Understanding the Shift
AI Bill of Materials (BOM) documentation is emerging rapidly, but many security programs are unprepared to utilize it effectively. The idea is simple: catalog AI components in your systems as you do with software dependencies. However, the challenge lies in the fact that existing BOM processes assume static libraries with CVE databases, which don't apply to AI components.
The real issue isn't just the need for AI BOMs—it's that your current risk assessment frameworks require fundamental changes to process them effectively.
Key Challenges
Traditional Tools Can't Assess AI Component Risks
Standard software composition analysis (SCA) tools identify known vulnerabilities by matching package versions against CVE databases. AI BOMs, however, involve different risk factors: training data sources, model architecture, inference endpoint configurations, and bias test results. These don't align with CVE identifiers. Relying on existing tools without adaptation will lead to ineffective risk management.
Fragmentation of AI BOM Standards
Various groups are defining AI BOM standards, focusing on different aspects like model cards, dataset lineage, and runtime dependencies. This isn't just a technical issue—it's strategic. The prevailing standard will dictate which risks are measurable and which remain hidden. Your input is crucial now, before standards become fixed.
Unclear Risk Ownership for AI Components
In traditional software, your AppSec team manages third-party library vulnerabilities. But with AI models, who handles bias testing, drift monitoring, or adversarial robustness verification? Most organizations haven't assigned these responsibilities, and AI BOMs will highlight components that lack clear evaluation ownership.
Compliance Frameworks Outpacing Guidance
The NIST Cybersecurity Framework v2.0 and ISO/IEC 27001:2022 have added AI-specific requirements. However, they don't explain how to convert an AI BOM into control evidence. You'll need to map AI component metadata to existing control frameworks, a process that is still largely undefined.
Vendor Questionnaires Lack AI Focus
You likely request SBOMs and security testing practices from vendors. But do you inquire about the datasets that trained their models, their testing for prompt injection, or their model versioning? AI BOMs can provide this data if you know what to request and how to interpret the answers.
Implications for Your Team
There's a gap between documentation and actionable insights. Even with perfect AI BOMs, your team may not know how to utilize them effectively. The issue isn't data availability—it's the capacity to analyze it.
Consider an AI BOM listing a model trained on public internet data from 2019-2021. Is this a risk? It depends on your use case, data sensitivity, regulatory obligations, and tolerance for outdated information. Your current SBOM review process likely lacks a decision framework for such scenarios.
This gap presents an opportunity. Organizations that develop AI risk assessment capabilities now will set industry standards. You can either wait for consensus or proactively create your own frameworks and influence the standards process.
Action Steps
1. Assign AI Component Ownership
Don't wait for perfect AI BOMs. Map your current AI usage—every API call to an LLM service, every embedded model, every AI-powered feature. Assign specific teams or roles to evaluate risks for each category. Update your RACI matrix to include "AI model risk assessment" as a distinct responsibility.
2. Enhance Vendor Assessment Processes
Incorporate AI-specific questions into your security questionnaires. Ask vendors about the data that trained their models, their bias testing methods, model versioning and rollback processes, drift monitoring, and adversarial testing. These questions are relevant even without a formal AI BOM.
3. Integrate AI Risks into Existing Frameworks
Use compliance frameworks you already report against, such as SOC 2 Type II, ISO/IEC 27001:2022, or PCI DSS v4.0.1 if you handle payments. Identify which controls apply to AI components and document these mappings before your auditor asks.
4. Participate in Standards Development
Join industry working groups defining AI BOM formats. Provide feedback on draft standards. Your participation ensures that final standards meet real security program needs.
5. Develop AI Risk Assessment Runbooks
Create decision frameworks for common AI BOM scenarios. Determine additional controls for models trained on public data and decide how to handle vendors that can't provide training data provenance. Document these decisions now to prepare for future implications.
By addressing these areas, your team can effectively integrate AI BOMs into your risk management strategy and lead the way in shaping industry standards.
