Designing Artificial Intelligence (AI) Based Secured Framework to Improvise the Data Security of Confidential Academic Records
Keywords:
Artificial Intelligence (AI), Secured Framework, Data Security, Confidential Academic RecordsAbstract
One of the difficulties associated with conducting analytics using artificial intelligence is trying to maximise utility while also safeguarding human rights and maintaining meaningful human control. In this situation, one of the most important things for policymakers and lawmakers to think about is how much they should let protection be done automatically in a society that is becoming more digital. Such Security-Preserving Technologies have the goal of implementing security-by-design into the back end and front end of digital services from the very beginning of the development process. They watch over the data architectures to make certain that they are safe and sound, as well as ensuring that any data-related dangers are neutralised during the design phase as well as during operation. In this paper, we talk about recent trends in the development of tools and technologies that help make AI security analytics safe and reliable. We also give recommendations based on the research's findings and insights. We also talk about recent trends in the creation of tools and technologies that make AI security analytics safe and reliable. This paper makes a contribution to the discussion by investigating the various technical solutions that have been developed by the projects of the AI-based secured framework. These solutions aim to protect academic records in terms of both their security and their confidentiality.
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https:/web.stanford.edu/class/aerchive/cs/cs106a.1188/lectures/lecture26.pdf
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