Mitigating Cyber Risks in AI-Driven Circular Economy Implementations

This article explores the integration of artificial intelligence (AI) into circular economy (CE) systems, focusing on its potential to enhance sustainability by optimizing resource utilization, reducing waste, and improving supply chain processes. It highlights how AI-driven innovations such as pre...

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Bibliographic Details
Main Authors: Iryna Bashynska, Olha Prokopenko
Format: Article
Language:English
Published: University of Applied Sciences in Bielsko-Biała 2024-12-01
Series:Zeszyty Naukowe Wyższej Szkoły Finansów i Prawa w Bielsku-Białej
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Online Access:https://asej.eu/index.php/asej/article/view/837
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Summary:This article explores the integration of artificial intelligence (AI) into circular economy (CE) systems, focusing on its potential to enhance sustainability by optimizing resource utilization, reducing waste, and improving supply chain processes. It highlights how AI-driven innovations such as predictive maintenance, waste sorting, and big data analytics contribute to the effective implementation of CE principles. At the same time, the study addresses the cybersecurity risks associated with these systems, including data breaches, system vulnerabilities, and ethical concerns. The research underscores the importance of adopting robust cybersecurity frameworks, such as the NIST AI Risk Management Framework and ISO/IEC 27001, to mitigate these risks and ensure the scalability and sustainability of AI-driven CE initiatives. Additionally, the article examines public perceptions of AI's societal impact, revealing optimism about productivity and efficiency but concerns regarding job market disruptions. Recommendations for organizations include proactive cybersecurity strategies, leveraging emerging technologies like blockchain, and fostering interdisciplinary collaboration. This study provides actionable insights for achieving a balance between technological advancement and sustainability in AI-enabled circular economy systems
ISSN:2543-9103
2543-411X