What Is Confidential Computing?
Target Keywords: “What is confidential computing?”, “Confidential Computing Definition”, “Encrypt Data in Use”
Meta Description: Confidential computing is a technology that protects data in use via secure enclaves. Phala explains how confidential computing protects data in use with secure enclaves — discover how it works, its benefits, and real-world AI applications.
TL;DR
Confidential computing is a technology that protects data while it’s being processed by keeping it encrypted even during computation. Unlike traditional encryption that only protects data at rest (in storage) and in transit (while moving), confidential computing secures data in use – the final frontier of data security.
Introduction
Every day, organizations process sensitive data – from patient health records to financial transactions to proprietary AI models. While encryption has long protected data stored on disks and data traveling across networks, there’s been a critical gap: data must be decrypted to be processed, leaving it vulnerable to breaches, insider threats, and cloud provider access.
Confidential computing solves this problem by creating hardware-based Trusted Execution Environments (TEEs) that keep data encrypted even while applications process it. At Phala, our TEE-based Confidential AI Cloud enables secure, privacy-preserving AI workloads through hardware-level protection.
The Problem: The Data-in-Use Security Gap
Modern encryption addresses two states of data:
- Data at rest: Encrypted when stored on disk
- Data in transit: Encrypted when sent over networks (HTTPS/TLS)
- Data in use: ❌ Traditionally UNPROTECTED during processing
This gap exposes organizations to serious risks:
- Cloud provider employees could access sensitive data
- Hackers who breach application memory can steal unencrypted data
- Insider threats from system administrators
- Compliance violations (GDPR, HIPAA) when data is exposed during processing
Confidential computing closes this gap by ensuring data remains encrypted throughout its entire lifecycle – including while being actively processed.
How Confidential Computing Works
Confidential computing relies on hardware-based Trusted Execution Environments (TEEs) – isolated, encrypted regions of a processor where code and data are protected from all other software on the system, including:
- Operating systems
- Hypervisors
- System administrators
- Cloud providers
- Other applications
The Three Core Principles
- Hardware-Based Isolation
- Dedicated secure areas in CPU/GPU hardware
- Protected from all software, including privileged code
- Processors like Intel SGX, AMD SEV, ARM TrustZone
- Memory Encryption
- All data in TEE memory is encrypted
- Encryption keys generated and stored in hardware
- Keys never exposed to software
- Remote Attestation
- Cryptographic proof that code is running in genuine TEE
- Verifies TEE hasn’t been tampered with
- Allows third parties to trust the execution environment
Learn more about Remote Attestation.
Confidential Computing vs. Traditional Cloud Security
| Security Layer | Traditional Encryption | Confidential Computing |
| Data at Rest | ✅ Encrypted | ✅ Encrypted |
| Data in Transit | ✅ Encrypted (TLS/HTTPS) | ✅ Encrypted (TLS/HTTPS) |
| Data in Use | ❌ Decrypted during processing | ✅ Encrypted in TEE |
| Protection from Cloud Provider | ❌ Provider has access | ✅ Hardware-level isolation |
| Protection from OS/Hypervisor | ❌ Can access memory | ✅ Cannot access TEE |
| Protection from Admins | ❌ Privileged access | ✅ No access to TEE |
The key difference: Traditional cloud security encrypts data at rest and in transit, but leaves data exposed during processing. Confidential computing keeps data encrypted even while in use, closing the final security gap.
Real-World Use Cases
1. Healthcare: Multi-Hospital AI Research
Hospitals can collaboratively train AI models on patient data without exposing Protected Health Information (PHI):
- Each hospital’s data stays encrypted in TEEs
- AI model trains on combined encrypted data
- Results are shared, but raw patient data never leaves secure enclaves
- Enables HIPAA-compliant medical research
2. Finance: Fraud Detection Across Banks
Banks can detect fraud patterns across institutions without sharing customer data:
- Transaction data remains encrypted in TEEs
- Cross-bank analysis happens on encrypted data
- No bank sees another’s customer information
- Meets GDPR and PCI-DSS requirements
3. AI/ML: Protecting Proprietary Models
Companies can deploy AI models to customers without exposing model weights:
- Model runs entirely in TEE
- Inputs and outputs pass through, but model stays encrypted
- Prevents model theft and reverse engineering
- Protects valuable IP
Explore more about Private AI Inference.
Benefits of Confidential Computing
For Enterprises
- Regulatory Compliance: Meet GDPR, HIPAA, PCI-DSS, CCPA requirements
- Multi-Party Collaboration: Share insights without sharing data
- Cloud Migration: Move sensitive workloads to cloud securely
- Zero-Trust Architecture: Eliminate trust in cloud providers/admins
For Developers
- Data Privacy by Default: Built-in protection without complex crypto
- Verifiable Security: Attestation proves code integrity
- Standard Development: Use familiar languages and frameworks
- Future-Proof: Industry-standard technology backed by major vendors
For End Users
- Data Sovereignty: Your data stays private even during processing
- Breach Protection: Encrypted data is useless to attackers
- Trust: Cryptographic proof your data is handled securely
Who’s Using Confidential Computing?
Major technology companies and organizations have adopted confidential computing:
- Microsoft Azure: Confidential VMs and Azure Confidential Computing
- Google Cloud: Confidential VMs with AMD SEV encryption
- AWS: Nitro Enclaves for isolated compute
- IBM: Confidential Computing for hybrid cloud
- Confidential Computing Consortium: Industry standard organization (Linux Foundation)
Industry Standards & Technologies
Confidential computing is supported by major hardware manufacturers:
- Intel SGX (Software Guard Extensions): Application-level enclaves
- AMD SEV (Secure Encrypted Virtualization): Full VM encryption
- ARM TrustZone: Secure world for mobile and IoT
- NVIDIA H100: Confidential GPUs for AI/ML workloads
Learn more about GPU TEE.
Getting Started with Confidential Computing
If you’re considering confidential computing for your organization:
- Identify Sensitive Workloads: Which data requires protection during processing?
- Evaluate Compliance Needs: What regulations apply (GDPR, HIPAA, etc.)?
- Choose TEE Technology: Based on workload (VMs, containers, applications)
- Pilot Project: Start with one use case to demonstrate ROI
- Scale Deployment: Expand to additional workloads
For developers ready to build with confidential computing, Phala provides a secure cloud platform where you can deploy AI workloads using Intel TDX, AMD SEV, and Nvidia H200 hardware.
Explore Phala Cloud for more details.
Frequently Asked Questions
What is the difference between confidential computing and encryption?
Traditional encryption protects data at rest and in transit but requires decryption for processing. Confidential computing keeps data encrypted throughout its entire lifecycle, including during active computation in memory.
Who needs confidential computing?
Organizations handling sensitive data (healthcare providers, financial institutions, government agencies), companies subject to strict compliance regulations, multi-party data collaborations, and any business wanting to protect proprietary algorithms or models.
Does confidential computing slow down applications?
Modern TEE implementations like Intel SGX and AMD SEV operate at near-native performance with minimal overhead (typically 0-15% depending on workload). The security benefits far outweigh minor performance costs.
Can confidential computing prevent all data breaches?
While confidential computing dramatically reduces attack surface by protecting data in use, it’s one layer of a defense-in-depth strategy. It should be combined with traditional security measures, access controls, and secure development practices.
Is confidential computing only for cloud environments?
No. Confidential computing works in cloud, on-premises, edge, and hybrid environments. It’s particularly valuable for organizations wanting cloud-like capabilities while maintaining on-premises data sovereignty.
Conclusion
Confidential computing represents the next evolution in data security – completing the encryption story by protecting data during its most vulnerable state: active processing. As data privacy regulations tighten and cyber threats grow, confidential computing is transitioning from emerging technology to essential infrastructure.
By using hardware-based Trusted Execution Environments, organizations can finally achieve true end-to-end data protection, enabling secure multi-party collaboration, compliant cloud adoption, and privacy-first AI development.
Ready to build with confidential computing? Phala’s Confidential AI Cloud lets organizations easily adopt confidential computing for AI and data applications — combining privacy, performance, and hardware-level security.
Related Articles
- What Is a Trusted Execution Environment (TEE)?
- What Is Confidential AI?
- Confidential Computing vs. Traditional Cloud Security
- Getting Started with Confidential Computing (Developer Guide)
