Think of an AI bill of materials as a detailed recipe card for every intelligent system in your organization. It lists the ingredients — the base models, the training data, the fine-tuning parameters. But what happens when you have thousands of recipe cards written in different languages, stuffed into a drawer, with no clear way to check for spoiled ingredients? That is the state of ai bom usability for most security teams today. The ecosystem is maturing quickly, but the gap between having a BOM and using a BOM is vast. Let’s break down the specific ways security leaders can close that gap.
The standards for AI bills of materials are moving from theory into practice. Tools are emerging. However, a recent Optero report found that while 85% of organizations have integrated AI into core operations, only 25% have comprehensive visibility into how that AI is used. This lack of visibility is a ticking clock for risk managers. The good news is that practitioners can iterate heavily on what they have learned from years of software supply chain work.
Daniel Bardenstein of Manifest Cyber puts it plainly. He says that AI is a subset of software and that 90% of AI security is traditional software security. If most organizations simply apply what they already do for software security onto their AI systems, they are already most of the way there. That last 10% is the tricky part. It requires new thinking about data provenance, model tuning, and embedded intelligence. Here are seven concrete ways to turn AI BOMs from a hopeful concept into a daily operational tool.
The First Step Always Starts With Scoping
You cannot document what you do not know exists. The first operational hurdle is simple discovery. Many organizations are surprised to learn how many AI models are running under their roof. This isn’t just about the shadow AI projects that business units spin up without IT approval. It is also about authorized software that has quietly integrated AI features.
Your team needs to build a comprehensive map. This means asking pointed questions during procurement. Does this tool embed a language model? Is it using our data to train a public model? It also means working with internal development teams to track every instance of model fine-tuning. If a developer takes an approved base model and fine-tunes it with internal data, a new artifact is born. Unless that change is captured, you have created a blind spot. Only once you have this map can you start to build a usable BOM.
7 Strategies to Bridge the AI BOM Usability Gap
1. Prioritize Discovery and Supply Chain Mapping
The path to ai bom usability begins with rigorous discovery. You need to know what your AI supply chains actually look like. This means identifying internal builds, embedded models in purchased software, and the data used for training or customization. Bardenstein emphasizes that this is where most of the work lives. Asking key scoping questions — Who built this? Where is it deployed? What data tuned it? — turns a vague shadow AI problem into a concrete inventory. Start with a simple spreadsheet if you must, but move quickly toward automated discovery tools that can scan your cloud environments and code repositories.
2. Adopt Open Standards for Cross-Platform Interoperability
An AI BOM is only useful if your existing tools can read it. The industry is rallying around standard formats, much like the SPDX and CycloneDX standards for SBOMs. CycloneDX, for instance, has already released specifications for AI model metadata. By adopting these open standards early, you ensure that your ai bom usability is not locked into a single vendor. This standardization allows your vulnerability scanners, asset management systems, and GRC platforms to ingest the data natively. It turns the BOM into an operational asset rather than a static PDF that sits on a drive.
3. Embed BOM Generation into Development Pipelines
Waiting until deployment to generate an AI BOM is a recipe for inaccuracy. The magic happens when BOM creation is automated into the development lifecycle. Modern MLOps and DevSecOps platforms can treat the AI BOM as a natural artifact of the build process. Every time a data scientist exports a model from a notebook, or when a developer pushes a container with a pre-trained model to a registry, the pipeline should automatically generate or update the BOM. This keeps the data fresh and drastically reduces the manual overhead that kills most compliance initiatives. It shifts the work left, making documentation a seamless byproduct of development.
4. Connect AI BOMs Directly to Incident Response Workflows
This is where theory meets reality. A usable AI BOM is not a filing cabinet document. It is a live data feed. Imagine a high-severity vulnerability is published for a popular open-source machine learning framework. Your inbox explodes. The CISO asks, “Are we affected?” Without a usable BOM integrated into your incident response platform, you are looking at days of manual work. You would need to email team leads, check deployment manifests, and guess. With a live, queryable AI BOM integrated into your SIEM and SOAR platforms, you can instantly run a search. You can ask, “Find all models using version X of framework Y.” This capability transforms the BOM into a critical triage tool. It is the difference between reacting and proactively containing a threat.
5. Tackle the Unique Challenges of Data and Model Provenance
Daniel Bardenstein notes that 90% of AI security is traditional security. The tricky 10% involves the unique aspects of AI: fine-tuning, custom datasets, and model drift. A base model from a reputable source is relatively safe. But what happens when a developer fine-tunes that model using a novel dataset? The dataset might contain biased information, proprietary code, or personally identifiable information.
Improving ai bom usability means specifically capturing this lineage. The BOM needs to answer, “This model started as Model A, was fine-tuned on Dataset B, by Person C, on Date D.” Tracking this provenance is hard, but it is non-negotiable for compliance with emerging regulations like the EU AI Act. It is the frontier of AI security, and it requires deep collaboration between security teams and data science teams.
6. Demand Transparency from Software Vendors
You cannot secure what you do not know about. This is particularly true for software-as-a-service products. A marketing team might adopt a new email platform that has an AI writing assistant built in. The security team might not even know the feature exists. CISOs need to build a muscle of asking the right questions during vendor risk assessments. Does your application embed any generative AI features? What models are you using internally? Are you training on our tenant data?
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The answers to these questions need to be fed back into the AI BOM ecosystem. This creates market pressure for vendors to be transparent. When a vendor discloses their embedded models, it raises the baseline of security for everyone. It also allows the customer to accurately represent their own risk posture. Vendor transparency is a cornerstone of a mature AI BOM strategy.
7. Automate Validation and Regular Hygiene Checks
An AI BOM is a living document. Models get updated, datasets change, and new vulnerabilities emerge every day. Treating the BOM as a one-time artifact defeats its purpose. The final strategy for usability is automating regular hygiene checks. Just as you run weekly vulnerability scans, you should run automated checks that compare your AI BOM inventory against known vulnerability databases.
Teams should set up automated jobs that scan their AI assets and compare them against the BOM. Are there any models running that are not documented? Have any datasets been updated without a corresponding update to the BOM? Integrating this hygiene process into existing GRC workflows ensures that the BOM remains a single source of truth. Treat it like an inventory checklist that you audit every quarter. This doesn’t just improve security. It builds confidence among stakeholders that the AI systems are being managed responsibly.
Making the Data Actionable
The real payoff for AI BOMs will come from how the documentation plugs into existing security and governance workflows. A BOM in a drawer is useless. A BOM that feeds your incident response tools, your asset management systems, and your compliance dashboards is invaluable. The platforms that handle SIEM, asset management, and GRC need to speak AI BOM natively.
This integration is where the majority of the engineering effort will lie over the next 18 months. Security teams need a clear roadmap for actionability. They need to know how to ingest the data, how to query it, and how to respond when the data changes. The good news for security leaders is that this does not require a wholesale reinvention of processes. It is an evolution of the supply chain security work that many teams have been doing for years.
Treating AI Security as an Extension of Existing Practice
The bar for getting started is lower than it seems. If your team already handles software bills of materials, you are most of the way there. The same principles apply. Identify your assets, document their dependencies, monitor for vulnerabilities, and automate your responses. The unique parts of AI — the data lineage, the model tuning, the concept of drift — are extensions to a framework you already understand.
CISOs should be building processes and integrated systems that help them move quickly when a vulnerability is disclosed. They should be having conversations with their development teams about where models come from and how they change. They should be asking their vendors for the same transparency they demand from their own teams.
The path to making AI BOMs genuinely useful is not about a single silver bullet. It is about weaving these documents into the fabric of existing security operations. By focusing on discovery, standardization, pipeline integration, incident response, data provenance, vendor transparency, and automation, security teams can move from being overwhelmed by AI complexity to confidently managing it. The tools and standards are catching up. The organizations that prioritize ai bom usability today will be the ones best prepared for the regulatory and threat landscapes of tomorrow.
