Confidential Computing

A cryptographic process that proves a TEE is genuine and running unmodified code. Before a client sends sensitive data, the enclave generates an attestation report signed by the hardware's root of trust. This allows a remote party to verify the enclave's identity and software hash, ensuring the environment is trustworthy before establishing a secure channel.

• Local Attestation: Verification between enclaves on the same platform.
• Remote Attestation: Verification by an external client or service.

Unlike standard encryption that protects data at rest (storage) and in transit (network), confidential computing encrypts data while it is being processed in memory. The CPU decrypts data exclusively within the TEE's encrypted memory region, preventing memory scraping attacks and exposure through cold boot attacks or compromised hypervisors. This closes the final gap in the data lifecycle protection.

The TEE enforces strict hardware-level memory isolation, carving out a private region of RAM that is inaccessible to any other software. Advanced implementations like AMD SEV-SNP add memory integrity protection, which cryptographically prevents the hypervisor from maliciously replaying or altering encrypted memory pages. This ensures both confidentiality and integrity of the computation.

Confidential computing dramatically shrinks the attack surface by removing the cloud provider, hypervisor, and host OS from the trust boundary. The Trusted Computing Base (TCB) is reduced to only the CPU hardware and the code running inside the enclave. This is critical for multi-tenant environments where a compromised hypervisor could traditionally expose all tenant data.

A primary enterprise use case where proprietary models and user prompts are protected during inference. The model weights and input data are decrypted only inside a GPU TEE, ensuring the AI service provider cannot see the user's query and the user cannot extract the model's intellectual property. This enables private, verifiable AI for regulated industries.

A secure area of a main processor that guarantees code and data loaded inside is protected with respect to confidentiality and integrity. The TEE provides hardware-enforced isolation that shields sensitive computations from the host operating system, hypervisor, and other privileged software.

• Intel SGX: Application-level enclaves with memory encryption
• AMD SEV-SNP: Full VM encryption with integrity protection
• ARM TrustZone: Split-world architecture for mobile and IoT

A TEE is the foundational hardware primitive that makes confidential computing possible.

The cryptographic process by which a TEE proves its identity and integrity to a remote party before that party entrusts it with secrets or proprietary data. Attestation generates a signed measurement of the enclave's code and state that can be verified against a known good configuration.

• Verifies the enclave is running unmodified, genuine code
• Prevents man-in-the-middle attacks during data provisioning
• Relies on a chain of trust anchored in the hardware manufacturer

Without attestation, a client cannot distinguish a genuine TEE from a malicious emulator.

A hardware component integrated into the memory controller that transparently encrypts and decrypts data as it moves between the processor and main memory. This prevents physical bus snooping and cold boot attacks from extracting plaintext data.

• Operates at line speed with minimal latency overhead
• Uses ephemeral keys generated per boot cycle
• Protects against physical attackers with DRAM access

The memory encryption engine ensures that even if an attacker physically probes the memory bus, they only recover ciphertext.

A hardware-isolated execution context within a processor that operates independently of the main operating system. The enclave's memory region is encrypted and inaccessible to all other software, including the kernel and hypervisor.

• Host OS cannot read enclave memory or modify its execution
• Communication with the enclave occurs through defined interfaces
• Enclave code is measured and verified during initialization

Secure enclaves are the execution sandbox where proprietary AI models perform inference on sensitive enterprise data without exposure to the cloud provider.

The protection of data while it is actively being processed in CPU registers, cache, and memory. This is the third pillar of the encryption lifecycle, complementing encryption at rest (storage) and encryption in transit (network).

• Traditional encryption only protects idle or moving data
• Data-in-use encryption closes the last gap in the security lifecycle
• Enables processing of sensitive data in untrusted environments

This capability is what allows enterprises to run proprietary AI workloads on shared cloud infrastructure without exposing intellectual property to the infrastructure operator.

The application of confidential computing to protect both the model weights and the input data during AI inference. The model runs inside a TEE, ensuring that neither the cloud provider nor any unauthorized party can extract the proprietary model or the user's query.

• Protects intellectual property embedded in model weights
• Ensures user prompt privacy during inference
• Enables multi-party computation where multiple organizations contribute encrypted data

Confidential AI inference is the direct use case for running fine-tuned enterprise models on third-party infrastructure without exposing sensitive business logic.