In the era of artificial intelligence, the demand for transparency and explainability has given rise to Explainable AI (XAI). In this paper, we analyze a design-centric approach to conceptualizing validation systems tailored for XAI. We explore the key principles and frameworks necessary for developing validation infrastructures that meet the technical requirements of AI models and address the ethical and regulatory considerations of explainability. By integrating interdisciplinary insights from computer science, ethics, and user experience design, we propose a comprehensive blueprint for building robust, XAI-ready validation systems that enhance trust and accountability in AI applications.