From Signal Integrity to Decision Integrity: Autonomous Validation Architectures for Institutional Governance Systems

Abstract

Modern institutions increasingly rely on complex data systems to support decisions that affect public resources, regulatory compliance, and broader societal outcomes. Despite this reliance, many decision-making systems lack structured mechanisms to ensure that the data informing these decisions is accurate, consistent, and validated before use. As a result, errors are often identified only after decisions have been made, which can lead to misallocation of resources and weakened institutional trust. In contrast, engineering disciplines such as telecommunications have long developed rigorous approaches to maintain signal integrity in environments affected by noise and interference. This paper introduces the concept of Decision Integrity and proposes an Autonomous Validation Architecture model that applies these principles to institutional governance systems. The study conceptualizes institutional data inputs as decision signals that are subject to distortion, inconsistency, and validation gaps as they move through organizational systems. The proposed framework embeds validation checkpoints, threshold calibration mechanisms, and feedback control processes within data systems before decisions are executed. By treating validation as a core system function rather than a retrospective audit activity, the model supports a transition from reactive correction to preventive integrity control. The paper outlines the system architecture, governance integration strategies, and potential applications across nonprofit, academic, and public funded environments. By linking engineering principles with institutional decision processes, this research offers a structured approach to improving accountability, strengthening decision reliability, and building greater confidence in complex decision-making systems.