Shadow Sentinal: A Semi-Autonomous AI-Based Intelligence Monitoring Framework

Abstract

The complexity of cybersecurity management has greatly increased due to the quick growth of cloud computing, Internet of Things (IoT) infrastructures, remote authentication systems, and distributed enterprise networks. Massive amounts of structured and unstructured behavioral data are produced by contemporary digital ecosystems, rendering conventional rule-based monitoring systems progressively less useful. Zero-day attacks, insider threats, and changing adversarial tactics are difficult to detect using signature-based detection and static threshold models. Additionally, a high number of false positive alerts causes alert fatigue, decreases analyst productivity, and delays incident response. In order to improve proactive cyber defense through hybrid machine learning integration, this study suggests Shadow Sentinal, a Semi-Autonomous AI-Based Intelligence Monitoring Framework. Within a layered validation framework, the suggested architecture combines Random Forest for supervised threat classification and Isolation Forest for unsupervised anomaly detection.
To create dynamic risk assessments, a contextual risk scoring engine combines anomaly scores, classification probabilities, and environmental variables like device change, geolocation, and temporal irregularity. The framework strikes a balance between automation and human supervision while functioning in Semi-Autonomous Mode. Analyst validation is necessary for high-risk actions in order to avoid operational disruptions and maintain quick threat mitigation capabilities. Continuous model adaptation is made possible by a feedback-driven retraining mechanism, which eventually lowers false positives and increases precision. In comparison to conventional intrusion detection systems, the suggested hybrid architecture achieves 89–92% accuracy, 90% precision, 88% recall, and roughly 45% reduction in false positive rates, according to experimental simulations performed on 1,900 synthetic behavioral event samples. The framework creates a modular and scalable basis for the future integration of fully autonomous cyber defense mechanisms, ensemble modeling, and reinforcement learning.