Private AI Cloud
Hardware-Enforced Privacy for AI
Explore the principles of confidential computing - where hardware-level encryption protects your data even during processing. Learn about Trusted Execution Environments, secure enclaves, and zero-knowledge architectures.
From data sovereignty requirements to regulatory compliance, discover how cryptographic guarantees enable privacy-preserving AI without trust assumptions.
Why Private AI Computing Matters
Traditional cloud computing requires trusting your provider. Confidential computing replaces trust with cryptographic guarantees.
At rest, in transit, in use
Cryptographic verification
CPU-level protection
Tamper-resistant storage
The Fundamental Problem
When you run AI workloads in traditional clouds, your data must be decrypted for processing. During this window, administrators, compromised systems, or malicious actors could potentially access sensitive information. This is the “data in use” vulnerability.
Confidential computing solves this by using hardware-enforced encryption that protects data even during computation. Your AI models process data inside secure enclaves where even the cloud provider cannot see inside.
Confidential Computing Framework
The four pillars that enable hardware-enforced privacy.Click any card to explore the concept.
Hardware-isolated execution using CPU security extensions. Code runs in encrypted memory regions protected from all software, including the OS and hypervisor.
Cryptographic verification that proves your code is running in a genuine secure enclave. Validates the execution environment before trusting it with sensitive data.
AES encryption of RAM contents at the hardware level. Data remains encrypted even with physical access to memory chips or debugging interfaces.
Keys generated and stored in HSMs never leave the hardware boundary. Customer-controlled root keys enable true data sovereignty.
Built on industry standards
Security Architecture
Defense in depth through multiple security layers.Click each layer to see its protections.
Application Layer
Your AI models and applications run inside secure enclaves
Encryption Layer
AES-256-GCM encryption for all data operations
Hardware Layer
CPU security extensions enforce isolation
Key Management Layer
HSM-backed cryptographic operations
Result: An attacker would need to compromise all four layers simultaneously to access your data. Hardware enforcement makes this practically impossible.
Deployment Models
Choose the right confidential computing model for your use case.
Full virtual machine isolation with encrypted memory. Lift-and-shift existing workloads without code changes.
Application-level isolation with smallest trusted computing base. Maximum security for sensitive code paths.
Kubernetes-native confidential computing. Run containers in TEEs with standard orchestration.
Mix confidential and standard workloads. Optimize cost while protecting sensitive components.
Interactive Security Sandbox
Explore confidential computing concepts through interactive demonstrations.
Secure Enclave Visualization
Your Data
Plaintext
Secure Enclave
Ready
Result
Encrypted
Threat Protection
Click to see how each threat is mitigated.
Defense in Depth Layers
Click each layer to enable. All three must be breached to access data.
Hardware TEE Encryption
CPU-level memory protection
Application Encryption
AES-256-GCM data encryption
Network TLS Encryption
TLS 1.3 transport security
Remote Attestation Process
Click to verify enclave authenticity.
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
Enterprise Applications
How leading organizations use confidential computing for privacy-preserving AI.
Process PHI for diagnostics and research while maintaining HIPAA compliance through cryptographic guarantees.
Fraud detection and risk modeling on encrypted transaction data. Meet PCI-DSS and SOX requirements.
Classified workload processing with hardware-enforced isolation. FedRAMP and IL6 ready architecture.
Protect proprietary AI models and training data from competitors and insider threats.
Compliance Framework Compatibility
SOC 2 Type II
Audited security controls
ISO 27001
Information security management
HIPAA
Healthcare data protection
GDPR
European data privacy
PCI-DSS
Payment card security
FedRAMP
Government cloud security
Confidential computing provides the technical controls required by these frameworks.
Interactive Learning Lab
Hands-on demonstrations to explore confidential computing concepts.
Additional Resources
Research Papers
Academic foundations of TEE technology
Technical Specs
Intel SGX, AMD SEV-SNP, ARM TrustZone
Case Studies
Real-world implementation examples