Google Cloud Confidential VMs
Google Cloud’s Confidential Computing delivers hardware-based Trusted Execution Environments to encrypt data in use, completing the encryption lifecycle alongside data at rest and in transit. It includes Confidential VMs (using AMD SEV, SEV-SNP, Intel TDX, and NVIDIA confidential GPUs), Confidential Space (enabling secure multi-party data sharing), Google Cloud Attestation, and split-trust encryption tooling. Confidential VMs support workloads in Compute Engine and are available across services such as Dataproc, Dataflow, GKE, and Gemini Enterprise Agent Platform Notebooks. It ensures runtime encryption of memory, isolation from host OS/hypervisor, and attestation features so customers gain proof that their workloads run in a secure enclave. Use cases range from confidential analytics and federated learning in healthcare and finance to generative-AI model hosting and collaborative supply-chain data sharing.
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vp.net
vp.net is a privacy-focused VPN service built on a zero-knowledge architecture and hardware-enforced security that delivers verifiable, end-to-end encrypted connections in which even the service provider cannot identify user activity. It leverages Intel SGX enclaves and attestation services to ensure code execution is genuine and auditable, offering users immutable proof that no logs are maintained and no user data is splintered from their secured session. Performance is optimized via advanced packet-routing technology, which claims markedly faster speeds compared to competitors, while full control is retained locally on the device, ensuring network traffic is anonymized, and any metadata collection is cryptographically impossible. It is designed so that the only entity with visibility into a user’s session is the user themselves, and operations are transparent and verifiable rather than simply promised.
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Fortanix Confidential AI
Fortanix Confidential AI is a unified platform that enables data teams to process sensitive datasets and run AI/ML models entirely within confidential computing environments, combining managed infrastructure, software, and workflow orchestration to maintain organizational privacy compliance. The service offers readily available, on-demand infrastructure powered by Intel Ice Lake third-generation scalable Xeon processors and supports execution of AI frameworks inside Intel SGX and other enclave technologies with zero external visibility. It delivers hardware-backed proofs of execution and detailed audit logs for stringent regulatory requirements, secures every stage of the MLOps pipeline, from data ingestion via Amazon S3 connectors or local uploads through model training, inference, and fine-tuning, and provides broad model compatibility.
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Tinfoil
Tinfoil is a verifiably private AI platform built to deliver zero-trust, zero-data-retention inference by running open-source or custom models inside secure hardware enclaves in the cloud, giving you the data-privacy assurances of on-premises systems with the scalability and convenience of the cloud. All user inputs and inference operations are processed in confidential-computing environments so that no one, not even Tinfoil or the cloud provider, can access or retain your data. It supports private chat, private data analysis, user-trained fine-tuning, and an OpenAI-compatible inference API, covers workloads such as AI agents, private content moderation, and proprietary code models, and provides features like public verification of enclave attestation, “provable zero data access,” and full compatibility with major open source models.
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