An AI-Driven Hybrid Cryptographic Model for Intelligent Data Security

Abstract

With the increasing digitization of sensitive information, ensuring robust data security has become a critical challenge. Traditional encryption methods, such as AES and RSA, provide strong protection but face limitations in computational efficiency, adaptability, and resistance to emerging cyber threats, including quantum computing attacks. Existing encryption models rely on static key generation and predefined security protocols, which can be vulnerable to sophisticated attacks. In this paper, we propose a novel Hybrid AI-Driven Encryption and Decryption Method that integrates artificial intelligence (AI) with cryptographic techniques to enhance security, adaptability, and robustness. Unlike conventional approaches, the proposed method employs machine learning for dynamic key generation, anomaly detection, and adaptive encryption strength adjustments based on real-time threat analysis. A generative adversarial network (GAN) is utilized for unpredictable key generation, ensuring high randomness and security. Additionally, AI-based anomaly detection monitors decryption processes to prevent unauthorized access attempts. Experimental results demonstrate the effectiveness of our approach, achieving a 30\% improvement in decryption anomalies, and enhanced resistance to brute-force attacks. By integrating AI into encryption, this method not only strengthens data security but also optimizes computational resources, making it a viable solution for future cybersecurity applications. The proposed hybrid AI-cryptographic model represents a significant advancement in secure communication, paving the way for quantum-resistant and self-learning security frameworks.