Confidential Computing Explained

Confidential computing protects data while it's being processed — the last gap in data security. Data at rest and in transit are well-solved; data in use has been the weak link.

How It Works

Confidential computing uses hardware-based Trusted Execution Environments (TEEs):

• Intel SGX/TDX — Hardware enclaves with encrypted memory
• AMD SEV-SNP — Full VM encryption with integrity protection
• ARM CCA — Confidential Compute Architecture for Arm processors
• NVIDIA H100 TEE — GPU confidential computing for AI workloads

The CPU encrypts memory so that even the hypervisor, host OS, and cloud provider cannot read it.

Use Cases

• Multi-party computation — Multiple organizations process shared data without revealing their inputs
• Regulated workloads — Healthcare, finance, and government data with strict compliance requirements
• AI model protection — Protect proprietary model weights during inference
• Key management — HSM-grade key protection without dedicated hardware
• Secure analytics — Process sensitive datasets without exposing raw data

Deploying Confidential VMs

All major cloud providers offer confidential computing:

# Azure confidential VM
az vm create \
  --name secure-workload \
  --image UbuntuServer2404 \
  --security-type ConfidentialVM \
  --os-disk-security-encryption-type VMGuestStateOnly \
  --size Standard_DC4as_v5

# GCP confidential VM
gcloud compute instances create secure-workload \
  --machine-type=n2d-standard-4 \
  --confidential-compute \
  --maintenance-policy=TERMINATE

# AWS Nitro Enclaves
aws ec2 run-instances \
  --instance-type m5.xlarge \
  --enclave-options Enabled=true

Kubernetes + Confidential Computing

Run confidential containers in Kubernetes with projects like Confidential Containers (CoCo):

apiVersion: v1
kind: Pod
metadata:
  name: confidential-workload
spec:
  runtimeClassName: kata-cc
  containers:
  - name: secure-app
    image: registry.example.com/secure-app:latest
    resources:
      limits:
        memory: "4Gi"

Key components:

• Kata Containers with TEE support for pod isolation
• Remote attestation to verify the enclave before sending secrets
• Encrypted container images that decrypt only inside the TEE
• Sealed secrets bound to specific enclave measurements

Attestation Flow

Remote attestation proves the workload runs in a genuine TEE:

1. Workload generates an attestation report (hardware-signed)
2. Report includes measurement of loaded code
3. Verifier checks report against expected measurements
4. If valid, secrets are released to the enclave
5. All processing happens inside the TEE

Performance Impact

Confidential computing adds overhead:

• CPU: 2-15% overhead depending on workload
• Memory: TEE memory limits (SGX: 256MB-1TB, SEV: full VM)
• I/O: Encryption/decryption adds latency to memory access
• GPU: NVIDIA H100 CC mode adds ~5% overhead for AI inference

FAQ

Does my cloud provider have access to my data with confidential computing? No. The hardware encrypts memory with keys the provider cannot access. This is verified through remote attestation.

Is confidential computing production-ready? Yes. Azure, GCP, and AWS all offer GA confidential VM options. Kubernetes support is maturing rapidly.

When should I use confidential computing vs. standard encryption? When you need to protect data during processing, not just at rest and in transit. Required for multi-party computation and strict compliance scenarios.

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