An Analytics-Driven Framework for Continuous IT Control Monitoring and Risk-Based Audit Modernization in U.S. Organizations

Abstract

Continuous IT control monitoring promises earlier detection of control failure, yet faster testing does not by itself produce reliable assurance. This study develops an evidence-centered framework that connects risk signals, authoritative populations, reproducible analytics, professional review, exception disposition, and verified remediation. A structured integrative review and design-science synthesis draws on research in continuous auditing, audit analytics, process mining, internal control, cybersecurity, and resilience, together with relevant professional standards. The analysis identifies five conditions for defensible reliance: explicit control objectives, validated source data, version-controlled test logic, documented human judgment, and outcome-based remediation testing. The proposed framework organizes these conditions into a closed assurance cycle and defines measures for population coverage, detection latency, alert quality, investigation consistency, remediation time, recurrence, and residual exposure. Its main contribution is a practical distinction between continuous testing and continuous assurance. Frequent automated tests improve visibility, while assurance depends on evidence quality, reviewer independence, and traceable conclusions. The framework supports bounded pilots and controlled expansion rather than immediate enterprise-wide automation. Because the study is conceptual, it does not establish causal performance gains. Longitudinal field studies should compare the framework with existing audit approaches across organizations, systems, and risk levels.