AI-Driven Supply Chain Security: Using AI-BOM to Detect Hidden Vulnerabilities
summary
Claims that an AI-BOM engine supplements SBOM by cataloging models, training data, prompts, agents, APIs and cloud resources, then correlates them into an AI dependency graph and supply-chain risk map that flags hidden vulnerabilities (example: npm malware and Copilot prompt injection) and speeds incident response and cost reduction. Implementation follows a seven-step implementation starting as simply as a spreadsheet, then layering runtime discovery and protection tools; market is fragmented and most organizations lack implementers.
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chapters (19)
- 0:00Introduction
- 1:35Security experience
- 3:00Security critical for orgs
- 4:27SBOM vs AI-BOM
- 6:15SBOM limitations in AI
- 8:24Runtime changes in AI
- 8:46AI-BOM components
- 10:56AI-BOM governance
- 12:19Developers first defense
- 13:43Accountability in AI
- 14:26AI-BOM risk assessment
- 15:29AI supply chain risks
- 16:30AI-BOM business value
- 16:50Seven steps to AI-BOM
- 18:35Developers securing AI
- 19:38AI security incidents
- 20:41AI-BOM tools
- 21:41Key takeaways
- 22:45Q&A
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