Concern 175 · Working draft · editorial review in progress
Software bill of materials (SBOM) for AI
An AI software bill of materials is an inventory of the components that make up an AI system; the concern is that missing details can hide dependencies and risks.
Understanding the concern
An ordinary software inventory lists components and versions. AI systems also depend on models, data, and other artifacts whose origins and relationships may be harder to document. Without a useful inventory, a team may not know whether a newly discovered problem affects its service.
An inventory is not a safety certificate. It helps people ask informed questions and locate affected components, but it does not prove that data is appropriate or that behavior is safe. Guidance is developing, including additional AI-specific elements, so documentation needs to remain current.
An example
An ingredients list helps identify an allergen, but it does not by itself establish that a meal was prepared safely.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
- Model
- The learned part of an AI system. It uses patterns found during training to produce an answer or prediction.
- SBOM
- Software bill of materials: a list of the software parts a product depends on.
Evidence and its limits
The 2026 CISA and G7 guidance adds AI-specific inventory recommendations and explicitly describes them as non-exhaustive and non-mandatory.
CISA · May 2026 · Official guidance announcement
CISA and G7 Partners Release Joint Guidance on the Minimum Elements of a Software Bill of Materials for Artificial Intelligence ↗ (opens in a new tab)Describes supplemental AI inventory recommendations as non-exhaustive and non-mandatory.
NIST · SP 800-218A · 2024 · Official guidance
Secure Software Development Practices for Generative AI and Dual-Use Foundation Models ↗ (opens in a new tab)Adds AI-specific lifecycle practices to secure software development; guidance does not certify a particular system.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
Can we identify the model, data and software dependencies needed to assess a newly reported risk?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Stewardship
Stewardship means caring for resources and systems that others depend on, including people who will use them later. It looks beyond the benefit of a quick launch.
Plan for maintenance, energy use, data protection, and eventual shutdown. Ask who will look after the tool when the original team moves on and how future users can avoid inheriting hidden costs.
Ask together: What are we responsible for caring for over time?
Prudence
Prudence means using careful judgment before and during action. It balances worthwhile opportunities with the need to avoid harm that could have been anticipated.
Start with a limited trial, set stopping points, and prefer choices that can be undone when uncertainty is high. Review what happens rather than assuming the original plan remains sound.
Ask together: What is a careful next step, and when should we stop or change course?
Accountability
Accountability means being answerable for decisions and taking responsibility for repair. It requires more than saying that the software made the choice.
Name the person or team responsible, keep useful records, and give them authority to correct problems. Make the route to a complaint clear to the people affected.
Ask together: Who will answer for the outcome and help put things right?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
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