Cryptographic Resilience in the AI Quantum Age: A Predictive Indexing Approach to Entropy Assessment
Frontier Technologies Laboratory at the University of Cambridge Independent Third-Party Evaluation

Abstract—The emergent threats of AI-driven cryptanalysis and quantum computing may require new approaches to validating cryptographic entropy. Traditional statistical test suites, such as NIST SP 800-22, show limitations in detecting certain complex, non-linear patterns that intelligent adversaries might exploit. This paper presents an independent third-party evaluation of Entrokey’s proprietary Predictive Indexing framework, a novel assessment methodology leveraging Convolutional Neural Net-works (CNNs) for deep pattern recognition, and its integrated AI-driven entropy generation system using diffusion models. Our empirical analysis shows that while traditional tests pass flawed sources with 82.3% mean pass rate, Entrokey’s Predic-tive Indexing differentiates high-quality entropy (score: 0.649) from patterned sequences (score: 0.548) consistently across 100 iterations. Entrokey’s candidate selection mechanism, guided by its Predictive Indexing, achieves a maximum quality of 0.9484. A comprehensive ECC case study across 100 iterations shows that Entrokey achieves low LSB bias (3.57% ± 1.81%) among all tested sources, performing favourably compared to standard PRNGs. We also find that Entrokey-generated entropy appears computationally unpredictable, with adversarial LSTM models achieving 50.11% accuracy, consistent with random chance. Our independent analysis suggests that Entrokey’s Predictive Indexing methodology coupled with its diffusion-based generation represents a potentially valuable software-based approach to cryptographic entropy assessment.
Index Terms—Cryptographic entropy, quantum computing, AI security, pattern recognition, Predictive Indexing, NIST SP 800-22

Crypto Resilience in the AI Quantum Age-Entropy Assessment 1-8-2026