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@article{186406,
        author = {Aditya Arun Ranaware and Nandkumar Aniruddha Kaldhone and Sahil Nitin Shinde and Pranjal Dattatray Sahane and Prof. Saba Chaugule},
        title = {AI-Based Compliance Policy Recommender},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {1691-1695},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186406},
        abstract = {In today’s data-driven world, organizations must follow various national and international data protection and privacy rules. Managing and pinpointing the right compliance policies can be complicated and time-consuming. It requires both legal knowledge and technical skills. This paper introduces an AI-Based Compliance Policy Recommender System that simplifies the process of identifying and suggesting relevant compliance frameworks based on an organization’s industry, location, and data-handling practices. The system uses Natural Language Processing (NLP) and Machine Learning (ML) techniques to analyze company information and match it with appropriate regulatory standards. The backend is built with Flask and MySQL, and the AI engine uses keyword extraction and similarity analysis to generate accurate recommendations. This solution reduces human error, shortens analysis time, and helps organizations stay ready for compliance. Experimental results show that the system offers relevant and context-aware recommendations, proving its value as a useful tool for compliance management in modern businesses.},
        keywords = {Artificial Intelligence (AI), Compliance Policy, Information Security Management System (ISMS), Machine Learning (ML), Natural Language Processing (NLP), Regulatory Frameworks, Policy Recommendation System, Data Protection.},
        month = {November},
        }