ZTCloudGuard: Zero Trust Context-Aware Access Management Framework to Avoid Misuse Cases in the Era of Generative AI and Cloud-based Health Information Ecosystem

• URL: http://arxiv.org/abs/2312.02993v1
• Date: Tue, 28 Nov 2023 22:12:07 GMT
• Title: ZTCloudGuard: Zero Trust Context-Aware Access Management Framework to Avoid Misuse Cases in the Era of Generative AI and Cloud-based Health Information Ecosystem
• Authors: Khalid Al-hammuri, Fayez Gebali, Awos Kanan,
• Abstract summary: This article proposes a zero-trust-based context-aware framework for managing access to the cloud ecosystem. The framework has two main scoring schemas to maintain the chain of trust. The analysis is based on a pre-trained machine learning model to generate the semantic and syntactic scores.
• Score: 0.5530212768657544
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Managing access between large numbers of distributed medical devices has become a crucial aspect of modern healthcare systems, enabling the establishment of smart hospitals and telehealth infrastructure. However, as telehealth technology continues to evolve and Internet of Things (IoT) devices become more widely used, they are also becoming increasingly exposed to various types of vulnerabilities and medical errors. In healthcare information systems, about 90\% of vulnerabilities emerged from misuse cases and human errors. As a result, there is a need for additional research and development of security tools to prevent such attacks. This article proposes a zero-trust-based context-aware framework for managing access to the main components of the cloud ecosystem, including users, devices and output data. The main goal and benefit of the proposed framework is to build a scoring system to prevent or alleviate misuse cases while using distributed medical devices in cloud-based healthcare information systems. The framework has two main scoring schemas to maintain the chain of trust. First, it proposes a critical trust score based on cloud-native micro-services of authentication, encryption, logging, and authorizations. Second, creating a bond trust scoring to assess the real-time semantic and syntactic analysis of attributes stored in a healthcare information system. The analysis is based on a pre-trained machine learning model to generate the semantic and syntactic scores. The framework also takes into account regulatory compliance and user consent to create a scoring system. The advantage of this method is that it is applicable to any language and adapts to all attributes as it relies on a language model, not just a set of predefined and limited attributes. The results show a high F1 score of 93.5%, which proves that it is valid for detecting misuse cases.

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