In the realm of Natural Language Processing (NLP), adversarial attacks have emerged as a critical threat, challenging the integrity and reliability of modern AI systems. This paper introduces a comprehensive, AI-based security framework focus on fortifying NLP systems against an array of adversarial attacks, including model extraction, backdoor, and evasion attacks. During data and collection processing, an unsupervised learning algorithm filters the perturbed data, while a GPT-2 based Backdoor Defense with Outlier Word Detection (ONION) strategy scans for potential backdoors. The model training phase leverages self-adaptive noise to obfuscate outputs, avoiding model extraction attacks. Upon deployment, the Frequency-Guided Word Substitutions (FGWS) mechanism continuously inspects inputs, flagging suspicious word substitution patterns to counteract evasion attacks. Furthermore, our security framework exhibits dynamism, it recognizes and adapts to new adversarial patterns in real-time by deploying an AI-driven feedback loop based on reinforcement learning. Our results highlight the importance of creating stronger, more adaptable NLP systems as they face ever-changing adversarial threats.