What Is a Confidential VM? A Complete Guide to Confidential Virtual Machines
Meta Description: Confidential VMs are virtual machines that use hardware-based encryption to protect data in use. Learn how confidential virtual machines work, their benefits, and when to use them.
Target Keywords: confidential VM, confidential virtual machine, AMD SEV, Intel TDX, secure VM, encrypted virtual machine, confidential computing VM
Reading Time: 14 minutes
TL;DR - What Is a Confidential VM?
A Confidential Virtual Machine (Confidential VM) is a virtual machine that runs inside a hardware-protected Trusted Execution Environment (TEE). It encrypts the VM’s memory, code, and runtime state throughout its entire lifecycle — ensuring that data remains protected even while being processed. This prevents unauthorized access from cloud providers, system administrators, or infrastructure-level attacks, enabling truly private workloads in public or multi-tenant clouds.
Key Points:
- Confidential VMs encrypt memory at the hardware level using CPU-based TEE technologies
- They protect against privileged access attacks, including from hypervisor and cloud administrators
- Major implementations: AMD SEV-SNP, Intel TDX, and ARM CCA
- Use cases: Multi-tenant cloud, regulated industries, sovereign cloud, sensitive AI workloads
- Unlike traditional VMs, confidential VMs provide cryptographic proof of their security state through remote attestation
What Is a Confidential Virtual Machine?
The Problem with Traditional Virtual Machines
Traditional virtual machines (VMs) have been the cornerstone of cloud computing for decades. They provide isolation between workloads running on the same physical hardware, allowing multiple tenants to share infrastructure efficiently. However, this isolation has a critical weakness: the hypervisor and cloud administrators have unrestricted access to the VM’s memory and data.
In a conventional cloud environment:
- The hypervisor can read all VM memory
- Cloud administrators can snapshot VM state
- Infrastructure operators can inspect data in transit between VMs
- Malicious insiders or compromised admin accounts pose significant risks
For organizations handling sensitive data—healthcare records, financial transactions, government secrets, proprietary AI models—this privileged access model is unacceptable.
How Confidential VMs Solve This Problem
A Confidential Virtual Machine fundamentally changes the trust model of cloud computing by leveraging hardware-based security features built into modern CPUs. Instead of trusting the cloud provider, hypervisor, and administrators, you only need to trust:
- The CPU vendor’s hardware (AMD, Intel, ARM)
- The cryptographic attestation process
- Your own code and data
Confidential VMs achieve this by:
- Hardware-Level Memory Encryption: The CPU automatically encrypts all VM memory using encryption keys that only the VM can access. Even the hypervisor sees only encrypted data.
- Secure Boot and Attestation: The VM boots in a cryptographically verified state, and you can remotely verify that the VM is running unmodified code before sending it sensitive data.
- Isolated Execution: The hypervisor manages resource allocation but cannot inspect or modify the VM’s internal state.
- State Protection: VM snapshots, migrations, and saved states are encrypted, preventing offline attacks.
Think of a confidential VM like a secure vault inside a bank. The bank (cloud provider) controls the building and utilities, but only you have the combination to the vault. Even bank employees cannot access your contents.
How Do Confidential VMs Work?
Architecture Overview
Confidential VMs rely on CPU-based Trusted Execution Environments (TEEs) designed specifically for virtualization workloads. Here’s a simplified architecture:
Key Technologies Behind Confidential VMs
- AMD SEV-SNP: Provides memory encryption and integrity protection. Widely used in multi-tenant cloud infrastructure.
- Intel TDX: Creates isolated “Trust Domains” with unique encryption keys, offering strong isolation for confidential cloud computing.
- ARM CCA: Supports isolated execution environments called “Realms” with dynamic memory encryption.
Confidential VMs vs. Traditional VMs vs. Application Enclaves
Understanding when to use confidential VMs versus other isolation technologies is crucial:
| Feature | Traditional VM | Application Enclave | Confidential VM |
| Isolation Level | Hypervisor-enforced | Process-level | Hardware-enforced VM |
| Trusted Computing Base | Hypervisor + Host OS | Host OS | Only CPU hardware |
| Memory Encryption | No | Yes | Yes |
| Protected Code Size | Full VM | Small enclave | Full VM |
| Guest OS Required | Yes | No | Yes |
| Performance Overhead | Baseline | 5-50% | 2-10% |
| Attestation | Not available | Yes | Yes |
When to Use Confidential VMs:
- Protect an entire application stack
- Minimal code changes
- Large memory requirements
When to Use Application Enclaves:
- Small portions of sensitive data
- Small memory requirements
When Traditional VMs Are Sufficient:
- Full trust in cloud provider
- Low data sensitivity
Benefits of Confidential VMs
1. Zero-Trust Cloud Architecture
Confidential VMs enable a true zero-trust model where you don’t have to trust:
- Cloud provider employees
- Hypervisor or host OS
- Co-tenants on shared hardware
2. Regulatory Compliance Made Easier
Confidential VMs provide:
- Technical enforcement of access controls
- Audit trail through attestation logs
- Data residency compliance
Example: A European hospital can use a US-based cloud for AI analysis of patient records, knowing that US cloud administrators cannot access the data—satisfying GDPR requirements.
3. Protection Against Sophisticated Attacks
Confidential VMs defend against:
- Malicious hypervisor
- Physical access attacks
- Memory snapshot attacks
4. Confidential Data Collaboration
Confidential VMs enable new business models where multiple parties can jointly process sensitive data without trusting each other.
5. Intellectual Property Protection
Companies can protect their most valuable assets when running in the cloud:
- AI Models: Run proprietary machine learning models without exposing weights or architectures
- Algorithms: Execute trade secret algorithms without revealing source code
Real-World Use Cases for Confidential VMs
1. Confidential AI and Machine Learning
Run AI workloads in confidential VMs:
- Training data remains encrypted during model training
- Model weights are never exposed to the hypervisor
Platforms: Phala Network provides GPU-accelerated confidential VMs specifically designed for confidential AI inference with NVIDIA H100 GPUs in TEEs.
2. Regulated Industries (Healthcare, Finance)
Deploy databases, analytics, and applications in confidential VMs to ensure compliance with regulations like HIPAA.
3. Sovereign Cloud and Data Residency
Use confidential VMs to enforce cryptographic data boundaries, ensuring data sovereignty.
4. Secure Multi-Tenant SaaS
Offer “confidential SaaS” where each customer’s data is processed in a dedicated confidential VM.
5. Blockchain and Web3
Use confidential VMs as secure off-chain compute, enabling confidential DeFi and private DAOs.
How to Get Started with Confidential VMs
1. Choose a Cloud Provider
Major cloud platforms now offer confidential VM services:
- Google Cloud Confidential VMs: AMD SEV, Intel TDX (preview)
- Microsoft Azure Confidential VMs: AMD SEV-SNP, Intel TDX
- AWS: Limited support via Nitro Enclaves
- Phala Confidential Cloud: AMD SEV-SNP with GPU TEE support
2. Set Up Your First Confidential VM
Simplified Deployment Workflow for Google Cloud:
- Enable Confidential Computing API via cloud console or CLI
- Create Confidential VM Instance:
- Select machine type (e.g., N2D series with AMD SEV)
- Enable confidential compute option
- Set maintenance policy to TERMINATE
- Choose OS image (Ubuntu, RHEL, etc.)
- Verify Configuration: Check instance metadata to confirm confidential computing is enabled
3. Implement Remote Attestation
Remote Attestation Verification Workflow:
- Fetch Attestation Report: Request attestation report from VM’s metadata server
- Submit for Verification: Send report to cloud provider’s attestation verification service
- Verify Signature: Service checks report signature using hardware manufacturer’s root certificate
- Validate Measurements: Compare VM measurements against expected values
4. Migrate Existing Workloads
Lift-and-Shift Process:
- Create a confidential VM with the same specs as your traditional VM
- Deploy your application using standard tools (Docker, Kubernetes, etc.)
- Implement attestation in your client application
- Update client code to send data only after successful attestation
Performance Considerations
Encryption Overhead
| Workload Type | Typical Overhead |
| Compute-intensive | 2-5% |
| Memory-intensive | 5-10% |
| I/O-intensive | 1-3% |
| AI inference | 3-8% |
| Database queries | 5-12% |
Live Migration Limitations
Confidential VMs cannot be live-migrated like traditional VMs. This is by design—live migration would expose decrypted memory to the hypervisor.
Limitations and Challenges
1. Limited Hardware Availability
Confidential VM technologies are relatively new and not all cloud regions support them yet.
2. Attestation Complexity
Implementing remote attestation requires cryptographic expertise.
3. Debugging Difficulties
Traditional debugging tools don’t work with confidential VMs.
4. Key Management
While confidential VMs protect data in use, you still need to manage encryption keys for data at rest.
The Future of Confidential VMs
GPU-Accelerated Confidential VMs
The next frontier is confidential AI with GPU TEEs, enabling confidential AI at scale.
Confidential Containers and Kubernetes
Confidential VMs are evolving to support container-native workflows.
Cross-Cloud Confidential Federation
Future standards will enable portable attestation and confidential multi-cloud.
Decentralized Confidential Compute
Projects like Phala Network are pioneering decentralized confidential computing.
Frequently Asked Questions (FAQ)
What is the difference between a confidential VM and a regular VM?
A regular VM relies on the hypervisor to isolate it from other VMs, but the hypervisor and cloud administrators can access the VM’s memory and data. A confidential VM uses CPU-based hardware encryption to protect its memory from the hypervisor, cloud admins, and physical attackers. Only the confidential VM itself can decrypt its data, and you can remotely verify its integrity through attestation.
Do I need to change my application code to use a confidential VM?
In most cases, no. Confidential VMs are designed for “lift-and-shift” migrations. Your application, operating system, and middleware run unmodified. The main code changes are in client applications that send data to the confidential VM—they should verify the VM’s attestation report before transmitting sensitive data.
Are confidential VMs slower than regular VMs?
Confidential VMs have a 2-10% performance overhead due to memory encryption and integrity checking. The exact impact depends on your workload (memory-intensive workloads see higher overhead). For most applications, this is an acceptable trade-off for the security benefits. The overhead decreases with each new CPU generation as encryption engines improve.
Can I use confidential VMs for AI and machine learning?
Yes. Confidential VMs are excellent for AI workloads where you need to protect training data, model weights, or inference requests. Recent advancements include GPU TEEs (NVIDIA H100 Confidential Computing) that enable confidential AI with GPU acceleration. Phala Network specializes in GPU-based confidential AI infrastructure.
Which cloud providers support confidential VMs?
- Google Cloud: AMD SEV-SNP and Intel TDX (preview) on N2D, C2D, C3 instances
- Microsoft Azure: AMD SEV-SNP and Intel TDX on DCasv5, ECasv5 series
- AWS: Limited support via Nitro Enclaves (not full confidential VMs yet)
- [Phala Cloud](https://docs.phala.com/phala-cloud/getting-started/overview): AMD SEV-SNP with GPU TEE support for confidential AI
How do I verify that my confidential VM is actually secure?
Use remote attestation. Before sending sensitive data, request an attestation report from the confidential VM. This report is cryptographically signed by the CPU hardware and proves:
- The VM is running on genuine TEE hardware (AMD SEV, Intel TDX, etc.)
- The VM’s boot state matches expected measurements (correct OS and configuration)
- The hypervisor hasn’t tampered with the VM
Cloud providers offer attestation services (Google Cloud Attestation, Azure Attestation) to simplify verification.
Can confidential VMs protect against all attacks?
No technology is 100% secure. Confidential VMs protect against:
- Malicious cloud providers and administrators
- Compromised hypervisors
- Physical access attacks
- Most software-based side-channel attacks
They don’t protect against:
- Bugs in your application code
- Vulnerabilities in the guest OS
- Sophisticated hardware side-channel attacks (though mitigations exist)
- Supply chain attacks on the CPU (you must trust the CPU vendor)
Use confidential VMs as part of a defense-in-depth strategy, not as a silver bullet.
What’s the difference between AMD SEV and Intel TDX?
Both provide memory encryption for confidential VMs, but with different approaches:
- AMD SEV-SNP: Encrypts VM memory with per-VM keys; SNP adds integrity protection. Available in AMD EPYC 3rd Gen and newer. Widely deployed in Google Cloud, Azure.
- Intel TDX: Creates “Trust Domains” with encrypted memory and includes stronger attestation features. Available in Intel Xeon 4th Gen (Sapphire Rapids) and newer. Emerging in cloud offerings.
Both achieve similar security goals; choice often depends on cloud provider availability and performance benchmarks for your workload.
Can I run Windows in a confidential VM?
Yes. Both Google Cloud and Microsoft Azure support Windows Server in confidential VMs:
- Azure: Windows Server 2019 and newer on DCasv5 series with AMD SEV-SNP
- Google Cloud: Windows Server 2019/2022 on N2D instances with AMD SEV
Linux support is more mature, but Windows confidential VMs are production-ready for enterprises.
How much do confidential VMs cost compared to regular VMs?
Pricing varies by cloud provider, but confidential VMs typically cost 10-30% more than equivalent regular VMs due to:
- Newer CPU generations with TEE features
- Limited availability (less competition drives up prices)
- Additional infrastructure for attestation services
As adoption grows and hardware becomes more common, prices are expected to approach parity with regular VMs.
Conclusion: Should You Use Confidential VMs?
Confidential VMs represent a fundamental shift in cloud security, moving from trust-based to cryptography-based protection. If your organization:
- Handles highly sensitive data (healthcare, finance, government, IP)
- Needs to comply with strict data protection regulations (GDPR, HIPAA, FedRAMP)
- Wants to use cloud infrastructure without trusting the cloud provider
- Requires protection against privileged access and insider threats
- Is building confidential AI or multi-party computation applications
…then confidential VMs are a game-changer.
Start small: Deploy a pilot workload in a confidential VM, implement attestation, and measure the performance impact. As you build expertise, expand to more sensitive workloads.
The future of cloud computing is confidential by default. Confidential VMs are the first step toward that vision.
