In today’s cybersecurity situation, traditional defence mechanisms are often not sufficient for the development of threats such as zero-day attacks, brute force testing, and lateral movement technologies. In this article, the implementation of AI-powered Cyber Deception and Fake Data Injection Systems presents a new approach to cybersecurity. The proposed system uses Generative Adversarial Networks (GANs) and the Python Faker library to generate realistic but synthetic user information and network traffic. The goal is to confuse attackers, monitor their actions, and trigger warning messages with interactions with fake assets. The improves early detection and response. The system also contains adaptive deceptions that generates dynamically targeted fake answers based on attacker actions such as port scans and brute-force applications. Experimental verification within a simulated laboratory environment demonstrates the feasibility and effectiveness of the proposed system. The results show that integrating AI-drive Functions into SIEM tools, it can significantly improve and reduce the success rate of attackers. This work contributes to the growth areas of intellectual deception and aggressive defence in cybersecurity. This study contributes to the knowledge base by incorporating GAN-generated synthetic data with a sophisticated deception logic in a tightly integrated SIEM platform. Unlike previous approaches that used static deception, our system dynamically adapts fake responses to attacker actions, enhancing realism and support for early threat detection.