The rapid evolution of generative artificial intelligence has amplified the sophistication of deepfake-based cyberattacks, posing unprecedented risks to sectors such as finance, healthcare, media, and national governance. These synthetic manipulations erode digital trust, compromise data integrity, and challenge conventional authentication mechanisms. To counter these adaptive and cross-sector threats, a Zero-Trust security approach is essential—one that eliminates implicit trust, enforces continuous verification, and dynamically manages access based on contextual risk. However, the traditional Zero-Trust model often operates as a black box, limiting visibility into decision-making processes and hindering user confidence. This research introduces an Explainable Zero-Trust Orchestration framework that embeds Explainable Artificial Intelligence (XAI) components within the trust management cycle to ensure transparency, interpretability, and accountability in automated defense mechanisms. The proposed framework integrates sector-specific threat modeling with explainability layers that analyze, justify, and adapt access control decisions in real time. By coupling AI-driven anomaly detection with human-understandable insights, the orchestration enhances resilience against deepfake-driven intrusions while reducing false positives and improving decision traceability. Experimental evaluation demonstrates that this hybrid approach not only strengthens cyber-resilience across diverse domains but also aligns security operations with human trust principles—fostering responsible, transparent, and verifiable defense systems for the era of intelligent threats.

Keywords: Zero-Trust Security, Explainable AI (XAI), Deepfake Detection, Cyber-Resilience, Multi-Sector Framework, Threat Orchestration, Trust Governance