Article Review: Cybersecurity and AI<\/strong> Article Review #2: Understanding AI in Cybercrime<\/strong> Article Review 1 Article Review: Cybersecurity and AIFactors on Incident ReportingJarrell JacksonOctober 2, 2024IntroductionThis review examines the article Impact of Cybersecurity and AI’s Related Factors onIncident Reporting Suspicious Behaviour (Muthuswamy & Esakki, 2024), exploring itsrelevance to the principles of social science and its contribution to understanding therelationship between cybersecurity, AI, and employee behavior.Principles of Social… <\/p>\n
Factors on Incident Reporting<\/strong>
Jarrell Jackson
October 2, 2024
Introduction<\/strong>
This review examines the article Impact of Cybersecurity and AI’s Related Factors on
Incident Reporting Suspicious Behaviour (Muthuswamy & Esakki, 2024), exploring its
relevance to the principles of social science and its contribution to understanding the
relationship between cybersecurity, AI, and employee behavior.
Principles of Social Science<\/strong>
The study demonstrates several social science principles. First, Empiricism is
reflected in the collection of data from employees regarding incident reporting and stress
levels, grounding the research in observable behavior. Relativism is evident in recognizing
that the impact of AI on stress and reporting behavior varies across different organizations.
Skepticism is applied in questioning assumptions about the efficacy of AI and cybersecurity
training without empirical evidence. Additionally, the study adheres to Ethical Neutrality,
objectively evaluating the benefits and risks of AI without promoting any specific ethical
stance.
Research Questions and Hypotheses<\/strong>
The study aimed to understand how incident reporting of suspicious behavior and
cybersecurity training influences stress and readiness for AI adoption. Eight hypotheses
were formulated, testing relationships between variables like Cyber Security Incident
Management (CSIM), Cyber Security Awareness (CSA), and perceived AI threats (PTA). The
key hypotheses explored whether incident reporting reduces employee stress and whether
cybersecurity training enhances reporting behavior.
Research Methods and Data<\/strong>
The study employed a cross-sectional survey method, collecting self-reported data
from employees in various organizations. This method allows for snapshot insights into
attitudes and behaviors. Data was analyzed using statistical methods to examine
correlations between stress levels, incident reporting, and AI adoption intentions.
Data Analysis<\/strong>
The article relies on quantitative analysis, using hypothesis testing to evaluate
relationships between variables. This approach aligns with the principle of Objectivity, as
the authors use measurable data to support their conclusions, avoiding subjective bias.
Concepts from Class<\/strong>
The study’s focus on AI integration connects to Determinism, as it examines how
certain factors like CSA and CSIM shape employee behavior and stress. Furthermore, the
research emphasizes Parsimony, aiming to explain complex behaviors with
straightforward relationships between cybersecurity variables.
Marginalized Groups and Societal Contributions
The article touches on concerns for marginalized groups, particularly regarding
employment displacement from AI. It suggests organizations should address these
concerns with ethical guidelines and training. The study contributes to society by offering
strategies to reduce employee stress and enhance cybersecurity, promoting safer AI
integration.
Conclusion<\/strong>
This research underscores the importance of cybersecurity awareness and
structured incident reporting to mitigate stress and enhance AI readiness. It reflects core
social science principles like empiricism and objectivity, providing both theoretical insights
and practical applications.
References<\/strong>
Muthuswamy, V. V., & Esakki, S. (2024, January). International Journal of Cyber
CriminologyVol 18 Issue 1 January \u2013June 202483\u00a9 2024 International Journal of
Cyber Criminology (Diamond Open Access Journal). Under a Creative Commons
Attribution-NonCommercial-ShareAlike4.0 International (CC BY-NC-SA 4.0)
LicenseImpact of Cybersecurity and AI\u2019s Related Factors on Incident Reporting
Suspicious Behaviour and Employees Stress: Moderating Role of Cybersecurity
Training. cybercrimejournal.
https:\/\/cybercrimejournal.com\/menuscript\/index.php\/cybercrimejournal\/ar
ticle\/view\/330\/99.<\/p>\n\n\n\nArticle Review 2<\/h2>\n\n\n\n
Name: Ja\u2019rrell Jackson
Date: November 17, 2024
Introduction<\/strong>
The article I choose for the second review is \u201cUnderstanding the Use of Artificial
Intelligence in Cybercrime\u201d by Choi, Dearden, and Parti (2024). This article talks about
how people are using AI to commit crimes, like creating fake videos and tricking others
online. Let\u2019s see how this connects to social science, research methods, and how it might
affect society.
Principles of the Social Sciences<\/strong>
This article connects to social science because it looks at how people and technology
interact. For example, it talks about how AI tools are being used for both good and bad,
which shows determinism things happen for a reason. It also makes you think critically
about technology, which ties into skepticism. The authors look at why certain crimes
happen and how technology plays a role in them.
Research Questions and Methods<\/strong>
The researchers asked things like, ‘How is AI being used for cybercrimes?’ and ‘What can
we do to stop it?’ To figure this out, they used different methods. For example, one study
used real-world case studies from healthcare to see how hackers target certain systems.
Another study used interviews and data to look at AI tools like large language models
(LLMs) and how they could be misused.
Data and Analysis<\/strong>
They collected information from a mix of case studies, interviews, and technical tools.
They looked at patterns in cybercrime and used frameworks like the VIVA framework to
figure out why some targets are more appealing to criminals. They also tested ideas like
the Cyber-RAT model to see how people\u2019s online habits might make them more
vulnerable.
Connections to Course Concepts<\/strong>
The article I choose connects well with what we are talking about in class, especially
when it comes to ethical neutrality. The researchers didn\u2019t try to say AI is all good or all
bad they just focused on how it\u2019s being used. It also connects to the idea of using
different fields, like technology, psychology, and sociology, to solve problems.
Marginalized Groups<\/strong>
While I was reading the article I was thinking about which marginalized groups might be
affected by this. I came up with the conclusion that, smaller companies or communities
that don\u2019t have the money for strong cybersecurity could be easier targets. This really
shows how important it is to make sure everyone has access to necessary resources to be
able to stay safe online.
Societal Contributions<\/strong>
One of the big takeaways from this is that this research can help people understand the
risks of AI and how to they can be able to protect against them. This is just about
stopping crime but it\u2019s also about teaching people and creating the very much needed
rules to keep people safe. The article gives ideas for making policies and educating
people to be more aware of the dangers.
Conclusion<\/strong>
So, in the end, this article shows how AI is being used in ways we might not have
expected, both good and bad. It also gives some solid ideas on how to handle these
issues. For me, it was a good reminder that we need to always keep learning and
changing how we do things to stay ahead of these new challenges that come forth with
the rise of technology.
References<\/strong>
Choi, S., Dearden, T., & Parti, K. (2024). Understanding the Use of Artificial Intelligence
in Cybercrime. \u201cInternational Journal of Cybersecurity Intelligence & Cybercrime, 7\u201d
(2), 1-3. https:\/\/vc.bridgew.edu\/ijcic\/vol7\/iss2\/1<\/p>\n","protected":false},"excerpt":{"rendered":"