This topic examines confidential computing platforms from Intel, AMD and the major cloud providers (AWS, Google Cloud, Azure) and how they support trustworthy AI by protecting data and models while in use. Confidential computing relies on hardware-backed trusted execution environments (TEEs) — e.g., Intel TDX and AMD SEV variants — plus cloud implementations such as Nitro-based enclaves and confidential VMs. Key capabilities to compare include attestation and identity, key management and HSM integration, isolation granularity, performance and devtooling, and how the platform integrates with AI stacks and governance controls. Relevance and timeliness (as of 2026-03-20): regulatory pressure, corporate risk policies, and broader enterprise adoption of large and private models have made protecting data-in-use essential. Organizations building agent platforms, contact-center AI, or embedding foundation models (examples: StackAI, Mistral, Kore.ai, Observe.AI, Microsoft 365 Copilot, Google Gemini) increasingly consider confidential compute to protect sensitive inputs, model IP, and to enable BYOM or hybrid hosting with verifiable isolation. Practical considerations covered here include trade-offs between security and latency, the complexity of remote attestation and supply-chain assurances, key lifecycle and KMS patterns, and integration with MLOps and governance tooling for policy, auditing, and observability. Readers will get a framework to compare vendor offerings by threat model (insider vs. cloud operator), deployment patterns (on-prem, hybrid, managed cloud), and operational maturity — helping security, privacy and AI governance teams choose platforms that align with compliance, performance, and integration requirements.