PRIVACY-PRESERVING CUSTOMER DATA MANAGEMENT USING HYBRID AI-CRYPTOGRAPHY MODELS

5G networks, IoT devices, and cloud computing have all made it easier for people to come up with new ideas, but they have also made systems more vulnerable to increasingly complicated cyber assaults. Traditional security measures work to some extent, but they can't handle the complicated, limited resources, and constantly changing digital environments we live in today. This paper introduces an innovative framework for creating resilient, scalable, and flexible security systems via the integration of artificial intelligence and advanced cryptography methodologies. A hybrid deep learning model that incorporates CNN, LSTM, and Autoencoders (AE) is utilised to detect threats. This model is more accurate than baseline methods and has fewer false positives. Dynamic key rotation across IoT, mobile, and cloud infrastructures may improve cryptographic resilience via reinforcement learning-driven key management. Blockchain-based trust management makes transactions and contracts more accountable and open by lowering communication costs and making sure that model training that protects privacy is done via federated learning. Experimental evaluations of healthcare, IoT, and 5G datasets indicate that the system exhibits enhanced accuracy (94.3%), increased resilience to adversarial attacks (with an attack success rate reduction of 70%), and improved energy efficiency. Multi-objective optimisation has shown that the framework's performance, scalability, and security balance is good. This makes it a good choice for next-generation digital infrastructures. This work aims to include post-quantum solutions and explainable AI into future advancements, facilitating the convergence of AI and cryptography to enhance security.