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Analysis for the adoption of security standards to improve the management of securities in public organizations

  • Segundo Moisés Toapanta Toapanta
  • , Madeleine Lilibeth Alvarado Ronquillo
  • , Luis Enrique Mafla Gallegos
  • , Alberto Ochoa Zezzatti

    Producción científica: Capítulo del libro/informe/acta de congresoContribución de conferenciarevisión exhaustiva

    Resumen

    Public organizations have the ongoing task of properly managing the security of the information they handle. The objective of this research is to analyze the security standards adopted by public organizations in Ecuador to improve their management of information security. The deductive method was applied for the review and analysis of appropriate standards for public institutions. As a result, information was obtained on the different security policies, standards and guidelines that apply, national and international public organizations. A Diagram of activities for the adoption of standards for public organizations resulted; a prototype standards-based Information Security Management Model; and an Information Security Management Matrix, from which the Risk Mitigation Percentage was calculated. It was concluded that maintaining high levels of security in public organizations requires the adoption of control standards in different areas and the collaboration of the different organizational and hierarchical levels of public organizations.

    Idioma originalInglés
    Título de la publicación alojadaMachine Learning and Artificial Intelligence - Proceedings of MLIS 2020
    EditoresAntonio J. Tallon-Ballesteros, Chi-Hua Chen
    EditorialIOS Press BV
    Páginas310-321
    Número de páginas12
    ISBN (versión digital)9781643681368
    DOI
    EstadoPublicada - 2 dic. 2020
    Evento2020 International Conference on Machine Learning and Intelligent Systems, MLIS 2020 - Virtual, Online, República de Corea
    Duración: 25 oct. 202028 oct. 2020

    Serie de la publicación

    NombreFrontiers in Artificial Intelligence and Applications
    Volumen332
    ISSN (versión impresa)0922-6389
    ISSN (versión digital)1879-8314

    Conferencia

    Conferencia2020 International Conference on Machine Learning and Intelligent Systems, MLIS 2020
    País/TerritorioRepública de Corea
    CiudadVirtual, Online
    Período25/10/2028/10/20

    Nota bibliográfica

    Publisher Copyright:
    © 2020 The authors and IOS Press.

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