Exon–Intron-Based DNA Encryption Systems for Artificial Intelligence Integration in Quantum Information Processing

The regulatory and coding structure of DNA, characterized by alternating exon and intron regions, offers a novel paradigm for bio-inspired encryption in DNA computing. In this paper, we propose a quantum-resilient encryption model that mirrors the biological mechanisms of RNA splicing, wherein only select exons are expressed from pre-mRNA. By using this selective expression mechanism as a cryptographic template, we demonstrate how exons can be interpreted as signal data and introns as noise or decoy code, offering dynamic encoding schemes. This method is evaluated in the context of DNA computers interfaced with artificial intelligence (AI), introducing a hybridized computational system capable of adaptive encoding and biological learning. The model is further supported with implications in post-quantum cryptography, steganographic masking, and AI-based key derivation functions.