AI-Powered Encryption Revolutionizing Cybersecurity with Adaptive Cryptographic Algorithms

This paper proposes a state-of-the-art encryption technique that integrates artificial intelligence into a dynamic, content-aware security system. We introduce an AI-powered encryption framework that automatically adjusts cryptographic parameters based on message sensitivity, effectively balancing security requirements and computational efficiency. The system combines a DistilBERT-based neural network for real-time content sensitivity analysis with a flexible encryption mechanism that adapts key lengths, iteration counts, and entropy levels on the fly. Our implementation demonstrates significant adaptability, with a correlation of 0.974 between content sensitivity and security parameters. The system distinguishes between different security requirements, using 32-byte keys with millions of iteration rounds for high-sensitivity content (sensitivity score of 8.64) and 16-byte keys with reduced iterations for low-sensitivity messages (sensitivity score of 3.10). The processing time scales linearly with security requirements, ranging from 300 ms for low-sensitivity content to 732 ms for high-security encryption. Performance evaluation was highly effective, with the system achieving an overall score of 8.64/10, including 9.87/10 for adaptability and 9.12/10 for performance efficiency. The security level rated high at 7.37/10 while maintaining manageable computational overhead. The framework effectively handled different types of content without sacrificing encryption-decryption accuracy across all levels of sensitivity. This work signifies a significant leap forward in the field of adaptive cryptography, demonstrating the capabilities of AI-driven security systems that can automate and optimize encryption parameters without compromising security standards. These results suggest that this approach may well represent the future of encrypted communications, providing scaled security appropriately without human intervention.