Artificial Intelligence Trust, Risk and Security Management (AI TRiSM): Frameworks, applications, challenges and future research directions

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dc.authoridAli, Mohamed Khalif/0000-0001-7312-1493
dc.authoridHabbal, Adib/0000-0002-3939-2609
dc.authoridAbuzaraida, Mustafa Ali/0000-0002-9327-8639
dc.contributor.authorHabbal, Adib
dc.contributor.authorAli, Mohamed Khalif
dc.contributor.authorAbuzaraida, Mustafa Ali
dc.date.accessioned2024-09-29T15:57:10Z
dc.date.available2024-09-29T15:57:10Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractArtificial Intelligence (AI) has become pervasive, enabling transformative advancements in various industries including smart city, smart healthcare, smart manufacturing, smart virtual world and the Metaverse. However, concerns related to risk, trust, and security are emerging with the increasing reliance on AI systems. One of the most beneficial and original solutions for ensuring the reliability and trustworthiness of AI systems is AI Trust, Risk and Security Management (AI TRiSM) framework. Despite being comparatively new to the market, the framework has demonstrated already its effectiveness in various products and AI models. It has successfully contributed to fostering innovation, building trust, and creating value for businesses and society. Due to the lack of systematic investigations in AI TRiSM, we carried out a comprehensive and detailed review to bridge the existing knowledge gaps and provide a better understanding of the framework from both theoretical and technical standpoints. This paper explores various applications of the AI TRiSM framework across different domains, including finance, healthcare, and the Metaverse. Futhermore, the paper discusses the obstacles related to implementing AI TRiSM framework, including adversarial attacks, the constantly changing landscape of threats, ensuring regulatory compliance, addressing skill gaps, and acquiring expertise in the field. Finally, it explores the future directions of AI TRiSM, emphasizing the importance of continual adaptation and collaboration among stakeholders to address emerging risks and promote ethical and enhanced overall security bearing for AI systems.en_US
dc.identifier.doi10.1016/j.eswa.2023.122442
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85179498220en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2023.122442
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4632
dc.identifier.volume240en_US
dc.identifier.wosWOS:001117718700001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryDiğeren_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAI TRiSM frameworken_US
dc.subjectAI TRiSM Orchestrationen_US
dc.subjectAI TRiSM Adaptiveen_US
dc.subjectModelOpsen_US
dc.subjectDeepfake Technology and Adversarial attacksen_US
dc.titleArtificial Intelligence Trust, Risk and Security Management (AI TRiSM): Frameworks, applications, challenges and future research directionsen_US
dc.typeReviewen_US

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