Shadow AI is often discussed as a developer tooling problem: unauthorized AI IDEs, local LLM runtimes, or browser extensions installed without approval. These are real issues, but they are not the hardest ones. In most corporate environments, these tools still execute on managed endpoints, traverse monitored networks, and leave traces that security teams can observe. The harder problem lives outside that boundary. Today, non-developers across business, HR, finance, and operations are using AI systems to build and deploy real applications without touching corporate infrastructure at all. A personal credit card SaaS subscription, an AI assisted code generator, and a one-click deploy platform like Vercel, Railway, or similar are enough to put a production service online in hours. These services often end up powering real workflows, either through shared URLs, embedded dashboards, or even custom domains mapped into corporate DNS. From a security perspective, these artifacts are invisible. There is no endpoint telemetry, no CI pipeline, no SBOM, no asset inventory, and no clear ownership. The organization does not know what code was generated, which libraries were pulled in, which models were used, where data flows, or what happens when the subscription expires or the platform changes behavior. This talk reframes Shadow AI as a dual visibility problem. On one side are AI enabled developer tools that introduce new but measurable supply chain risks. On the other are AI generated and AI deployed artifacts that exist entirely outside organizational control, yet still process business data and influence decisions. Rather than proposing bans or policy-first responses, this session focuses on practical detection and response strategies. Attendees will learn how to think about Shadow AI as an asset discovery and supply chain visibility problem, how to identify signals of externally deployed AI artifacts, and how to create engagement models that bring these systems back into view before they become business critical liabilities.