The term "Big Data" refers to data that has massive volume, is varied in nature, and is generated at a high velocity. This data is difficult to process using conventional database management tools, and it grows over time. Cybersecurity is not limited to securing software applications or infrastructures; instead, it focuses on safeguarding data systems from any kind of breach or unauthorized access. Enterprise Resource Planning, or ERP system, is an information system that integrates all business transactions, and the data present in the system flow between various units or departments of the company. Such systems are more often targets of cyberattacks because of the data they hold. AI-driven ERP cybersecurity applications are imperative for securing such systems against modern cyber threats. AI technologies provide automation, and when integrated with Big Data, it becomes a stronger tool for enhancing the security of any data system. This study is focused on AI and Big Data techniques that can be used in ERP cybersecurity systems. The proposed methodology can be used by any cybersecurity developer for building a data processing system or AI mechanism that can effectively process data from an enterprise system or any data system. Associative classification techniques can be impressively effective compared to conventional classification solutions in the development of an AI algorithm for detecting cyber threats against ERP systems. It is more effective in comparison to traditional algorithms, as very few works are available on associating associative rules on a complete log file of an organization containing organizational data for work purposes. Additionally, the proposed framework can be imperatively helpful for real-time surveillance and data management systems and may lead to future growth in the cybersecurity and digitization domain.