Creating AI applications shares common challenges with traditional software development, such as difficulties in model production, infrastructure complexities, and security issues. The JFrog Platform, with its new advanced model registry capabilities and the FrogML SDK, aims to integrate AI/ML workflows with standard DevOps and Security frameworks, providing a single source of truth across MLOps and DevSecOps. The Machine Learning Repository and FrogML SDK allow AI/ML artifacts to coexist with traditional software artifacts, thereby applying mature development controls to AI development. The FrogML SDK, a lightweight Python library, facilitates model management and integrates seamlessly with JFrog Artifactory, supporting various machine learning frameworks like Catboost, HuggingFace, and PyTorch. This integration allows data science teams to maintain their preferred toolchains while adopting enterprise-level development standards, or alternatively, to opt for the all-in-one JFrog ML solution that simplifies testing, experimentation, and model deployment. By uniting DevOps, DevSecOps, and MLOps, the JFrog Platform aims to instill trust in AI/ML workflows and align AI development with traditional enterprise practices.