Integrating Machine Learning and Blockchain for Enhanced Data Security in Business Intelligence Systems

Abstract

In the era of digital transformation, businesses are increasingly relying on data-driven insights to enhance their operational efficiency and decision-making processes. However, the growing volume and complexity of data also expose organizations to various security vulnerabilities and threats. This paper explores the integration of Machine Learning (ML) and Blockchain technology as a dual approach to enhancing data security within Business Intelligence (BI) systems. By leveraging the predictive capabilities of ML, organizations can identify and mitigate potential security threats in real-time, improving the overall resilience of their data infrastructures. Machine learning algorithms can analyze patterns and anomalies in data access and usage, enabling proactive measures against unauthorized access and data breaches. On the other hand, Blockchain provides a decentralized and immutable ledger that enhances data integrity and traceability. It ensures that data remains secure, transparent, and tamper-proof throughout its lifecycle. This combination of ML and Blockchain creates a robust framework for securing sensitive information, as it allows for continuous monitoring of data interactions while ensuring the authenticity of the data stored. Moreover, the integration facilitates automated responses to identified threats, reducing the potential impact of cyberattacks. The synergy between these technologies not only fortifies data security but also instills greater trust in BI systems, as stakeholders can rely on the integrity of the data used for critical decision-making.