Glossary

Confidential VM

A full virtual machine instance running inside a hardware-backed Trusted Execution Environment, encrypting its entire memory space to protect against unauthorized access from the hypervisor or cloud operator.

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HARDWARE-ENFORCED VIRTUAL MACHINE SECURITY

What is a Confidential VM?

A Confidential VM is a full virtual machine instance whose entire memory space is encrypted and integrity-protected by a hardware-backed Trusted Execution Environment, shielding all data and applications from unauthorized access by the hypervisor, host operating system, or cloud provider.

A Confidential VM extends the isolation guarantees of Confidential Computing to an entire virtual machine, enabling a lift-and-shift migration of existing workloads without code modification. Unlike process-based enclaves, a Confidential VM encrypts all memory pages using hardware-managed keys, ensuring that even a compromised hypervisor cannot inspect or tamper with the guest's runtime state. Technologies such as AMD SEV-SNP, Intel TDX, and ARM CCA implement this by adding integrity protection and replay prevention to the encrypted memory, creating a cryptographically isolated execution environment that protects data-in-use.

The trustworthiness of a Confidential VM is established through remote attestation, where the hardware generates a signed report of the VM's initial firmware, kernel, and configuration measurements. A remote verifier validates this attestation evidence against a known-good policy before provisioning secrets or allowing the VM to process sensitive data. This mechanism creates a Hardware Root of Trust that decouples security from the cloud operator, making Confidential VMs foundational for Confidential AI, Private Inference, and regulated multi-party data collaboration.

HARDWARE-ENFORCED PROTECTION

Key Features of Confidential VMs

Confidential VMs extend Trusted Execution Environment guarantees to entire virtual machine instances, encrypting all memory and CPU state to protect data-in-use from the hypervisor, host OS, and cloud operator.

Full Memory Encryption

The entire VM memory space is transparently encrypted by a hardware memory controller. Data remains encrypted even when loaded into RAM, preventing the hypervisor or a rogue administrator from dumping plaintext memory. This is implemented via AES-XTS encryption engines integrated into the memory path, with keys generated at boot and never exposed to software.

256-bit

AES-XTS Key Strength

Hardware-Based Isolation

Confidential VMs leverage CPU extensions to create a cryptographically isolated execution environment. Technologies include:

AMD SEV-SNP: Adds memory integrity protection to prevent replay and remapping attacks
Intel TDX: Provides VM-level isolation with a secure-arbitration mode that enforces access controls
ARM CCA: Introduces Realm worlds that isolate from both the hypervisor and other VMs

Lift-and-Shift Compatibility

Unlike process-based enclaves that require code refactoring, Confidential VMs run unmodified operating systems and applications. This enables legacy workloads to gain data-in-use protection without recompilation. The guest OS boots inside the encrypted context, and all processes inherit the protection automatically.

Cryptographic Attestation

Before provisioning secrets or trusting a workload, a remote party can verify the VM's identity and integrity through remote attestation. The hardware generates a signed report containing:

Launch measurement: A cryptographic hash of the VM's initial state
Platform endorsement: Proof that the hardware is genuine and firmware is up-to-date This establishes a hardware root of trust before any data is exchanged.

Live Migration with Security

Confidential VMs support secure live migration between physical hosts while maintaining encryption. The source platform establishes an attested, encrypted channel with the destination, transfers the VM's memory state and encryption keys, and the destination re-seals the VM into its own hardware context without exposing plaintext at any point.

Confidential AI Workloads

When combined with NVIDIA Confidential Computing GPUs, Confidential VMs protect the entire AI pipeline:

Model weights remain encrypted during inference
Training data is shielded from the infrastructure provider
Client inputs are never exposed to the cloud operator This enables private inference and secure multi-party model training without trusting the platform.

CONFIDENTIAL VM

Frequently Asked Questions

Clear, technically precise answers to the most common questions about Confidential Virtual Machines, their hardware underpinnings, and their role in protecting data in use.

A Confidential VM is a full virtual machine instance that executes inside a hardware-backed Trusted Execution Environment (TEE) , encrypting its entire memory space to protect data and code from unauthorized access by the hypervisor, host operating system, or cloud provider. It works by leveraging CPU extensions like AMD SEV-SNP, Intel TDX, or ARM CCA that create a cryptographically isolated memory region for the entire guest OS. The processor encrypts all data as it moves between the CPU and RAM, ensuring that even a compromised hypervisor cannot read the VM's state. This enables a 'lift-and-shift' migration of existing workloads into a confidential computing environment without modifying the application code, providing data-in-use protection for sensitive workloads in untrusted cloud environments.

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CONFIDENTIAL COMPUTING ECOSYSTEM

Related Terms

Confidential VMs operate within a broader ecosystem of hardware and software technologies. Understanding these adjacent concepts is essential for architects designing secure, attested compute environments.

Trusted Execution Environment (TEE)

A hardware-enforced secure area within a main processor that guarantees the confidentiality and integrity of code and data loaded inside. The TEE isolates sensitive computations from the host operating system, hypervisor, and other applications.

Provides runtime isolation for data-in-use
Forms the foundational hardware layer for Confidential VMs
Examples include Intel SGX, AMD SEV, and ARM TrustZone

EXPLORE

Attestation

The cryptographic process by which a TEE proves its identity, integrity, and security posture to a remote relying party before secrets are provisioned. Attestation establishes a hardware-rooted chain of trust.

Verifies the exact hash of the VM's firmware and initial state
Prevents spoofing of the confidential environment
Enables zero-trust deployment where even the cloud operator is untrusted

Intel TDX

Intel Trust Domain Extensions is a hardware-isolated, VM-level TEE that extends Confidential Computing to entire virtual machines. It enables lift-and-shift migration of legacy workloads without code modification.

Encrypts the full VM memory space transparently
Protects against malicious hypervisor attacks
Designed for multi-tenant cloud environments where the VMM is outside the trust boundary

AMD SEV-SNP

Secure Encrypted Virtualization with Secure Nested Paging adds strong memory integrity protection to AMD's encrypted VM technology. It prevents hypervisor-based attacks like data replay and memory remapping.

Provides cryptographic isolation between VMs
Includes reverse map table to block page table manipulation
Enables confidential containers and confidential VMs on AMD EPYC processors

Remote Attestation

A protocol enabling a TEE on one machine to prove its identity and software integrity to a remote verifier. This establishes a cryptographic trust anchor for distributed confidential workloads.

Uses signed measurements of the VM's initial state
Integrates with Key Management Services to conditionally release secrets
Essential for multi-party confidential computing scenarios

Confidential AI

The application of Confidential Computing hardware to protect the confidentiality and integrity of machine learning models, training data, and inference inputs during active computation.

Protects proprietary model weights from cloud operators
Ensures client data privacy during inference
Enables secure multi-party training on sensitive datasets without exposing raw data

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.

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