Artificial Intelligence for Autonomous Database Management in Saudi Arabia: Predictive Performance Monitoring and Intelligent Incident Prevention for Vision 2030

Abstract

-Saudi Arabia’s Vision 2030 is accelerating data - intensive digital government, cloud adoption, artificial intelligence (AI), cybersecurity, and highly available national platforms. As mission-critical database estates grow, conventional administration based on static thresholds, manual diagnosis, and reactive incident handling becomes difficult to scale. This paper examines how AI can support autonomous database management in Saudi Arabia through predictive performance monitoring, anomaly detection, root-cause analysis, and guardrailed incident prevention. A structured narrative review of 30 core sources published mainly between 2020 and 2026 synthesizes peer-reviewed database and AIOps research, selected industry documentation, and official Saudi digital and AI policy. The review shows that effective autonomy requires more than anomaly detection: heterogeneous telemetry must be transformed into contextual features, models must adapt to workload drift, anomalies must be linked to probable causes, and remediation must be constrained by explainability, policy, rollback, security, and human oversight. Building on these findings, the paper proposes a six-layer Saudi Autonomous Database Operations Framework spanning observability, operational data engineering, AI analytics, contextual diagnosis, decision orchestration, and governance. A five-stage maturity model guides progression from observable operations to policy-governed closed-loop automation. The framework aligns with Saudi priorities for data and AI adoption, advanced digital infrastructure, reliability, cybersecurity, responsible AI, and efficient digital services. The paper concludes that AI can strengthen database resilience when autonomy is introduced incrementally and governed by trustworthy telemetry, validated models, architecture-aware diagnosis, and controlled automation.