adaptive ERP systems

This paper introduces a framework to enhance the present enterprise resource planning (ERP) systems by integrating big data analytics and state-of-the-art artificial intelligence (AI)-driven cybersecurity mechanisms. Nowadays, due to the diversity and high volume of data, the use of business intelligence and analytics solutions is paramount for ERP systems. This use results in the optimization of enterprise resource planning functions. AI can be used with cybersecurity mechanisms to predict and stop the behavior of a potential threat, thus leading to an optimal cybersecurity model. However, the biggest challenge is the integration of run-time cybersecurity solutions with the enterprise resources in the ERP systems. The proposed solutions incorporate advanced AI-driven cybersecurity techniques for intrusion detection, anomaly detection innovation, and prediction-based mechanisms to mitigate potential threats at the onset. In particular, the paper proposes a set of measures and guidelines for IT stakeholders and business executives on how to integrate technology innovation while maintaining the ERP systems to be modern, relevant, and adaptive in a competitive business environment. We believe that our work is beneficial for both researchers and practitioners to systematically understand the significance of integrating big data analytics with the existing ERP functions and the application of AI in the cybersecurity model for enterprises. Our results emphasize that the implementation of big data and AI-based solutions within the organization will support innovation, safeguard security mechanisms, and lead to a sustainable position in the digital market.

In the modern world of digital technologies, several technological and managerial aspects share a strong intersection. Among others, three important aspects are big data, cybersecurity, and enterprise resource planning systems. In the IT era, organizations have been relying on applications for their growth and day-to-day activities. While it is essential to manage and operate ERPs to improve and attain new business heights, there are also big questions about continuous cyberattacks, security, and hacking issues. The ignorance and negligence of the management and staff in any of these have led them to a significant level of loss. Some instances have practically shown companies' reputations and prospects went down drastically due to such uncontrollable white-collar crimes. The growth of the digital era forces us to devise adept mechanisms and take stringent measures. Consequently, there is a need to transcend these major challenges in unison. In this exploratory study, we have proposed the potential and the possibility for deep learning in the area of big data and cybersecurity when closely knit with enterprise resource planning systems. Contemporary ERPs are like the central nervous system of a living organism: providing vital data and ensuring efficient operations. The key elements of today's organizations are reporting systems and transaction processing systems. The database systems that store business data are often architected with a mixture of different data management technologies. These conglomerate systems are often hybrids with a complex array of proprietary, open-source, and emerging cloud-based convergences. Considering the fast and rapidly changing IT environments, ERPs have to be adaptable to these data challenges. The security of an organization's data is a core issue and must be protected from IT-based security threats. Organizations must have reserved data hacked or be demoralized by hacking attempts. The harmonic convergence of data management, cybersecurity analytics, and deep learning provides profound new approaches for hardening the systems that manage and protect the data!