Towards Intelligent Zero Trust Systems: Merging AI Based Threat Modeling with Privacy Centric Controls

Zero Trust has become a key paradigm of the cybersecurity, anticipating the motto of never trust, always verify. Although it is increasingly being adopted in critical domains, traditional Zero Trust implementations use mostly fixed policies and fixed access control policies, thus making them poorly suited to deal with threat that is environments that are more dynamic. At the same time, the introduction of the innovative artificial intelligence (AI) functionality in cybersecurity demonstrated the potential of automating detection, increasing flexibility, and offering 24/7 safety. However, the introduction of AI to security systems raises legitimate questions about privacy of data, transparency, and compliance with the regulations. The paper suggests a smart Zero Trust framework that combines intelligent threat modeling, based on AI, with privacy- focused controls, which would remain flexible to changing threats and maintain user-confidence and privacy of data. Through a comprehensive literature review, we will create a conceptual framework that demonstrates the role of AI in enhancing adaptive threat detection and prevention in Zero Trust. Privacy preserving systems such as federated learning, differential privacy, and encryption-based access controls are also examined in the paper as the basis of having a trustworthy deployment. The practicality and effectiveness of the proposed approach are evidenced by a case-study deployment to a simulated cloud-based enterprise context, showing that detection accuracy, policy enforcement as well as compliance assurance are greatly improved compared to the conventional models. The results therefore aid in the development of cybersecurity by suggesting a smart Zero Trust architecture that supports flexibility, effectiveness and privacy and therefore leading to sustainable and reliable digital ecosystems.