AI- Powered Iot Security With Lstm,rl&pq Encryption

In an increasingly digital world, the threats to cybersecurity are becoming more frequent, complex and large-scale, calling into question the adequacy of traditional rule-based defense systems. In this paper, we present a unified AI-based cybersecurity framework for anomaly detection, data integrity verification, incident response automation and long-term cryptographic resilience. The framework is comprised of four major parts: (i) Long Short-Term Memory (LSTM) network to detect temporal anomalies, (ii) homomorphic hashing with SHA-256 and hidden salts to verify real-time data integrity, (iii) reinforcement learning based on Q-learning to automate threat response, and (iv) lattice-based encryption based on the Learning With Errors (LWE) problem to provide resistance against quantum era attacks. The system was tested in a simulated IoT network environment and showed high accuracy in detecting anomalies, differentiating between original and tampered data, and adaptively responding to different levels of cyber threats. The interaction between these components enables the framework to function autonomously and contextually, enhancing scalability and responsiveness in resource-constrained digital infrastructures. In conclusion, this study finds that the proposed framework overcomes the main drawbacks of current methods by providing a scalable, adaptive, and future-proof cybersecurity solution. The results show its promise for deployment in real-world scenarios such as smart cities, healthcare systems and critical infrastructure. Future work will focus on improving real-time adaptability and validating performance in live, heterogeneous environments.