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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greymatter.io
Maximize your resources. Ensure optimal use of your clouds, platforms, and software. This is application and API network operations management redefined. The same governance rules, observability, auditing, and policy control for every application, API, and network across your multi-cloud, data center and edge environments, all in one place. Zero-trust micro-segmentation, omni-directional traffic splitting, infrastructure agnostic attestation, and traffic management to secure your resources. IT-informed decision-making is real. Application, API & network monitoring and control generate massive IT operations data. Use it in real time through AI. Logging, metrics, tracing, and audits through Grey Matter simplifies integration and standardizes aggregation for all IT Operations data. Fully leverage your mesh telemetry and securely and flexibly future-proof your hybrid infrastructure.
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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 Vertex AI Workbench. 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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NVIDIA Confidential Computing
NVIDIA Confidential Computing secures data in use, protecting AI models and workloads as they execute, by leveraging hardware-based trusted execution environments built into NVIDIA Hopper and Blackwell architectures and supported platforms. It enables enterprises to deploy AI training and inference, whether on-premises, in the cloud, or at the edge, with no changes to model code, while ensuring the confidentiality and integrity of both data and models. Key features include zero-trust isolation of workloads from the host OS or hypervisor, device attestation to verify that only legitimate NVIDIA hardware is running the code, and full compatibility with shared or remote infrastructure for ISVs, enterprises, and multi-tenant environments. By safeguarding proprietary AI models, inputs, weights, and inference activities, NVIDIA Confidential Computing enables high-performance AI without compromising security or performance.
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