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Book chapter
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Reference no. BEP8831
Chapter from: "Artificial Intelligence for Security"
Published by: Business Expert Press
Originally published in: 2020

Abstract

This chapter is excerpted from 'Artificial Intelligence for Security'. Shootings are becoming prevalent in the United States (Lankford, Adkins, and Madfis 2019). Oftentimes, shooters post their intentions on the Internet before carrying out the act of shooting. Security organizations plant bots to help trace postings that could lead to harm. The bots - also known as botnet - help organizations to avoid harmful security situations (Anzilotti et al 2019; Berinato 2018). Other approaches can be used to identify security issues early enough to avoid harmful situations and minimize impact. Security management plays a major role in the case of uncertainty. Security management must ask the following questions: How can this situation be forecasted and managed? Can the occurrences be captured historically? Can such patterns of occurrences be identified? Can the data relating to the occurrences be captured? Can the data be used to predict future occurrences? Can the relevance of the data be an important factor? Does the security piece become important? What happens if the data are manipulated? How is the data manipulated? How can the data be protected and saved safely? Security plays an important role with data and is considered a driving factor in security management. Various types of securities apply to certain industries, business functions, roles, and responsibilities (Clark 2019). This book intends to illustrate the top security business cases and use cases that apply to respective industries by defining, analyzing, monitoring, controlling, and mitigating the associated securities. It is important to use the data and put corrective actions in place to observe lurking security. The empirical study shows that large amounts of data take a long time to process and cannot be analyzed by a human. Data science, data analytics, and machine learning (ML) algorithms can be used (De Veaux and De Veaux 2019; Hao and Ho 2019). Artificial Intelligence (AI) enables machines to learn from previous human experiences and enables continuous learning from new sets of input data. The development of mathematical algorithms has led to the creation of ML and subsequently to the AI revolution. AI is used to determine how security should be approached. AI produces effective and dramatic results in business and can aid organizations in understanding and improving security management skills. Security has become important everywhere due to the large volume of data, different velocities, and variety of data. These rationales are growing and are used more frequently with any amount of negative impact. The range and breadth of security creates havoc everywhere in the world on a variety of projects. Security management is important in an organization; without it, an organization may struggle with defining its objectives. Data security strategies are important in many ways; however, the most important strategical implementation is to avoid financial loss. This book focuses on problem statements with appropriate use cases and proposes AI solutions using data science and ML approaches. This book aims to give concrete answers to the following crucial questions: Where are these securities and what can be done to lower the impacts? Is AI part of the answer to security mitigation?

About

Abstract

This chapter is excerpted from 'Artificial Intelligence for Security'. Shootings are becoming prevalent in the United States (Lankford, Adkins, and Madfis 2019). Oftentimes, shooters post their intentions on the Internet before carrying out the act of shooting. Security organizations plant bots to help trace postings that could lead to harm. The bots - also known as botnet - help organizations to avoid harmful security situations (Anzilotti et al 2019; Berinato 2018). Other approaches can be used to identify security issues early enough to avoid harmful situations and minimize impact. Security management plays a major role in the case of uncertainty. Security management must ask the following questions: How can this situation be forecasted and managed? Can the occurrences be captured historically? Can such patterns of occurrences be identified? Can the data relating to the occurrences be captured? Can the data be used to predict future occurrences? Can the relevance of the data be an important factor? Does the security piece become important? What happens if the data are manipulated? How is the data manipulated? How can the data be protected and saved safely? Security plays an important role with data and is considered a driving factor in security management. Various types of securities apply to certain industries, business functions, roles, and responsibilities (Clark 2019). This book intends to illustrate the top security business cases and use cases that apply to respective industries by defining, analyzing, monitoring, controlling, and mitigating the associated securities. It is important to use the data and put corrective actions in place to observe lurking security. The empirical study shows that large amounts of data take a long time to process and cannot be analyzed by a human. Data science, data analytics, and machine learning (ML) algorithms can be used (De Veaux and De Veaux 2019; Hao and Ho 2019). Artificial Intelligence (AI) enables machines to learn from previous human experiences and enables continuous learning from new sets of input data. The development of mathematical algorithms has led to the creation of ML and subsequently to the AI revolution. AI is used to determine how security should be approached. AI produces effective and dramatic results in business and can aid organizations in understanding and improving security management skills. Security has become important everywhere due to the large volume of data, different velocities, and variety of data. These rationales are growing and are used more frequently with any amount of negative impact. The range and breadth of security creates havoc everywhere in the world on a variety of projects. Security management is important in an organization; without it, an organization may struggle with defining its objectives. Data security strategies are important in many ways; however, the most important strategical implementation is to avoid financial loss. This book focuses on problem statements with appropriate use cases and proposes AI solutions using data science and ML approaches. This book aims to give concrete answers to the following crucial questions: Where are these securities and what can be done to lower the impacts? Is AI part of the answer to security mitigation?

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