OWASP AI Bill of Materials (AIBOM)

Summary

Just as Software Bill of Materials (SBOMs) revolutionized software supply chain transparency, the OWASP AI Bill of Materials project aims to do the same for AI systems. This emerging standard provides a structured format for documenting how AI models are built, trained, and deployed—covering everything from training data sources to model architecture to security configurations. For organizations deploying AI at scale, AIBOMs represent a critical tool for managing AI supply chain risk and meeting increasing regulatory transparency requirements.

Why AI Needs Its Own BOM Standard

Traditional SBOMs focus on software libraries and dependencies, but AI systems introduce unique transparency challenges:

• Training Data Provenance: Unlike software components with clear version numbers, training data may come from countless sources with varying licenses and quality levels
• Model Architecture Complexity: AI systems often combine multiple models, fine-tuning layers, and preprocessing pipelines that traditional BOMs can't capture
• Dynamic Behavior: AI systems can produce different outputs based on inputs in ways that static documentation struggles to represent
• Emerging Attack Vectors: Adversarial attacks, data poisoning, and prompt injection require security documentation beyond traditional vulnerability tracking

The AIBOM specification addresses these challenges by extending existing BOM formats (particularly CycloneDX) with AI-specific fields and relationships.

Core AIBOM Components

Model Provenance

• Base model identification and versioning
• Fine-tuning history and parameter modifications
• Training infrastructure and compute specifications
• Checkpoint and deployment version tracking

Training Data Documentation

• Data source identification and licensing
• Processing and transformation pipelines
• Quality metrics and bias assessments
• Data retention and access controls

Security Configuration

• Input validation and sanitization measures
• Guardrails and content filtering settings
• Access control and authentication requirements
• Incident response and rollback procedures

Operational Context

• Intended use cases and deployment environments
• Performance benchmarks and limitations
• Monitoring and logging configurations
• Update and maintenance schedules

Who This Resource Is For

• CISOs and Security Architects responsible for AI supply chain security who need standardized visibility into AI system composition
• Chief AI Officers establishing AI governance programs that require comprehensive system documentation
• Compliance Teams preparing for regulations like the EU AI Act that mandate AI system transparency
• MLOps Engineers managing AI model deployments who need to track versions, dependencies, and configurations
• Procurement Teams evaluating third-party AI solutions and needing to assess supply chain risk
• Auditors conducting AI system assessments who require standardized documentation formats

Implementation Roadmap

The OWASP AIBOM project provides both the specification and tooling:

Phase 1: Assessment

• Inventory existing AI systems and their components
• Identify gaps in current documentation practices
• Map regulatory requirements to AIBOM fields

Phase 2: Tool Integration

• Evaluate AIBOM generator tools (including the OWASP AIBOM Generator for Hugging Face models)
• Integrate AIBOM generation into MLOps pipelines
• Establish storage and versioning for AIBOM artifacts

Phase 3: Governance

• Define AIBOM review and approval workflows
• Establish update triggers and maintenance schedules
• Integrate with existing vulnerability management processes

The Regulatory Context

AIBOMs are increasingly relevant as AI transparency requirements tighten globally. The EU AI Act's technical documentation requirements align closely with AIBOM components, making early adoption a practical compliance strategy. Similarly, US federal agencies are exploring AI transparency mandates that could benefit from standardized documentation formats.

By adopting AIBOM practices now, organizations position themselves ahead of regulatory curves while gaining immediate operational benefits in AI system management and security.