Google Cloud's Confidential Computing offers hardware-based Trusted Execution Environments (TEEs) that encrypt data while it is actively being used, thus completing the encryption process for data both at rest and in transit. This suite includes Confidential VMs, which utilize AMD SEV, SEV-SNP, Intel TDX, and NVIDIA confidential GPUs, alongside Confidential Space facilitating secure multi-party data sharing, Google Cloud Attestation, and split-trust encryption tools. Confidential VMs are designed to support workloads within Compute Engine and are applicable across various services such as Dataproc, Dataflow, GKE, and Vertex AI Workbench. The underlying architecture guarantees that memory is encrypted during runtime, isolates workloads from the host operating system and hypervisor, and includes attestation features that provide customers with proof of operation within a secure enclave. Use cases are diverse, spanning confidential analytics, federated learning in sectors like healthcare and finance, generative AI model deployment, and collaborative data sharing in supply chains. Ultimately, this innovative approach minimizes the trust boundary to only the guest application rather than the entire computing environment, enhancing overall security and privacy for sensitive workloads.