Today's confidential computing hardware provides the fundamental building blocks for data privacy in the cloud. However, current solutions built on this technology fail to deliver the level of security or the performance needed, while still demanding prohibitive resources. This project identifies the root cause as the inappropriate application of software abstractions originally designed for traditional computing environments to confidential computing contexts. Its goal is to evolve these abstractions to support elastic confidential computing and translate research outcomes into practical, widely accessible learning opportunities that position confidential computing as a first-order software design principle rather than an afterthought. The project's novelty lies in identifying the key primitives missing from confidential computing for elastic cloud settings and designing secure and automated mechanisms to realize them. Beyond advancing a technology capable of transforming data privacy and accelerating growth in the public cloud domain, the project's broader impact and significance also stem from coordinated translational efforts with the confidential computing industry. This project advances confidential computing through four innovations. First, it develops a compiler-driven analysis and validation framework to automate the adoption of trustworthy isolation primitives within Confidential Virtual Machines (VMs). Second, it introduces a multi-process Library operating s