Exploring the Role of Neural Networks in Big Data-Driven ERP Systems for Proactive Cybersecurity Management

The increasing reliance on Enterprise Resource Planning (ERP) systems for managing critical business functions has made them prime targets for cyber threats. Traditional security measures often fail to detect sophisticated attacks, especially in big data-driven environments where vast amounts of information are processed. Neural networks, a subset of artificial intelligence, offer a proactive approach to cybersecurity by leveraging pattern recognition, anomaly detection, and predictive analytics. This paper explores the role of neural networks in enhancing ERP system security, focusing on their ability to detect threats in real-time, automate incident responses, and improve fraud detection. Additionally, the integration of big data analytics with neural networks strengthens security frameworks by providing deeper insights into potential vulnerabilities. Despite challenges such as data privacy concerns, computational demands, and false positives, neural networks present a transformative solution for proactive cybersecurity management in ERP systems. As cyber threats continue to evolve, AI-driven security mechanisms will play an increasingly crucial role in protecting enterprise data and operations.