Researchers rely on confidential and sensitive microdata provided by national statistical institutes and other organizations, making disclosure control a critical challenge. Manual output checking processes are time-consuming and require expert knowledge, limiting scalability. This paper presents an automated framework that integrates large language models (LLMs), prompt engineering, and workflow automation (n8n) for statistical disclosure control (SDC). The system introduces an AI-driven output validation, including code generation, data processing, and risk assessment, allowing researchers to pre-check their outputs via a Seamless Within-Activity Review (SWAR) approach. Key challenges such as computational costs, confidentiality concerns, and the need for human oversight are addressed, and reinforcement learning is proposed to enhance future risk evaluation. The framework marks a step toward scalable, privacy-compliant, AI-assisted disclosure control in official statistics.