Intelligent Anomaly Detection in Large-Scale Database Environments: An AI-Enabled Framework for Saudi Digital Infrastructure

Abstract

Large-scale database environments supporting digital government, financial services, healthcare, smart-city platforms, cloud applications, and national data services generate continuous streams of performance metrics, logs, query statistics, configuration events, and security signals. Static thresholds and manual dashboard inspection are increasingly inadequate because normal behaviour changes with workload cycles, application releases, data growth, infrastructure scaling, and distributed-system dependencies. This paper develops an artificial-intelligence-enabled framework for intelligent anomaly detection in large-scale database environments, with particular attention to Saudi Arabia's rapidly expanding digital infrastructure under Vision 2030. A structured narrative review synthesizes peer-reviewed work on KPI-based database anomaly detection, multivariate time-series modelling, log analytics, deep learning, explainable anomaly detection, AIOps, and database root-cause analysis, together with Saudi policy sources on data and AI, digital government, cybersecurity, and cloud adoption. The proposed framework integrates six layers: governed observability, context-aware data engineering, multi-model anomaly detection, multimodal correlation and root-cause analysis, risk-based alert orchestration, and human-in-the-loop learning. It distinguishes point, contextual, collective, compound, workload, resource, query, availability, configuration, and security-related anomalies, and recommends combining robust statistical baselines with unsupervised machine learning, autoencoders, recurrent or transformer models, and log-sequence analysis rather than relying on a single algorithm. Evaluation is defined using both machine-learning metrics and operational outcomes, including event-level precision and recall, false-alert burden, detection latency, time-to-diagnosis, and stability under concept drift. The framework is aligned with Saudi requirements for responsible AI, resilient digital infrastructure, data protection, and cybersecurity. The paper argues that intelligent anomaly detection should be deployed as an auditable decision-support capability before progressing toward automated remediation, enabling Saudi organizations to improve reliability while preserving governance and expert oversight.