Enhancing organizational security through proactive cybersecurity risk management and continuous threat monitoring systems

The fact that digital infrastructures are growing at a high rate has increased the type and frequency of risk to and occurrence of cybersecurity challenges to modern organizations. The conventional surveillance means will tend to either use the predetermined principles of detection or separate anomaly detection models that limit their ability to effectively prioritize security occurrences and respond to the appeared threats. This leads to the relevance of dynamic monitoring systems which integrate the behavioral detection as well as the situational risk assessment. The proposed research presents a Risk-Adaptive Continuous Threat Monitoring (RA-CTM) model, which includes the integration of anomaly-based behavioral detection and automatic response mechanisms and contextual estimation of risk. The framework has adopted various enterprise security telemetry including network traffic log, system events and cloud monitoring. The identified anomalies are examined with the aid of likelihood-impact risk model to quantify the degree of cybersecurity risk and enable the adaptive responses. Experimental evaluation demonstrates that the proposed framework achieves a detection accuracy of 95.2%, with a precision of 93.8%, recall of 94.5%, and an F1-score of 94.1%. The comparative analysis indicates that the performance is excellent compared to the performance of the conventional intrusion detection systems and baseline anomaly detection models, which are Isolation Forest, Local Outlier Factor, and One-Class Support Vector Machine. Behavioral anomaly detection and contextual risk modeling are useful to a considerable extent in improving the functionality of cybersecurity monitoring and encouraging proper prioritization of security incidents within the enterprise environment.