Q-LOQ: A Unified Approach to AI Fraud Detection and Post-Quantum Consumer Security
Q-LOQ: A Unified Approach to AI Fraud Detection and Post-Quantum Consumer Security
Presents Q-LOQ, a unified platform architecture addressing both the AI fraud crisis and the quantum security transition. Analyzes the convergent threat landscape of deepfake video, voice cloning, AI phishing, and synthetic identity fraud alongside the quantum computing threat. Proposes a multi-modal detection framework with post-quantum cryptographic protection, consumer-accessible design, and ACEA EthIQ ethical governance. Addresses EU AI Act compliance (August 2, 2026) and NIST post-quantum migration requirements. No proprietary implementation details disclosed; provisional patents filed.
This paper presents design philosophy and market positioning. Technical implementation details are protected by provisional patent applications filed with the USPTO (March 2026). No proprietary algorithms or methods are disclosed.
Q-LOQ, NIST PQC, deepfake detection, voice cloning, ACEA, EthIQ, AI fraud detection, consumer security, multi-modal analysis, post-quantum cryptography, EU AI Act
Q-LOQ, NIST PQC, deepfake detection, voice cloning, ACEA, EthIQ, AI fraud detection, consumer security, multi-modal analysis, post-quantum cryptography, EU AI Act
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