Industry · MAXIMUM crypto policy
QNSP for Sovereign AI Labs & Model Marketplaces
Encrypted model training, GPU-enclave orchestration, and PQC-signed inference for sovereign AI labs and model marketplaces.
Encrypted model training pipelines in customer-controlled sovereign cloud, VPC, or on-prem environments. GPU enclave orchestration (Intel SGX, AMD SEV-SNP, AWS Nitro Enclaves), PQC-signed inference APIs, and zero plaintext exposure of training sets.
Threat model
What we're defending against
The HNDL, regulatory, and operational threats specific to this vertical.
Training-data extraction from the model
Membership-inference and gradient-leakage attacks recover training samples from served weights. End-to-end encryption from data lake to enclave neutralises the bulk-exposure risk.
Model exfiltration from the inference path
Served models are themselves IP. Enclave-bound inference + ML-DSA-signed responses make black-box weight extraction provably tamper-evident.
Cross-tenant leakage on shared GPUs
GPU enclaves (SGX, SEV-SNP, Nitro) plus QNSP tenant isolation give each customer cryptographic separation even on shared hardware.
Supply-chain attack on training data
PQC-signed dataset attestations — every input training file carries an ML-DSA signature that traces to its source.
Compliance mapping
Frameworks this vertical operates under
QNSP supports continuous evaluation for 7 live frameworks; other named frameworks are architecturally supported with evidence available on request.
| Framework | How QNSP maps |
|---|---|
| ISO/IEC 27001:2022 ↗ | A.8 cryptography controls for AI training and inference pipelines. |
| SOC 2 Type II ↗ | Logical access, tenant isolation, audit-trail integrity for shared AI infrastructure. |
| EU AI Act | High-risk AI system requirements include data-governance, traceability, and security — QNSP audit chain and PQC provenance signatures support each. |
| NIST AI RMF | Govern → Map → Measure → Manage; QNSP gives the cryptographic substrate for each. |
QNSP architecture
Capabilities mapped to this vertical
How QNSP services compose to meet this vertical's needs.
Intel SGX, AMD SEV-SNP, AWS Nitro Enclaves — attestation-verified training and inference
Every inference response carries an ML-DSA-65 signature; clients verify provenance independently
RAG over encrypted vector indexes — embeddings never leave the encryption boundary
Per-tenant model artifacts, per-tenant inference quotas, per-tenant audit
Outcomes
What deploying QNSP for this vertical delivers
- ✓Maximum crypto-policy tier — strongest parameter sets across training and inference
- ✓GPU-enclave attestation — training and inference run on verified hardware
- ✓PQC-signed inference responses — verifiable provenance from served model to consumer
- ✓Per-tenant isolation on shared GPU infrastructure
For your engineers
Build patterns that map to this vertical
When you've evaluated the platform, hand these references to your engineering team.
Next step