TRUST-CL Theory: A Data Reliability Framework for Decision Intelligence Systems

Reliable data forms the foundational layer of all decision-making systems, yet existing frameworks predominantly focus on data quality dimensions without fully addressing trustworthiness in dynamic, AI-driven environments. This paper introduces TRUST-CL Theory, a structured framework for evaluating data reliability across six dimensions: Traceability, Reliability, Usability, Security, Timeliness, and Contextual Logic.

Positioned as the Data Foundation Layer within a multi-layered decision intelligence architecture, TRUST-CL ensures that upstream data integrity supports downstream cognitive processing (EIARA), decision evaluation (DICE), and measurement (DMQS). The framework extends traditional data quality models by integrating context-awareness and logical alignment, which are critical in Explainable AI (XAI) and human-AI collaborative systems.

TRUST-CL contributes to both theory and practice by enabling systematic validation of data reliability before it enters decision pipelines, thereby reducing bias, improving transparency, and strengthening decision outcomes.