Amidst the rapid growth of AI applications, the need for a sophisticated permission system to regulate user interactions through natural language becomes evident. Traditional API-based access control with structured commands is insufficient for nuanced natural language requests, necessitating a dynamic, AI-driven approach. This guide explores using OpenAI's language models in conjunction with Permit.io to create an AI-driven prompt classification system for access control, focusing on understanding user intent and applying permissions based on roles and attributes. The transition from basic input validation and pattern-matching to AI-powered dynamic classification allows for more accurate permission assignments, although it is essential to maintain human oversight to prevent misinterpretations. The implementation involves defining user roles, resource types, and permissions, leveraging a Policy Decision Point (PDP) to evaluate access requests dynamically. This innovative approach ensures adaptive security, bridging the gap between natural language processing and structured security enforcement, while highlighting the importance of continual human supervision to mitigate potential AI errors.