Agentic Design

Patterns
🔒

Confidential Computing Patterns(CCP)

Hardware-based trusted execution environments (TEEs) protecting AI agents and data processing in untrusted environments

Complexity: highSecurity & Privacy Patterns

🎯 30-Second Overview

Pattern: Hardware-based trusted execution environments (TEEs) protecting AI agents and data processing in untrusted environments

Why: Provides hardware-verified security, enables multi-party collaboration, protects proprietary algorithms, and ensures regulatory compliance

Key Insight: Hardware TEE + remote attestation + encrypted communication → verifiable confidential AI processing

⚡ Quick Implementation

1TEE Selection:Choose Intel SGX, AMD SEV-SNP, or Intel TDX
2Framework Setup:Deploy Enarx, Gramine, or Occlum runtime
3Agent Enclave:Package AI agents in trusted execution environment
4Remote Attestation:Verify enclave integrity and authenticity
5Secure Communication:Encrypted channels between enclaves
Example: tee_selection → framework_setup → agent_enclave → remote_attestation → secure_communication

📋 Do's & Don'ts

✅Use hardware-based TEEs for maximum security guarantees
✅Implement remote attestation for enclave verification
✅Choose appropriate framework based on performance requirements
✅Encrypt all data in transit between enclaves
✅Monitor enclave resource usage and side-channel attacks
❌Trust enclaves without proper attestation verification
❌Store secrets in non-encrypted enclave memory
❌Ignore performance overhead of confidential computing
❌Use deprecated Intel SGX for new deployments
❌Skip security updates for TEE frameworks

🚦 When to Use

Use When

  • • Multi-party agentic AI collaboration
  • • Sensitive data processing requirements
  • • Untrusted cloud environments
  • • Regulatory compliance mandates

Avoid When

  • • Public data processing only
  • • Latency-critical real-time applications
  • • Resource-constrained edge devices
  • • Single-tenant trusted environments

📊 Key Metrics

Enclave Integrity
Successful attestation verification rate
Performance Overhead
Computation slowdown factor (1.5-30x)
Memory Protection
% data protected in TEE memory
Side-Channel Resistance
Attack mitigation effectiveness
Scalability Factor
Max concurrent enclaves supported
Framework Compatibility
% applications running without modification

💡 Top Use Cases

Federated AI: Multi-hospital ML training with patient privacy in TEE enclaves
Financial Trading: High-frequency algorithms protected from market manipulation
Multi-Agent Collaboration: Competitive intelligence sharing without data exposure
Edge AI: Autonomous vehicle decision-making with proprietary algorithm protection
Government AI: Classified data processing with hardware-verified security boundaries

References & Further Reading

Deepen your understanding with these curated resources

Hardware Technologies

Intel Software Guard Extensions (SGX) Documentation

AMD Secure Encrypted Virtualization (SEV) Guide

Intel Trust Domain Extensions (TDX) Architecture

ARM Confidential Compute Architecture (CCA)

Frameworks & Runtimes

Enarx - WebAssembly-based Confidential Computing

Gramine - LibOS for Intel SGX

Occlum - Memory-Safe SGX LibOS

Google Asylo - Cross-Platform TEE Framework

Academic Research

Machine Learning with Confidential Computing Survey (ACM 2024)

PPFL: Privacy-preserving Federated Learning with TEE (arXiv)

TEE Technology Evolution Benchmarking (arXiv 2024)

Confidential Computing Survey (IET Communications 2024)

Cloud Platforms & Tools

Microsoft Azure Confidential Computing

Google Cloud Confidential Computing

NVIDIA Confidential Computing for AI

Confidential Computing Consortium

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