Trusted AI Adoption (Part 1): Consolidation
Blog post from JFrog
In the realm of modern software development, a stark contrast exists between the well-governed software supply chain and the chaotic AI supply chain, often leading to security vulnerabilities and governance issues. While traditional software development follows a secure and automated process with CI/CD pipelines and rigorous scanning, AI development frequently operates in a disconnected manner, with developers using public hubs and local machines, creating a "shadow supply chain." This blog post, the first of a five-part series, addresses the need for consolidation as the initial step towards trusted AI adoption. By merging AI and software assets into a single, governed lifecycle, companies can eliminate off-road practices, ensuring AI models and components are treated with the same security and governance as traditional software. This consolidation involves using centralized proxies for model downloads, creating an AI registry, and linking AI components to enterprise software for full traceability, ultimately transforming governance from a hindrance into a competitive advantage. The post sets the stage for further exploration of managing hidden AI connections in future installments of the series.
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