An Intellectual Zero Trust Security Framework Using Deep Reinforcement Learning for Predictive Threat Mitigation in AI-Based Fraud Detection Systems

The rapid growth of Artificial Intelligence (AI) driven financial platforms has led to an increase in sophisticated cyberattacks and fraudulent activities that easily bypass traditional security controls. Current fraud detection mechanisms mostly utilize fixed rule-based engines, monitored classifiers, based on signature, or high-dimensional decision space and all of them find it difficult to cope with changing threat patterns, zero-day attacks, and high-dimensional decision spaces. Even though the recent research has covered machine learning and deep learning architectures, the solutions remain to have significant limitations, such as slow adaptation to novel threats, lack of proactive defense, high rates of false-positives, and incapacity to implement dynamic trust assessment in real-time AI-based fraud detection systems. To counter such limitations, this research proposes an Intellectual Zero Trust Security Framework that is based on Deep Reinforcement Learning (DRL) to mitigate threats in predictive mode in an attempt to detect fraud in AI-based systems. The suggested framework would incorporate a policy-optimization DRL agent with an active trust-evaluation mechanism that would monitor dynamically user behaviors, system interaction, and new anomaly patterns. Incorporating state-state modeling, reward-shaping, and incremental threat-scoring, the DRL-Zero Trust agent is able to learn the best security actions which include access restriction, adaptive authentication, and anomaly-suppression without any predefined rules. Evaluation of the proposed DRL-Zero Trust model on a benchmark dataset of fraud detection shows that it significantly outperforms classical classifiers, including K Nearest Neighbor (KNN), Random Forest, Logistic Regression (LR) and Support Vector Machine (SVM). The proposed model achieved 98.7% accuracy, 98.4% precision, 98.9% recall and an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.995, and is better resistant to zero-day attacks and adversarial instances. These findings verify that the suggested framework presents a very flexible, intelligent, and proactive defense system that can protect the modern AI-driven financial systems.