构建AI物料清单，实现模型来源可验证、生命周期可追溯。
Operationalising Artificial Intelligence Bills of Materials (AIBOMs) for Verifiable AI Provenance and Lifecycle Assurance
- 扩展CycloneDX标准，建立AI专用的物料清单框架。
- 98.7%复现准确率，96.2%漏洞匹配精度，人工干预减少63%。
- 适合关注AI安全与合规的开发者和监管机构使用。
人工智能系统日益依赖复杂的多层软件供应链，带来可复现性、透明度和安全保证方面的挑战。本文提出一种人工智能物料清单（AIBOM）框架，扩展CycloneDX标准，以捕获AI特有的来源信息、模型谱系和披露元数据。该框架通过结构化模式设计、密码学验证与代理驱动自动化，实现可验证的软件来源追溯。开发了一个自主AI流水线，持续进行环境检测、漏洞增强与复现性审计，基于机器可验证的来源链。实证评估显示，在容器化分析工作流中，复现保真度达98.7%，漏洞匹配精度为96.2%，人工监督减少63%。结果证实了自动化来源保障与可复现的AI生命周期验证的可行性。AIBOM框架推动了软件供应链透明度与AI可复现性工程的科学基础，提供了一种通用方法，用于保障AI系统安全、强化来源完整性，并支持符合国际信息安全标准。
原文摘要 · Abstract (English)
Artificial Intelligence (AI) systems are increasingly dependent on complex, multi-layered software supply chains that introduce challenges for reproducibility, transparency, and security assurance. This study presents an Artificial Intelligence Bill of Materials (AIBOM) schema extending the CycloneDX standard to capture AI-specific provenance, model lineage, and disclosure metadata. The framework provides a formalised approach to verifiable software provenance through structured schema engineering, cryptographic validation, and agent-driven automation. An autonomous AI pipeline is developed to perform continuous environment inspection, vulnerability enrichment, and reproducibility auditing using machine-verifiable provenance chains. Empirical evaluation demonstrates 98.7% reproducibility fidelity, 96.2% vulnerability match precision, and a 63% reduction in manual oversight across containerised analytic workflows. These results confirm the feasibility of automated provenance assurance and reproducible AI lifecycle validation. The AIBOM framework advances the scientific foundations of software supply chain transparency and AI reproducibility engineering, offering a generalisable methodology for securing AI systems, strengthening provenance integrity, and supporting compliance with international information security standards.
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