Cross-sector AI framework for risk detection in national security, energy and financial networks
1 Department of Information Technology, Washington University of Science and Technology, USA.
2 Information Security and Digital Forensics, University of East London. UK.
3 Department of Accounting & Data Analytics, Drexel University, USA.
Review Article
World Journal of Advanced Research and Reviews, 2023, 18(01), 1307-1327
Publication history:
Received on 12 March 2023; revised on 21 April 2023; accepted on 28 April 2023
Abstract:
The increasing complexity and interdependence of critical national infrastructures—such as defense systems, energy grids, and financial institutions—necessitate a unified, intelligent approach to real-time risk detection. Traditional sector-specific risk management systems, often operating in isolation, are inadequate for identifying emerging threats that exploit intersectoral vulnerabilities. Artificial Intelligence (AI) offers transformative capabilities for detecting, predicting, and responding to risks across these domains. However, current implementations remain largely siloed, lacking interoperable frameworks that enable cross-sector intelligence sharing, collaborative threat modeling, and unified response coordination. This article proposes a Cross-Sector AI Framework designed to integrate and standardize risk detection across national security, energy, and financial networks. Drawing from advancements in federated learning, graph-based anomaly detection, and real-time decision support systems, the framework leverages shared indicators of compromise (IoCs), behavior analytics, and sector-specific ontologies. By adopting a modular architecture supported by edge-cloud collaboration and dynamic policy reinforcement, the proposed system enables scalable, privacy-preserving, and adaptive risk governance. Through comparative case analysis and system-level simulations, we demonstrate how cross-sector intelligence fusion can reduce false positives, accelerate threat response, and prevent cascading failures. Furthermore, the framework is designed to be resilient against adversarial AI attacks and compliant with regulatory mandates across sectors. This cross-sectoral AI model represents a shift toward proactive national resilience, providing decision-makers with real-time situational awareness and predictive foresight. The work concludes by outlining implementation challenges, including data sovereignty, ethical considerations, and multi-agency coordination.
Keywords:
Artificial Intelligence Integration; National Security Risk; Energy Grid Protection; Financial Network Surveillance; Cross-Sector Resilience; Real-Time Threat Detection
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0