Confidential AI SDK

Provides a unified programming model across Intel SGX, Intel TDX, AMD SEV-SNP, and ARM CCA backends. The SDK normalizes attestation flows and memory management, allowing a single codebase to target multiple TEE architectures without vendor lock-in.

• Abstracts CPU-specific enclave creation and sealing
• Normalizes measurement and attestation across hardware
• Supports NVIDIA Confidential Computing for GPU workloads

Built-in libraries that handle the cryptographic verification of the Trusted Execution Environment before releasing secrets or loading models. The SDK manages the full attestation handshake with Enclave-Aware Key Management Services.

• Integrates with SPIRE for SPIFFE-based workload identity
• Verifies Enclave Measurement against known good hashes
• Supports remote attestation to third-party relying parties

APIs for encrypting model weights at rest and loading them only into verified enclaves. Enclave Sealing binds encrypted model artifacts to a specific enclave identity, preventing decryption on unauthorized platforms.

• Implements Model Provenance Attestation to verify training lineage
• Supports encrypted model distribution from Private Container Registries
• Integrates with Tamper-Proof Model Registries for audit trails

A hardened model serving engine that runs entirely within the enclave boundary. Client inputs, model parameters, and generated outputs never leave the encrypted memory region in plaintext.

• Supports standard serving protocols (gRPC, REST) with TLS termination inside the enclave
• Implements Side-Channel Resistance for timing and cache attacks
• Compatible with Confidential Retrieval-Augmented Generation pipelines

Structured logging and metrics export that respects the confidentiality boundary. Sensitive data is redacted or encrypted before leaving the enclave, while operational metrics remain visible for Agentic Observability.

• Differential privacy applied to telemetry streams
• Encrypted log shipping to Confidential Persistent Storage
• Integrates with existing observability stacks without leaking plaintext data

Optional libraries enabling collaborative AI scenarios where multiple distrusting parties contribute data or models to a computation without revealing their inputs to each other.

• Supports Federated Model Aggregation within enclaves
• Enables secure multi-tenant inference with hardware-enforced isolation
• Integrates with Confidential Service Mesh for cross-enclave communication

The cryptographic handshake that establishes trust before any AI workload executes. Attestation is the process of cryptographically verifying the identity and integrity of a TEE, producing a signed measurement that proves the exact hardware, firmware, and software stack has not been tampered with.

• Local Attestation: Verification between enclaves on the same platform
• Remote Attestation: Verification by a remote client or key management service
• Critical Output: An enclave measurement hash that serves as a unique identity fingerprint

The mechanism that bridges volatile secure computation with persistent storage. Enclave sealing allows a TEE to encrypt data for storage by binding it to a specific enclave identity. The sealed data can only be decrypted by the exact same application on the exact same platform, preventing offline analysis of model weights or cached inference results.

• Sealing Policy: Bind to enclave identity or signing authority
• Use Case: Securely caching model parameters to disk between sessions

A virtual machine instance backed by hardware-based memory encryption that isolates entire operating systems from the cloud provider. Unlike process-level enclaves, CVMs protect full workloads without code modification. Technologies include AMD SEV-SNP, Intel TDX, and AWS Nitro Enclaves.

• Lift-and-Shift: Run unmodified AI applications in a secure bubble
• Hypervisor Isolation: Cloud operator cannot inspect VM memory
• Live Migration: Secure relocation between physical hosts without decryption

A RAG architecture where both the retrieval of context documents and the generation of the response occur entirely within a TEE. This ensures that proprietary knowledge base queries, retrieved chunks, and the final output are never exposed to the infrastructure provider.

• Protected Pipeline: Query → Vector Search → Context Assembly → Generation
• Key Benefit: Enterprise knowledge bases remain encrypted during semantic search
• Integration: Combines encrypted vector databases with confidential inference

A KMS that integrates directly with TEE attestation protocols, releasing decryption keys only after successful verification of the enclave's identity and integrity. This ensures that model weights and API secrets are never exposed to untrusted environments.

• Conditional Release: Keys gated on valid attestation reports
• Integration: Works with HashiCorp Vault, Azure Key Vault, and custom KMS
• Critical Role: Protects model decryption keys from host-level access