Trusted Compute Overview#

Trusted Compute (TC) is an advanced security framework that combines software-defined security extensions with underlying hardware security capabilities to create isolated execution environments for edge computing workloads. This technology provides a hardware root of trust that ensures sensitive applications and data remain protected from external threats, unauthorized access, and potential system compromises.

What is Trusted Compute?#

Trusted Compute leverages Intel platform security features to create hardware-assisted virtual machines where applications can execute in complete isolation from other workloads. This isolation extends beyond traditional containerization by providing:

  • Hardware-backed Security: Utilizes Intel platform security features like Intel VT-x (Virtualization Technology) and TPM (Trusted Platform Module)

  • Memory Encryption: Provides runtime protection for sensitive algorithms and detection models by safeguarding against cold boot attacks and physical threats to the memory subsystem

  • Secure Boot Process: Ensures only authenticated and verified code executes within the trusted environment

  • Full Disk Encryption (FDE) Process: Prevents unauthorized access to disk data, particularly in scenarios involving device theft, loss, or physical compromise

Key Benefits#

Enhanced Security#

  • Workload Isolation: Applications run in completely isolated environments, preventing cross-contamination

  • Data Protection: Sensitive runtime data remains protected from other workloads

  • Runtime Security: Guards against runtime attacks, malware, and unauthorized modifications

Edge Computing Optimization#

  • Reduced Attack Surface: Minimizes exposure points for potential security breaches

  • Local Processing: Enables secure processing of sensitive data at the edge without cloud dependencies

  • Performance: Maintains high performance while providing security through hardware acceleration

Use Cases#

Trusted Compute is particularly valuable for:

  • AI/ML Model Protection: Securing proprietary algorithms and training data

  • Video Analytics: Processing sensitive surveillance or traffic data securely

  • Autonomous AI Agents: Protecting decision-making processes and sensitive operational data in self-governing AI systems

Reference Implementation#

This documentation includes a practical example demonstrating Trusted Compute implementation:

  • Smart Intersection Deployment: A comprehensive guide showing how to deploy video analytics applications using Trusted Compute technology, including step-by-step instructions for isolating AI models and processing pipelines in a secure execution environment.

  • Smart Intersection Deployment: A comprehensive guide showing how to deploy Agentic AI applications using Trusted Compute technology, including step-by-step instructions for isolating AI & VLM models and processing pipelines in a secure execution environment.