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UID:pretalx-devconf-us-2026-KPN7LF@pretalx.devconf.info
DTSTART;TZID=EST:20260925T102000
DTEND;TZID=EST:20260925T105500
DESCRIPTION:Software Bill of Materials (SBOM) has become a foundational req
 uirement for software supply chain security. But modern applications incre
 asingly embed machine learning models\, datasets\, feature stores\, prompt
 s\, vector databases\, and training pipeline artifacts that traditional SB
 OMs were never designed to describe.\nThis talk explores extending SBOM co
 ncepts into an **AI Bill of Materials (AI-BOM)** that captures machine lea
 rning artifacts across the lifecycle.\n\nWe will examine:\n* Why SBOM alon
 e is insufficient for ML systems\n* What additional metadata is required f
 or AI systems\n* Mapping ML artifacts into CycloneDX/SPDX extensions\n* Pr
 ovenance\, reproducibility\, and compliance challenges\n* Security risks i
 n models\, datasets\, and prompt supply chains\n* How AI-BOM supports gove
 rnance\, auditability\, and responsible AI\nThe session includes a practic
 al architecture walkthrough and a focused demo showing how AI-BOM artifact
 s can be generated and integrated into existing DevSecOps workflows.
DTSTAMP:20260727T165130Z
LOCATION:106 (Capacity 45)
SUMMARY:Extending SBOM into AI-BOM: Managing ML Artifacts Beyond Traditiona
 l Software - Ayushi Tiwari
URL:https://pretalx.devconf.info/devconf-us-2026/talk/KPN7LF/
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