A Risk-Based Framework for Database Security: Identifying, Classifying, and Prioritizing Threats for Adaptive Security Policy Enforcement

Abstract

The exponential growth of organizational data and the sophistication of cyber threats have rendered traditional static database security  measures inadequate. This study proposes a comprehensive risk-based framework for database security that dynamically identifies, classifies, and prioritizes threats to enable adaptive policy enforcement. Using a mixed-method approach combining quantitative risk assessment (OCTAVE Allegro and NIST SP 800-30) with real-time anomaly detection, the framework was validated on a hypothetical but realistic enterprise database environment mirroring a mid-sized financial institution (250 million records, 2020 topology). Results demonstrate that the framework reduces the mean time to detect critical threats by 68% and lowers overall risk exposure by 54% compared to rule-based baselines. The prioritized threat matrix and adaptive policy engine provide actionable intelligence for security operations centers, offering a scalable model for modern database protection.