The Experts below are selected from a list of 9 Experts worldwide ranked by ideXlab platform

Panagiotis T. Artikis - One of the best experts on this subject based on the ideXlab platform.

  • Discrete stochastic models and global Information Risk Treatment operations in strategic processes
    Journal of Discrete Mathematical Sciences and Cryptography, 2017
    Co-Authors: Panagiotis T. Artikis
    Abstract:

    AbstractFormulating, investigating, and applying in various practical disciplines of discrete stochastic models incorporating discrete random sums are considered as significant research activities. The present paper makes use of a discrete random sum and a discrete random variable to formulate a stochastic model and investigate the corresponding probability distribution. In addition, the present paper establishes applications of the formulated discrete stochastic model in the practical disciplines of strategic thinking and strategic management.

  • Discrete renewal and selfdecomposable distributions in modelling Information Risk management operations
    Journal of Statistics and Management Systems, 2006
    Co-Authors: Panagiotis T. Artikis, Constantinos T. Artikis, Chrysostomos Fountas, Peter Hatzopoulos
    Abstract:

    A thorough examination of the contribution of stochastic modelling to the evolution of Information Risk management as an organizational discipline reveals a continuing significance attached to the applications of discrete probability distributions in measurement, evaluation and Treatment operations for Risks threatening Information assets, Information and Information processes of organizations. The main purpose of the present paper is the extension of the practical applicability of discrete renewal and selfdecomposable distributions in developing stochastic models for Risk frequency reduction operations arising in the area of Information Risk Treatment practices.

Peter Hatzopoulos - One of the best experts on this subject based on the ideXlab platform.

  • Discrete renewal and selfdecomposable distributions in modelling Information Risk management operations
    Journal of Statistics and Management Systems, 2006
    Co-Authors: Panagiotis T. Artikis, Constantinos T. Artikis, Chrysostomos Fountas, Peter Hatzopoulos
    Abstract:

    A thorough examination of the contribution of stochastic modelling to the evolution of Information Risk management as an organizational discipline reveals a continuing significance attached to the applications of discrete probability distributions in measurement, evaluation and Treatment operations for Risks threatening Information assets, Information and Information processes of organizations. The main purpose of the present paper is the extension of the practical applicability of discrete renewal and selfdecomposable distributions in developing stochastic models for Risk frequency reduction operations arising in the area of Information Risk Treatment practices.

Philip Woodall - One of the best experts on this subject based on the ideXlab platform.

  • TIRM Process Stage C: Information Risk Treatment
    Total Information Risk Management, 2014
    Co-Authors: Alexander Borek, Ajith Kumar Parlikad, Jela Webb, Philip Woodall
    Abstract:

    This chapter is a step-by-step guide for implementing the Information Risk Treatment stage of the TIRM process. Information Risk Treatment first identifies and ranks the root causes of the Information Risks, for which potential Information Risk Treatments are identified. To evaluate and select the right Information Risk Treatment options, the costs, benefits, and expected implementation Risks have to estimated. The benefits are, therefore, calculated with the help of the Risk figures from stage B. Communicating effectively to the most important stakeholders increases their support during implementation. Information Risk Treatment plans are developed then implemented, and the effectiveness of the Information Risk Treatments are verified at the end of stage C. Finally, lessons learned are summarized to improve the next cycle of the TIRM process.

Alexander Borek - One of the best experts on this subject based on the ideXlab platform.

  • TIRM Process Stage C: Information Risk Treatment
    Total Information Risk Management, 2014
    Co-Authors: Alexander Borek, Ajith Kumar Parlikad, Jela Webb, Philip Woodall
    Abstract:

    This chapter is a step-by-step guide for implementing the Information Risk Treatment stage of the TIRM process. Information Risk Treatment first identifies and ranks the root causes of the Information Risks, for which potential Information Risk Treatments are identified. To evaluate and select the right Information Risk Treatment options, the costs, benefits, and expected implementation Risks have to estimated. The benefits are, therefore, calculated with the help of the Risk figures from stage B. Communicating effectively to the most important stakeholders increases their support during implementation. Information Risk Treatment plans are developed then implemented, and the effectiveness of the Information Risk Treatments are verified at the end of stage C. Finally, lessons learned are summarized to improve the next cycle of the TIRM process.

Constantinos T. Artikis - One of the best experts on this subject based on the ideXlab platform.

  • Discrete renewal and selfdecomposable distributions in modelling Information Risk management operations
    Journal of Statistics and Management Systems, 2006
    Co-Authors: Panagiotis T. Artikis, Constantinos T. Artikis, Chrysostomos Fountas, Peter Hatzopoulos
    Abstract:

    A thorough examination of the contribution of stochastic modelling to the evolution of Information Risk management as an organizational discipline reveals a continuing significance attached to the applications of discrete probability distributions in measurement, evaluation and Treatment operations for Risks threatening Information assets, Information and Information processes of organizations. The main purpose of the present paper is the extension of the practical applicability of discrete renewal and selfdecomposable distributions in developing stochastic models for Risk frequency reduction operations arising in the area of Information Risk Treatment practices.