Author | Adenola, Victoria Mojisola | |
Date Accessioned | 2023-06-05T13:48:11Z | |
Date Available | 2023-06-05T13:48:11Z | |
Date Created | 2023-05 | |
Date of Issue | 2023-04-27 | |
xmlui.metadata.dc.date.submitted | May 2023 | |
Identifier (URI) | http://hdl.handle.net/10342/12838 | |
Description | The foundation of cybersecurity is identity and access management (IAM). Its methods, procedures, and guidelines control identity access to digital resources and define the scope of identity permission over the resources. Every week, a new data breach or cyber threat is reported. A significant number of data breaches are caused by ineffective security features, software vulnerabilities, human error, malicious insiders, and the misappropriation of access and privileges. Artificial intelligence (AI) techniques can upgrade the access management system. As a result, research into artificial intelligence in IAM is required to enable organizations to take a more detailed and flexible approach to authentication and access control to mitigate cyber threats and other IAM challenges. This study explores the relationship between access management systems and artificial intelligence with regard to AI applications in identity and access management, specifically the monitoring, administration, and control of access privileges. The objective of this study was to provide evidence from the relevant literature to help understand how AI works in mitigating identified IAM challenges. The findings in this study demonstrate how artificial intelligence strengthens identity and access management in mitigating growing cyber threats, automating processes, and keeping up with technological advancements. | |
Mimetype | application/pdf | |
Language | en | |
Publisher | East Carolina University | |
Subject | Access control | |
Subject | Artificial intelligence | |
Subject | Access management | |
Subject | Identity management | |
Subject | AI techniques | |
Title | Artificial Intelligence based Access Management System | |
Type | Master's Thesis | |
xmlui.metadata.dc.date.updated | 2023-06-02T15:40:36Z | |
Department | Technology Systems | |
xmlui.metadata.dc.degree.name | M.S. | |
xmlui.metadata.dc.degree.level | Masters | |
xmlui.metadata.dc.degree.discipline | MS-Network Technology | |
xmlui.metadata.dc.degree.grantor | East Carolina University | |
xmlui.metadata.dc.degree.department | Technology Systems | |
xmlui.metadata.dc.type.material | text | |