloging both components and their dependencies in a consistent format, an AI-BOM provides the visibility required to secure, govern, and audit AI systems as they evolve in production.
What's the difference between AI-BOM and SBOM?
AI-BOMs function similarly to SBOMs (or Software Bills of Materials) but are purpose-built for the complexities of modern AI systems.
Unlike the SBOM’s focus on static software components, AI systems involve non-deterministic models, constantly evolving artificial intelligence algorithms, and their data dependencies. This expansive and detailed approach is necessary to capture these complexities and provide the foundation for effective AI Security Operations (AI SecOps).
An AI-BOM builds on the SBOM concept but extends it beyond code to include models, datasets, and dynamic dependencies – everything that influences AI system behavior.
What are the benefits of AI-BOMs?
As organizations scale their AI operations, the complexity and volume of AI risks grow rapidly. That’s where AI-BOMs help. They provide a foundation for teams to manage risk, compliance, and governance.
Here are four key benefits of implementing an AI-BOM in your organization:
Gain complete visibility into your AI landscape: AI-BOMs uncover hidden risks by identifying shadow AI tools, outdated components, unvetted datasets, and insecure dependencies in AI models.
Simplify AI regulatory compliance: They provide the documentation and traceability your organization needs to meet audit requirements and stay aligned with frameworks like the EU AI Act and the NIST AI Risk Management Framework (AI RMF).
Increase transparency and build stakeholder trust: AI-BOMs outline how AI systems operate, the safeguards in place, and how your team addresses issues such as bias or misuse.
Strengthen governance across the AI lifecycle: By tracking ownership, usage, and change history, AI-BOMs support reproducibility, auditability, and internal policy enforcement across teams.
How an AI-BOM is used
An AI Bill of Materials is most valuable when it is treated as a living source of truth rather than a one-time artifact. In practice, organizations use AI-BOMs to support day-to-day security, governance, and operational decisions across the AI lifecycle.
AI discovery and inventory
Identify which models, datasets, services, and dependencies are in use across environments
Surface unmanaged or undocumented AI usage as systems evolve
Traceability and explainability
Understand how models are built, where they run, and what data and services they rely on
Trace AI behavior back to underlying components during reviews or investigations
Risk assessment and prioritization
Evaluate exposure based on access to sensitive data, permissions, and downstream dependencies
Prioritize issues using real relationships rather than isolated findings
Governance and compliance
Support audits, internal reviews, and regulatory requirements with a structured record of AI components
Demonstrate ownership, controls, and change history over time
Change management and incident response
Assess the impact of model updates, data changes, or dependency upgrades before deployment
Speed up investigations by identifying affected AI components and their blast radius
Used this way, an AI-BOM provides shared context across engineering, security, and governance teams. Instead of relying on static documentation or tribal knowledge, organizations can reason about AI systems using a consistent, traceable view that evolves alongside production environments.
Core components of an AI-BOM
An AI Bill of Materials captures more than a list of models. To be useful for security, governance, and operations, an AI-BOM documents the full set of components that make up an AI system and how they relate to one another.
At a minimum, a complete AI-BOM includes the following core components:
Models
Foundation models, hosted models, fine-tuned models, and internally trained models
Model versions, configurations, and deployment contexts
Data assets
Training data, fine-tuning datasets, evaluation data, and inference-time data sources
Data stores, feature stores, and external data feeds accessed by models
AI services and interfaces
Managed AI services, APIs, endpoints, and SDKs used to invoke models
Agent frameworks, orchestration layers, and tool integrations
Dependencies and libraries
ML frameworks, AI SDKs, supporting libraries, and third-party packages
Runtime dependencies required for training, serving, or orchestration
Infrastructure and environments
Compute, storage, networking, and acceleration resources used by AI workloads
Cloud accounts, regions, environments, and deployment boundaries
Identities and access
Service accounts, roles, permissions, and credentials used by AI systems
Access paths between models, data, and downstream services
Relationships and lineage
How models connect to data, services, and infrastructure
Upstream and downstream dependencies that determine blast radius and impact
Together, these components allow an AI-BOM to represent how AI systems actually operate in production. By capturing both assets and their relationships, the AI-BOM becomes a foundation for traceability, risk analysis, and governance as AI systems evolve.
How to build an AI-BOM
Developing an AI-BOM may seem complex, but it becomes more manageable when you approach it with a straightforward, step-by-step process. By following these steps, you can guide your organization from initial visibility planning to full, confident automation:
Plan and scope: Start by identifying the AI systems, teams, and environments the AI-BOM should cover. This step ensures clear boundaries and alignment between your AI-BOM and organizational goals. You must also decide whether the AI-BOM will serve a single project or many projects across your organization's broader AI ecosystem.
Select a framework: Use established frameworks, like SPDX AI, to capture essential details about the datasets, AI models, dependencies, and infrastructure. Templates like this save time and ensure your AI-BOM doesn’t miss critical components. Once you've chosen a framework, consider how you'll present and manage the information. Designing the AI-BOM as an interactive catalog makes it easier for teams to browse components and helps keep entries up to date as your AI systems evolve.
Catalog the components: Create an initial inventory by recording the core components of your AI-BOM. As you document each AI component, assign clear ownership so it’s easy to identify who is responsible for those assets. This traceability lays the groundwork for visibility across your AI ecosystem, helping you answer questions like “What assets do we have?” and “Who’s accountable for this risk?”
Operationalize your AI-BOM: Automate dynamic data collection by integrating your AI-BOM with CI/CD and MLOps pipelines. You can use pipeline scripts and automation tools to extract metadata from your AI components during runtime. Implementing automatic updates helps keep the inventory current—the pipeline generates a fresh AI-BOM every time your team trains a new model version or updates a dependency. Finally, centralize and enable version control to preserve a complete history of the AI system.
Implement continuous monitoring and enforcement: Make your AI-BOM proactive by automating security enforcement. Embed automated checks into your pipelines that enforce compliance gates. Next, you’ll want to track component behavior to detect unauthorized changes or drifts, since even minor, unapproved changes to models or dependencies can introduce security vulnerabilities or break compliance. Finally, set up remediation workflows to act on policy violations.
With these steps in place, your AI-BOM becomes a foundation for managing AI assets and supporting security, compliance, and governance as your systems scale.
However, challenges can still arise due to difficulties in maintaining accuracy in dynamic AI environments, managing third-party components, and aligning with evolving regulations. To address these challenges, consider advanced solutions like Wiz’s AI security posture management (AI-SPM).
Wiz AI-BOM
Wiz approaches AI-BOM as a security-first system of record for AI, not a static checklist or documentation exercise. Instead of asking teams to manually track models, data, and dependencies, Wiz automatically discovers AI services, model usage, and supporting infrastructure across cloud environments and maps them into the Wiz Security Graph.
This graph-based approach is what makes Wiz AI-BOM actionable. Every model, dataset, identity, network path, and cloud resource is connected, allowing security teams to see not just what AI exists, but how it behaves in production, what it can access, and where real risk accumulates. AI-BOM becomes the foundation for evaluating exposure, blast radius, and ownership as AI systems evolve.
Because AI-BOM is built on Wiz’s agentless cloud visibility, it continuously stays up to date as teams experiment, deploy new services, or introduce AI agents and tools. That means security teams can detect shadow AI early, enforce guardrails consistently, and prioritize AI risk using the same context-driven workflows they already use for cloud security.
In Wiz, AI-BOM is not an isolated AI feature. It is a core extension of the Wiz platform that brings AI assets into the same unified view as vulnerabilities, misconfigurations, identities, and data. This allows organizations to secure AI at the speed it is built, without slowing teams down or introducing yet another disconnected tool.
Ready to see our AI capabilities in action? Request a demo to experience how Wiz streamlines AI security operations from code to cloud.