Quantum-Secure AI: The Next Horizon
How quantum-resistant architectures protect AI against adversarial attacks in the post-quantum era.
Dr. Sarah Chen
February 12, 2025
How quantum-resistant architectures protect AI against adversarial attacks in the post-quantum era.
Traditional encryption methods like AES-256 are vulnerable to quantum decryption attacks. As AI becomes mission-critical, protecting model weights and inference data from adversarial manipulation requires quantum-resistant security layers.
Our implementation uses lattice-based cryptography to secure both static model weights and dynamic inference data against quantum decryption attempts.
Quantum-resistant protocols encrypt model inputs/outputs in real-time, achieving <99.7% security assurance against adversarial inputs.
37% reduction in successful adversarial attacks on trained models compared to classical defenses.
Full verification pipeline ensures all data inputs, model weights, and inference outputs meet quantum-safe standards.
Less than 1.5% performance overhead while maintaining quantum-level security across training and inference cycles.
import { QuantumSecureModel } from '@elid/ai-security';
const model = new QuantumSecureModel({
encryption: 'lattice-based',
threatModel: 'post-quantum',
trainingProtection: true
});
model.train(dataset).then(protectedModel => {
const secureResult = model.infer(input, { quantumProtection: 'active' });
});
Chief Quantum Security Scientist
Pioneered the foundational quantum-resistant algorithms in this research.
AI Implementation Lead
Designed the secure inference engine with adversarial detection systems.
Security Engineer
Implemented quantum-safe data validation at each processing stage.
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