Hierarchical Trust-based Artificial Intelligence Governance Model for Data Protection

Abstract

The widespread adoption of artificial intelligence in organizations has introduced critical risks for trade secret protection, as AI systems may inadvertently disclose or infer sensitive information. Existing governance frameworks lack empirical validation, hierarchical trade-secret safeguards, and integration with zero-trust principles. This study presents a hierarchical trust‑based governance framework that explicitly prevents AI systems from accessing trade secrets beyond authorized organizational levels. A six-equation mathematical model quantifies trust level, access control strength, decision rights, AI usage eligibility, governance effectiveness, and knowledge advancement. The framework is implemented as the HTF Platform, a web application with two-factor authentication, real-time dashboards, and a policy decision confusion matrix. A real-world deployment with 250 audited access decisions achieved 91.6% accuracy, 94.7% precision, 91.6% recall, and 93.1% F1-score. The system architecture and operational flowchart are presented. The confusion matrix confirms that lower-tier users are correctly denied access to higher-tier trade secrets, while legitimate requests are reliably granted. The HTF Platform provides a practical, verifiable, and adaptive solution for safeguarding trade secrets in AI-driven organizations.