An AI-Enabled Aviation Integrity Management Framework for Predictive Maintenance, NDT Data, Cybersecurity, and Operational Risk

Abstract

Aviation organizations depend on connected sensors, inspection systems, maintenance platforms, operational databases, and artificial intelligence, yet fragmented governance often separates physical integrity, information integrity, cybersecurity, and operational risk—leaving diagnostic ambiguity unresolved when a single anomaly could plausibly reflect degradation, sensor error, process error, software defect, or manipulation. To address this gap, this paper develops the Aviation Intelligence and Integrity Framework (AIIF) through a disclosed-protocol conceptual synthesis of systems safety, asset management, condition-based maintenance, information systems, cyber-physical security, human-automation interaction, and artificial intelligence risk management. The framework links eight constructs across six layers, differentiates AIIF from EASA, ICAO, NIST, DO-178C, DO-326A, ARP4754A, and asset-management approaches through an explicit comparison, and proposes twelve testable relationships, each with its own causal mechanism and boundary conditions. Furthermore, it presents a diagnostic separability model formalized as a likelihood-based classification problem, a measurement model covering all eight constructs, an eight-step implementation process with step-specific entry and exit criteria, and two fully worked application examples alongside six brief illustrative sketches. Overall, the framework offers a structured, testable basis for designing, governing, and monitoring aviation artificial intelligence; while its claims remain hypotheses pending empirical validation, it effectively places operational efficiency within explicit safety and cybersecurity constraints and specifies the studies needed to test each proposition.