# Making AI Bills of Materials Actionable: Five Critical Steps for Modern Security Programs


Artificial intelligence is reshaping enterprise infrastructure faster than security teams can assess the risks. As organizations race to deploy large language models, generative AI applications, and machine learning systems, a critical transparency gap has emerged: most teams have no visibility into what's actually inside these systems. AI Bills of Materials—detailed inventories of model components, training data, dependencies, and design choices—promise to close that gap. But getting there requires CISOs to actively shape how these BOMs are created and consumed.


## The Problem: Transparency Meets Reality


The concept of a Bill of Materials isn't new. Software developers have used SBOMs (Software Bills of Materials) for years to track dependencies, licenses, and vulnerabilities in code. AI BOMs should work similarly—documenting everything from the training datasets used to build a model, to the frameworks and libraries involved, to potential biases and performance limitations.


The challenge is that AI systems are fundamentally different from traditional software. A model's behavior emerges from billions of parameters shaped by training data whose provenance is often murky, proprietary, or incomplete. A single model might incorporate data from dozens of sources, some licensed, some scraped, some whose copyright status is actively disputed.


Yet the business pressure to deploy AI is intense. Executives want AI-powered customer service chatbots, predictive analytics, autonomous systems—now. Security teams are often asked to "enable" these deployments rather than fully understand them. Without standardized, usable BOMs, organizations have no systematic way to evaluate whether an AI system introduces unacceptable risks.


## Background: Why AI BOMs Matter Now


The regulatory environment is tightening. The EU's AI Act requires documentation of high-risk systems. NIST's AI Risk Management Framework recommends transparency measures. The White House's Executive Order on AI governance calls for accountability. Meanwhile, real incidents—from discriminatory hiring systems to hallucinating chatbots confidently providing false medical advice—have demonstrated that AI risks aren't theoretical.


The current vacuum means many organizations are doing one of two things: deploying AI systems with minimal security review, or blocking AI initiatives entirely because nobody can prove they're safe. Neither is sustainable.


This is where AI BOMs come in. By providing structured, standardized documentation, they allow CISOs to move beyond binary approve/reject decisions and into informed risk assessment. But the BOMs being generated today are often too vague to be useful—or so detailed they're impossible to act on.


## What's Inside an AI Bill of Materials?


A comprehensive AI BOM should include:


  • Model Information: Architecture, training approach, version history, creator organization
  • Data Provenance: Datasets used in training, their size, source, licensing status, potential biases
  • Dependencies: Libraries, frameworks, tokenizers, inference engines, and their versions
  • Compute Specifications: Hardware used for training, inference requirements, environmental impact
  • Performance Metrics: Accuracy, latency, known failure modes, performance on different demographic groups
  • Safety and Security: Known vulnerabilities, adversarial robustness testing results, content filtering mechanisms
  • Limitations: Clear documentation of what the model can't do, when it's likely to fail, who shouldn't use it

    • For proprietary models from vendors like OpenAI or Anthropic, CISOs face another problem: vendors release BOMs that omit sensitive information—which is often the information security teams most need to make risk decisions.


    ## Five Ways CISOs Can Prepare for AI BOMs


    ### 1. Define Your AI Taxonomy


    Before you can consume BOMs effectively, you need to classify your AI systems. What counts as "AI" in your organization? Is a simple linear regression model an AI system requiring a BOM? What about a fine-tuned LLM versus a novel foundation model? Create categories that map to your risk profile:


  • Tier 1 (Critical): Models touching sensitive data, making autonomous decisions, or directly exposed to end users
  • Tier 2 (Important): Models supporting business operations or informing decisions
  • Tier 3 (Low-risk): Experimental models, sandboxed systems, research only

    • Only Tier 1 systems need comprehensive BOMs initially. This keeps the effort manageable while protecting your highest-risk deployments.


    ### 2. Establish BOM Requirements in Vendor Contracts


    Security teams often have little leverage once a vendor is chosen. The time to demand AI BOM standards is during procurement. Update your vendor security questionnaires:


  • Require vendors to provide BOMs in standardized formats (look toward emerging standards like the NTIA's Minimum Elements for Software BOMs, adapted for AI)
  • Specify what information must be included—training data sources, known limitations, security testing results
  • Define update cadence: how frequently will BOMs be refreshed as models change?
  • Clarify what happens if vendors claim confidentiality—what's the minimum acceptable alternative?

    • For open-source models, many are already beginning to document training data and dependencies more transparently. Favor those when possible.


    ### 3. Build Internal Documentation Standards


    For models your organization builds or fine-tunes, standardize BOM creation. This includes:


  • AI Development Lifecycle: Require teams to document models at creation, not retroactively
  • Data Governance: Track where training data comes from, who owns it, what licenses apply
  • Model Registry: Maintain a central inventory of all models in use, along with their BOMs
  • Version Control: Treat model versions like code versions—each should have documentation of what changed

    • This isn't security theater. Internal documentation helps prevent the scenario where a team fine-tunes a model on sensitive data without realizing the implications, or where you discover a year later that a "trusted" vendor model was trained on data you specifically prohibit.


    ### 4. Create a BOM Review Process


    BOMs are only useful if security teams actually read and act on them. Establish a workflow:


    1. Intake: When a new AI system or model is proposed for deployment, the BOM is required

    2. Analysis: Assign ownership to someone (security architect, risk analyst) who evaluates the BOM against your risk criteria

    3. Decision: Approve as-is, approve with conditions (mitigating controls), or reject

    4. Monitoring: Revisit the decision when the model is updated or your risk tolerance changes


    Key questions to ask:

  • Where did the training data come from? Are there licensing or copyright issues?
  • Does the model exhibit documented biases or failure modes we can't accept?
  • Are dependencies up-to-date, or are we deploying with known vulnerabilities?
  • What's the inference cost, latency, and resource requirement? Can our infrastructure support it?
  • How will we detect if the model degrades or misbehaves in production?

    • ### 5. Actively Influence BOM Standards


    Don't just consume BOMs passively. CISOs should:


  • Participate in standards bodies: Groups like NIST, IEEE, and the Linux Foundation are developing AI documentation standards. Your input shapes what becomes normalized
  • Share feedback with vendors: Tell model providers what information you actually need to make decisions
  • Contribute to open-source efforts: If your organization uses open models, contribute to their documentation and BOM improvements
  • Collaborate with peers: Join industry groups focused on AI governance—shared requirements carry more weight with vendors

    • The standards you help establish today will determine whether AI BOMs become a genuine security tool or another checkbox compliance document.


    ## HackWire Analysis


    The push for AI BOMs represents a critical inflection point in how organizations will govern—or fail to govern—AI risk. But there's a dangerous gap between the vision of transparent, auditable AI systems and the reality of proprietary models whose vendors view their architectures and training data as trade secrets.


    The pattern here mirrors the fight over software SBOMs a decade ago, but with higher stakes. When companies resisted revealing their dependencies, regulators eventually forced compliance. We're seeing the early stages of that dynamic with AI: initial vendor cooperation with vague documentation, followed by regulatory pressure, followed eventually by meaningful transparency standards.


    What makes AI different is that the stakes touch more than just cybersecurity. A biased hiring model or a hallucinating medical recommendation system causes real harm. CISOs who passively wait for vendors to provide perfect BOMs will find themselves responsible for AI systems they never fully understood.


    The five preparations outlined above aren't theoretical. They're the difference between having BOM documents you can't act on and having a systematic way to make risk-informed decisions about AI deployment. CISOs who move now—defining requirements, updating contracts, building internal standards—will be the ones who actually influence how AI governance evolves. Those who wait will be reactive, audited, and often forced to deploy mitigations after problems appear.


    The window for CISOs to shape this landscape is open now. In two years, it will likely be closed.


    — HackWire Editorial


    ## Recommendations for Organizations


    Immediate Actions:

  • Audit your current AI/ML systems to identify which ones lack BOMs or adequate documentation
  • Add BOM requirements to your next vendor renewal negotiation
  • Start documenting models built in-house using a consistent template

    • Medium-term (3–6 months):

  • Establish formal review criteria for AI BOMs aligned with your risk tolerance
  • Build a model inventory and registry
  • Assign ownership for AI governance across security, product, and engineering teams

    • Long-term:

  • Monitor emerging standards from NIST, IEEE, and industry groups
  • Participate in standards-setting where your organization has relevant expertise
  • Establish a cadence for revisiting approved models as the threat landscape and your systems evolve

    • ---


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