Unified Artificial Intelligence Driven Framework for Secure Data Engineering Threat Detection and Risk Intelligence in Digital Systems

Abstract

The increasing complexity of digital systems and the exponential growth of data have introduced significant challenges in ensuring data security, integrity, and resilience against cyber threats. Traditional security frameworks often operate in silos, limiting their effectiveness in identifying and mitigating sophisticated attacks. This research proposes a unified artificial intelligence (AI)–driven framework that integrates secure data engineering, real-time threat detection, and risk intelligence to enhance cybersecurity in modern digital environments. The framework leverages advanced AI techniques, including machine learning, deep learning, and anomaly detection, to analyze large-scale data streams and identify potential vulnerabilities and threats proactively. By combining data engineering pipelines with intelligent threat analytics, the system ensures continuous monitoring, rapid response, and adaptive learning capabilities. The proposed model emphasizes data quality, governance, and secure processing while enabling predictive risk assessment through AI-driven insights. Experimental evaluation demonstrates improved accuracy, reduced response time, and enhanced scalability compared to traditional methods. The study highlights the importance of integrating AI across all layers of digital systems to create a holistic security ecosystem. The findings suggest that a unified AI-driven approach can significantly strengthen cyber resilience and support informed decision-making in risk management.