HIP-5: Post-Quantum Security for AI Infrastructure

Abstract

This proposal mandates the integration of NIST Post-Quantum Cryptography standards across all Hanzo AI infrastructure, ensuring quantum-resistant security for AI models, data, and communications. Building on Lux Network's PQC implementation (LP-100), this extends quantum resistance to AI-specific operations.

Motivation

AI infrastructure faces unique security challenges that will be amplified by quantum computing:

Model Theft: Quantum computers could break encryption protecting proprietary models
Data Privacy: Training data and user inputs need long-term protection
Inference Security: Model outputs must remain confidential
Authentication: API access and billing require quantum-resistant signatures
Long-term Value: AI models and data have decades-long value requiring future-proof security

Specification

PQC Algorithm Adoption

Inherit from Lux Network (LP-100):

ML-KEM-768: Default key encapsulation
ML-DSA-65: Default digital signatures
Hybrid Mode: ML-KEM + X25519 for defense-in-depth

AI-Specific Security Layers

Model Protection

pub struct SecureModel {
    // Model weights encrypted with ML-KEM
    encrypted_weights: Vec<u8>,
    // Signature for integrity
    signature: MlDsaSignature,
    // Encryption key wrapped with KEK
    wrapped_key: WrappedKey,
    // Privacy tier configuration
    privacy_tier: PrivacyTier,
}

Secure Inference Pipeline

Input Encryption: Client encrypts input with ML-KEM
TEE Processing: Computation in secure enclave
Output Encryption: Results encrypted before transmission
Audit Trail: ML-DSA signed logs

API Authentication

{
  "api_key_id": "ak_1234",
  "timestamp": 1703001234,
  "nonce": "random_value",
  "signature": {
    "algorithm": "ML-DSA-65",
    "value": "base64_signature"
  }
}

Privacy Tiers for AI

Tier	Use Case	Security Features
0	Public models	Basic PQC encryption
1	User data	+ At-rest encryption
2	Proprietary models	+ TEE processing
3	Sensitive inference	+ GPU CC (H100)
4	Classified AI	+ Full TEE-I/O

Key Management

Hierarchical Key Structure

Root Key (ML-KEM-1024)
├── Model Encryption Keys (ML-KEM-768)
├── API Authentication Keys (ML-DSA-65)
├── Data Encryption Keys (ML-KEM-768)
└── Session Keys (Hybrid Mode)

Key Rotation Schedule

Root Keys: Annual rotation
Model Keys: Per version
API Keys: Monthly rotation
Session Keys: Per connection

Implementation Requirements

For AI Services

All model storage uses ML-KEM encryption
API requests require ML-DSA signatures
Inter-service communication uses hybrid mode
Audit logs are cryptographically signed

For Client SDKs

from hanzo import SecureClient

client = SecureClient(
    api_key="...",
    pqc_enabled=True,  # Default
    privacy_tier=2
)

# Automatic PQC encryption/signing
response = client.complete(
    model="HMM-32B",
    messages=[...]
)

Rationale

Why NIST Standards?

Industry Standard: Wide adoption expected
Proven Security: Extensively analyzed
Hardware Support: Accelerators coming
Compliance: Meets regulatory requirements

Why Privacy Tiers?

Different AI applications have varying security needs:

Public APIs: Basic quantum resistance sufficient
Enterprise AI: Require TEE processing
Government AI: Need maximum security

Security Considerations

Threat Model

Quantum Adversary: Assumes quantum computer access
Side Channels: Protected by TEE deployment
Model Extraction: Prevented by secure inference
Data Leakage: Encrypted at all stages

Compliance

NIST Standards: FIPS 203/204 compliant
GDPR: Quantum-resistant data protection
HIPAA: Healthcare data security
SOC 2: Security controls

Implementation

Phase 1: Core Integration (Complete)

Lux Network PQC implementation
Basic key management
API authentication

Phase 2: Model Security (Q1 2025)

Encrypted model storage
Secure inference pipeline
Client SDK updates

Phase 3: Full Deployment (Q2 2025)

All services PQC-enabled
Legacy migration complete
Performance optimization

Post-Quantum Security for AI Infrastructure