Technology module
Security and data sovereignty
The central question in enterprise AI adoption is whether data and models stay under your control. VXI builds a foundation of trust from silicon to software so AI runs locally, data stays on site, and model assets remain protected.
Security · Deployment
Why it matters
Support enterprise deployment through secure boot, model protection, and local data processing.
Trust from the first instruction
Secure boot and integrity checks ensure the platform runs only trusted firmware and software.
Protected model assets
Model encryption and access control reduce the risk of copying or tampering.
Data stays on site
Inference completes locally, so sensitive data never has to leave the enterprise or device, supporting compliance and privacy.
How it works
Root of trust
A hardware root of trust anchors secure boot and key management.
Secure boot verification
Verify firmware and software integrity layer by layer and block unauthorized code.
Model and data protection
Protect models and sensitive data while they load and run.
Local inference
Complete inference inside the device or enterprise environment to reduce data exposure.
What we align on during evaluation
Bring this information to your evaluation request so the VXI team can propose a concrete platform and adoption plan faster.
- Industry compliance requirements and data classification policies
- Model asset protection and licensing needs
- Deployment environment and update management
- Integration with existing security and identity architecture
Frequently asked questions
- What is data sovereignty?
- Data sovereignty means an organization controls where its data is stored, processed, and used. Running AI locally keeps sensitive data from being uploaded to external clouds.
- How are models deployed on devices protected?
- VXI protects model assets with secure boot, model encryption, and access control, and can plan licensing and update flows around customer needs.
- Which organizations benefit from private AI deployment?
- Organizations that prioritize privacy and compliance — such as finance, healthcare, manufacturing, public services, and R&D — are strong fits for on-premises or intranet AI.
Related technology modules
- Compiler and runtime — From model conversion and quantization to runtime control
- System co-engineering — An integration path connecting model, software, host, and memory
- Memory hierarchy — Co-placed weights, activations, and cache
See private AI applications · What we align on during evaluation