AI-Powered Autonomous Audit Intelligence for Manufacturing Systems
Keywords:
Autonomous Audit Intelligence (AAI), Continuous Internal Auditing, Compliance Monitoring Systems, Manufacturing Audit Automation, Operational Anomaly Detection, Investigative Audit Triggering, Telemetry and Monitoring Infrastructure, Real-Time Data Acquisition, Data Quality Assurance, Audit Data Provenance, Secure Industrial Data Integration, Natural Language Processing for Audits, Information Retrieval for Compliance, Case-Based Reasoning Systems, Agent-Based Audit Tools, Machine Learning for Anomaly Detection, Two-Layer Audit Architectures, Industrial Risk and Compliance Management, Autonomous Audits in Process Industries, Empirical Audit Intelligence Case Studies.Abstract
Autonomous Audit Intelligence (AAI) is a specialized form of autonomous artificial intelligence that performs continuous internal audits in complex, data-rich operational environments such as manufacturing. An AAI system operates on data produced by operational systems to verify compliance with requirements. By monitoring requirements such as industry regulations, corporate policies, and best business practices, the monitoring and telemetry capabilities of AAI systems enable the detection of operational anomalies and deviations. Such anomalies in turn trigger investigative audits that seek to isolate and determine the cause of a specific incident. A two-layer architectural framework supports AAI for manufacturing infrastructure. The lower layer represents the telemetry, sensing, and monitoring infrastructure that provides extensive real-time data acquisition. The upper layer addresses data quality, integration, agility, security, and provenance. Several core AI technologies are required for the AAI systems: natural language processing, data quality assurance, information retrieval, case-based reasoning, agent-based tools, and machine learning for anomaly detection.
Several autonomous audit intelligence projects have been implemented in the domain of discrete manufacturing and have been supported by empirical case studies. Otherproject are currently being developed and poineered in utility-scale service industries such as water, power, and transportation. The empirical work has demonstrated the operation and value of autonomous audits using system-generated data but not yet in real-time, with semi-automated analysis of audio-visual data. The methodology and supporting technologies are now being brought to bear in a process industry—risks, compliance, and data quality in a large-scale gas production facility and real-time anomaly detection in an airport terminal.
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Copyright (c) 2025 Ethan Williams (Author)
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