Episode 26 — Supply Chain & Artifacts

This episode examines supply chain and artifact security, focusing on how external dependencies and stored components create systemic risks in AI systems. Artifacts include datasets, model weights, configuration files, and container images, each of which must be treated as high-value assets. For certification purposes, learners must be able to define supply chain risk in the AI context and explain how compromised datasets or libraries propagate vulnerabilities downstream. Exam questions often emphasize provenance, attestation, and the importance of verifying that all artifacts come from trusted, validated sources. Understanding the breadth of supply chain risk is essential to recognizing why AI systems require unique approaches compared to traditional applications.

In practical application, this episode explores scenarios such as poisoned community datasets, tampered pre-trained models downloaded from open repositories, or malicious dependencies in machine learning libraries. Best practices include generating software bills of materials (SBOM) or model bills of materials (MBOM), applying cryptographic signatures to artifacts, and maintaining auditable provenance records. Troubleshooting considerations highlight the difficulty of detecting hidden backdoors or ensuring reproducibility when artifacts are poorly documented. For exam readiness, learners must be able to describe the interplay between artifact management, vendor oversight, and organizational governance frameworks. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.