Protect AI model integrity, federated learning pipelines, and edge inference against quantum computing threats. From PQC-secured model distribution to FHE-accelerated classified inference and quantum-random training augmentation for resilient defense AI.
ML-DSA signed model updates and ML-KEM encrypted distribution ensure AI models cannot be tampered with, poisoned, or exfiltrated by quantum-capable adversaries.
Fully Homomorphic Encryption accelerators enable classified AI inference on encrypted data, ensuring sensitive inputs and outputs are never exposed in plaintext.
Integrated RISC-V + NPU + PQC accelerator system-on-chip for quantum-safe AI inference at the tactical edge with minimal power and maximum security.
Defense AI systems depend on trusted model distribution, secure training data pipelines, and authenticated inference endpoints. All of these rely on classical cryptography that quantum computers will break.
Once cryptographic protections fail, adversaries can poison training data through decrypted channels, launch adversarial attacks by exploiting quantum-broken authentication, and compromise AI model distribution to insert backdoors into deployed defense systems.
| Parameter | Value |
|---|---|
| Solutions Available | 6 quantum-safe solutions |
| ASIC Solutions | 2 (FHE accelerator, Edge AI SoC) |
| Firmware & Platforms | 4 (model distro, adversarial, federated, QRNG) |
| FPGA Solutions | 1 (federated learning) |
| AI-Integrated | 2 (adversarial defense, Edge AI SoC) |
| Standards | FIPS 203, 204, CNSA 2.0 |
| Migration Phase | 2025-2030 (hybrid first) |
| Solution | Type | Description |
|---|---|---|
| PQC-Secured Model Distribution | Software | ML-DSA signed AI model updates and ML-KEM encrypted model transfer for tamper-proof defense AI deployment |
| QS Federated Learning | Software, FPGA | PQC-encrypted federated learning for distributed defense AI training across classification boundaries |
| FHE for Privacy-Preserving Inference | ASIC | Fully Homomorphic Encryption accelerator for classified AI inference on encrypted data without decryption |
| AI-PQC Adversarial Defense | Software | ML model detecting quantum-enabled adversarial attacks on defense AI systems with PQC-secured alerts |
| QS Edge AI SoC | ASIC | RISC-V + NPU + PQC accelerator integrated SoC for defense edge AI with quantum-safe secure boot |
| QRNG for AI Training | Software | Quantum random data augmentation for robust defense AI training with true entropy injection |
| Solution | Description |
|---|---|
| FHE for Privacy-Preserving Inference | Dedicated ASIC accelerator implementing Fully Homomorphic Encryption for classified AI inference. Enables computation on encrypted data without exposing plaintext inputs or model outputs. |
| QS Edge AI SoC | Integrated RISC-V processor with neural processing unit and PQC accelerator on a single die. Quantum-safe secure boot, encrypted model loading, and tamper-resistant design for tactical edge deployment. |
| Solution | Description |
|---|---|
| PQC-Secured Model Distribution | Software framework for ML-DSA signed and ML-KEM encrypted AI model updates. Ensures model integrity and confidentiality across defense AI supply chains. |
| QS Federated Learning | PQC-encrypted federated learning platform enabling distributed defense AI training without exposing local training data across classification boundaries. |
| AI-PQC Adversarial Defense | Machine learning model trained to detect quantum-enabled adversarial attacks including model evasion, data poisoning, and inference manipulation on defense AI systems. |
| QRNG for AI Training | Quantum random number generator integration for defense AI training pipelines. Provides true entropy for data augmentation, dropout regularization, and stochastic training processes. |
| Solution | Description |
|---|---|
| AI-PQC Adversarial Defense | Deep learning model trained to identify quantum-era adversarial patterns including gradient-based evasion, model inversion, and data poisoning attacks. All detection results are PQC-signed for forensic chain-of-custody integrity. |
| QS Edge AI SoC | Integrated neural processing unit with PQC acceleration on RISC-V architecture. Performs quantum-safe AI inference at the tactical edge with encrypted model storage, authenticated data pipelines, and real-time threat classification. |
Choose the delivery model that matches your AI/ML system's integration requirements.
PQC-integrated AI/ML libraries, model signing tools, and federated learning frameworks. Compatible with TensorFlow, PyTorch, and ONNX runtimes.
FHE accelerator and QS Edge AI SoC delivered as hard IP blocks for integration into defense-grade ASICs. Includes verification and characterization data.
Pre-characterized FPGA implementations for federated learning acceleration and PQC-secured model loading on Xilinx UltraScale+ and Intel Agilex platforms.
Quantum-safe solutions that complement AI/ML defense security.
Contact us for quantum vulnerability assessments, AI/ML security evaluations, or custom integration for your defense AI platform.