DETECTION, ANALYSIS AND PROTECTION OF CONFIDENTIAL DATA USING AMAZON MACIE MACHINE LEARNING TECHNOLOGY
DOI:
https://doi.org/10.28925/2663-4023.2024.23.132144Keywords:
machine learning, Amazon Macie, cyber security, automated analysis, confidential data, AWSAbstract
Over the past decades, the field of data storage and processing has undergone significant changes and expansion, especially with the advent of cloud technologies and computing. Cloud services enable organizations to store and access large amounts of data through distributed systems. However, along with these new opportunities come new challenges, particularly in the area of protecting confidential data. Protecting sensitive data is an extremely important task for today's organizations, especially in the face of a growing number of digital threats and security breaches. In order to ensure reliable protection of valuable and sensitive information, developers and researchers are actively working on the development of new technologies and tools. One of the powerful tools used to identify, analyze and protect confidential data is the machine learning technology of the Amazon Macie service. Amazon Macie is an AWS cloud computing service that uses artificial intelligence and machine learning algorithms to automate data analysis and identify potential data security threats.
The main purpose of this work is the detection, analysis and protection of confidential data using Amazon Macie machine learning technology. Amazon Macie is an innovative service developed by Amazon Web Services (AWS) that uses advanced machine learning algorithms for automated discovery and analysis of sensitive data. As part of the work, an analysis of the main machine learning algorithms, principles of data storage systems and methods of protecting confidential information was carried out. The working principles and capabilities of Amazon Macie, which uses advanced machine learning algorithms for automated data analysis and detection of potential threats to data security, were investigated.
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Copyright (c) 2024 Андрій Партика, Ольга Михайлова , Станіслав Шпак
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