As artificial intelligence becomes a first-class citizen in modern software stacks, the limitations of traditional Software Bills of Materials (SBOMs) are becoming increasingly apparent. Classical SBOM solutions were designed to inventory source code, libraries, and open-source dependencies. While that remains necessary, it is no longer sufficient for organizations building AI-native applications whose behavior is shaped as much by data and models as by code.
This is precisely where sbom.sh from Codenotary differentiates itself. sbom.sh is engineered for environments where AI components are dynamic, opaque, and operationally critical—providing visibility that traditional SBOM tooling simply does not offer.
sbom.sh solves the AI supply-chain problem by extending SBOMs from a static list of code dependencies into a living, behavioral inventory of data, models, and AI operations—restoring visibility, accountability, and control where traditional SBOMs go blind.
Capturing What Really Drives AI Behavior: Data
In AI-heavy development organizations, datasets are foundational assets. Training data, fine-tuning corpora, embeddings, and retrieved documents all influence model behavior and risk exposure. Traditional SBOMs ignore these entirely.
sbom.sh extends the SBOM concept by enabling teams to explicitly document dataset sources, classifications, licensing terms, and approval status. This capability is essential for enforcing data governance, avoiding license violations, and demonstrating provenance during audits or regulatory reviews. By treating datasets as first-class supply chain artifacts, sbom.sh closes a critical gap in AI security and compliance.
Full Model Lineage and Training Transparency
Models do not exist in isolation. They are trained, fine-tuned, versioned, and sometimes updated by external providers without direct visibility to the consuming application. Conventional SBOM tools typically record only the SDK or client library used to access a model, offering no insight into the model itself.
sbom.sh enables organizations to capture model lineage in depth, including base model origins, training and fine-tuning details, version identifiers, and update history. This allows teams to trace outputs back to specific model versions and training inputs—an essential requirement for debugging, risk analysis, and regulatory accountability in AI-driven systems.
Operational Visibility Into Inference and Integrations
AI supply chain risk extends well beyond training. Inference endpoints, access controls, monitoring hooks, and downstream integrations define how models are used in production and how failures or misuse propagate through systems.
sbom.sh documents inference endpoints alongside their access policies, helping organizations understand where AI capabilities are exposed and who can interact with them. It also captures monitoring and observability hooks, as well as downstream systems that consume AI outputs. This operational visibility—entirely absent from traditional SBOMs—is critical for incident response, blast-radius assessment, and runtime governance.
Ownership and Accountability Across AI Components
One of the most common challenges in AI environments is unclear ownership. When an issue arises, teams often struggle to determine who approved a dataset, who owns a model, or who is responsible for a specific integration.
sbom.sh embeds ownership and approval metadata for every AI component, enforcing accountability and simplifying governance. This dramatically reduces time to resolution during security incidents, audits, or compliance reviews.
Sbom.sh is Free, Open, and Built for AI-First Teams
Importantly, sbom.sh is completely free to use and openly accessible to everyone at https://sbom.sh
This removes barriers to adoption and allows organizations of any size to immediately improve visibility into their AI software supply chain.
In the age of AI-native software, sbom.sh represents a necessary evolution of SBOMs—one that reflects how modern systems are actually built, deployed, and operated.
