The integration of Artificial Intelligence (AI) in Cyber-Physical Production Systems (CPPS) is rapidly transforming modern manufacturing. AI is becoming instrumental in improving operational efficiency, decision-making, and robotic service orchestration. However, the opaque nature of AI decision-making introduces significant risks in environments where AI-driven decisions can have physical consequences. This study proposes a novel Model-Driven Service-Oriented Adaptive Data Basin framework (AI.Lock), which applies strict control over the data accessible to AI engines and limits their actions within predefined, model-driven boundaries. By adopting a model-based architecture, this framework enforces data restriction to a need-to-know basis for AI engines, ensuring that only relevant data subsets are accessible based on the service or task at hand. Additionally, it encapsulates AI behaviours within the scope of predefined models, mitigating safety risks and ensuring that actions remain predictable, traceable, and compliant with factory operations. The framework is particularly suited for multi-party, and multi-vendor environments, where model-driven approaches provide verifiable constraints. Furthermore, the proposed framework leverages Open Platform Communications Unified Architecture’s (OPC-UA) communication and information modelling capabilities, integrating with digital twins to ensure seamless adaptation in dynamic CPPS environments. This study explores the benefits of using a model-driven approach to enhance safety, reduce risk, and provide a foundation for secure AI integration in modern manufacturing.