Private AI Cloud
Confidential Computing for AI
An educational platform exploring confidential computing — hardware-enforced privacy that protects data during processing. Learn about Trusted Execution Environments, secure enclaves, and how cryptographic guarantees enable privacy-preserving AI without compromising on performance.
What is Confidential Computing?
Confidential computing is a security model that protects data while it's being processed. Traditional encryption protects data at rest (storage) and in transit (network), but leaves it exposed in memory. Confidential computing closes this gap by encrypting data even during computation.
Hardware-Enforced Privacy
Confidential computing uses specialized CPU features to create isolated execution environments where data is encrypted even during processing. Unlike software-based security, hardware enforces the isolation—making it immune to operating system vulnerabilities.
Trust Boundaries Redefined
Traditional computing requires trusting the cloud provider, administrators, and operating system. Confidential computing shrinks the trusted computing base (TCB) to just the CPU and your code, eliminating the need to trust infrastructure providers.
Encrypted Memory
Data is encrypted at rest (storage), in transit (network), and critically—in use (memory). Modern CPUs encrypt memory contents at the hardware level, making it unreadable even with physical access to RAM or debugging tools.
Remote Attestation
Before trusting a confidential computing environment, you can cryptographically verify its integrity. Remote attestation provides cryptographic proof that your code is running in a genuine secure enclave, unmodified by malware or attackers.
Why Confidential Computing Matters for AI
Data Protection
Process sensitive datasets (healthcare, finance, personal data) without exposing them to infrastructure providers or administrators.
Model Privacy
Protect proprietary AI models and intellectual property from theft or inspection, even when running on third-party infrastructure.
Regulatory Compliance
Meet GDPR, HIPAA, and other regulations requiring data minimization and privacy-by-design through cryptographic guarantees.
Confidential Computing Consortium Standards
Privacy-Preserving Architecture Framework
A step-by-step walkthrough of how confidential computing protects AI workloads at every stage of processing.
Your Data Arrives
Sensitive AI workloads, models, and datasets
Encrypted at Ingress
AES-256-GCM encryption before storage or processing
Processed in TEE
Trusted Execution Environment isolates computation
Keys in HSM
Hardware Security Module stores cryptographic keys
Encrypted Output
Results encrypted before returning to you
At no point in this chain does our infrastructure have access to your unencrypted data, models, or results. Cryptographic guarantees, not promises.
Secure Enclave in Action
Watch how data flows through a Trusted Execution Environment with hardware-enforced encryption.
Your Data
Plaintext
Secure Enclave
Ready
Result
Encrypted
1. Encrypt
Data encrypted with AES-256-GCM before entering enclave
2. Process
Computation happens in hardware-isolated memory
3. Return
Results encrypted before leaving the secure boundary
Zero-Knowledge Architecture
In a properly designed confidential computing system, the infrastructure provider is cryptographically prevented from accessing user data.
Your training data or datasets
Your model weights or architectures
Your prompts or inference inputs
Your API keys or credentials
Your encryption keys
Your inference results or outputs
Your usage patterns or metadata
Your business logic or IP
This is the core principle of confidential computing: cryptographic blindness. Even with physical server access, administrative privileges, or legal subpoenas, encrypted data in secure enclaves remains mathematically inaccessible to infrastructure providers. This architectural guarantee is what separates confidential computing from traditional cloud security.
Cryptographic Foundations
The cryptographic primitives and hardware technologies that enable confidential computing for AI workloads.
Trusted Execution Environments
TEEHardware-enforced isolated execution using CPU security extensions. Code runs in encrypted memory regions that remain protected even from privileged software like hypervisors and operating systems.
Hardware Security Modules
HSMFIPS 140-2 Level 3 certified tamper-resistant hardware for cryptographic operations. All key material generated and stored within HSM boundary.
End-to-End Encryption
E2EEAES-256-GCM for data at rest, TLS 1.3 with perfect forward secrecy for data in transit. All encryption keys derived from customer-controlled root keys.
Built on industry-standard cryptographic protocols
Enterprise Privacy Applications
How confidential computing helps organizations meet regulatory requirements through verifiable cryptographic controls rather than trust-based policies.
SOC 2 Type II
Cryptographic controls audited for confidentiality and availability
ISO 27001
Information security management with cryptographic key management
HIPAA Compliant
PHI protected with BAA-backed encryption guarantees
GDPR Ready
Data minimization and cryptographic right-to-erasure
FedRAMP In Progress
Government-grade security controls and HSM requirements
Confidential computing enables a shift from trust-based security to cryptographically verifiable controls. Instead of auditing policies and procedures, compliance can be demonstrated through hardware attestation and mathematical proofs.
Interactive Security Concepts
Click any concept below to explore with interactive visualizations. Every security concept includes visual diagrams and hands-on demonstrations.
Threat Protection Mechanisms
Malicious Administrator
HSM-backed keys prevent admin access to encryption keys
ProtectedCompromised Hypervisor
TEE encryption prevents memory inspection
ProtectedPhysical Server Access
Memory encryption and secure boot attestation
ProtectedNetwork Eavesdropping
TLS 1.3 with perfect forward secrecy
ProtectedDefense in Depth Layers
Click each layer to enable it. All three layers must be compromised to access data.
Hardware TEE Encryption
CPU-level memory protection
Application-Level Encryption
AES-256-GCM data encryption
Network TLS Encryption
TLS 1.3 transport security
Security Level: 0/3 Layers Active
Remote Attestation Process
Click “Run Attestation” to see how cryptographic verification proves enclave integrity.
Request Quote
Client requests attestation quote from enclave
Generate Signature
TEE signs measurement with private key
Verify Certificate
Client validates signature against CPU certificate
Trust Established
Cryptographic proof confirms genuine enclave
Verification Methods
Confidential computing replaces trust with cryptographic verification.
Remote Attestation
Remote attestation provides cryptographic proof that code is running in a genuine secure enclave. Users can verify the integrity of the execution environment without trusting the infrastructure provider.
# Example verification command$ verify-enclave --quote attestation.quote --policy security-policy.jsonCustomer-Managed Keys
In confidential computing architectures, encryption keys can be generated and managed entirely by customers. The infrastructure provider never gains access to key material.
- Customer-controlled key management
- Hardware security module (HSM) integration
- Cryptographic key derivation functions
- Zero-knowledge key wrapping protocols
Security Audits
Independent third-party audits of cryptographic implementation and infrastructure.
“Don't trust, verify.” Confidential computing replaces trust-based security models with cryptographic proofs and hardware-verified guarantees.
Learn More About Confidential Computing
Questions about privacy-preserving AI? Want to discuss confidential computing use cases? Reach out.