A model registry is a server-managed system for storing, versioning, organizing, and governing machine learning models. It tracks model artifacts, metadata, aliases, approvals, and deployment-ready versions so teams can manage models as shared assets rather than loose files.
In cyber security, a registry matters because it often sits inside the control plane of an AI platform and may handle trusted server-side operations. If its APIs fetch remote resources, validate package locations, or process model references without strict checks, attackers can turn that logic into server-side request forgery, internal scanning, or data exposure. Defenders should treat registry inputs as untrusted, enforce destination allowlists, isolate network access, and log unusual outbound requests. A secure registry helps govern models; a weak one can become a pivot point into internal services or cloud metadata endpoints.