Article Review #1
Understanding the Use of Artificial Intelligence in Cybercrime
Clifford Mammah
10/5/2025
Introduction / Connection to Social Sciences
The article Understanding the Use of Artificial Intelligence in Cybercrime by Parti, Dearden, and Choi (2023) takes a look at how criminals are starting to use artificial intelligence to commit crimes. The authors focus on two main examples, deepfakes in the metaverse and social engineering attacks using large language models. This connects to the social sciences because it studies people’s behavior, technology, and society. Criminology and psychology theories are used to explain why offenders use AI, why certain people become victims, and how we can respond. For example, the Routine Activities Theory explains crime through motivated offenders, vulnerable targets, and lack of protection; the Big Five Personality Traits model shows why some people are easier to trick than others.
Research Question, Hypotheses, Independent and Dependent Variables
The studies asked two questions, how are deepfakes being used to harm people in the metaverse, and how can LLMs like GPT-4 be used to study weaknesses in phishing attacks. The first study predicted that younger people are more at risk of deepfake crimes, often for money or sexual purposes. The second study predicted that people with certain personality traits, such as being careless, anxious, or overly trusting, are more likely to fall for phishing. In these studies, the independent variable was the type of AI being used, and the dependent variable was the level of risk or victimization.
Research Methods
The researchers used different methods to study these questions. The deepfake study interviewed eight experts in South Korea from areas like law enforcement, policy, and industry. Their answers were analyzed for common themes, such as why offenders act and how crimes could be prevented. The social engineering study took a technical approach by using GPT-4 to simulate phishing attempts. The results were compared to different personality traits from the Big Five model. These methods, expert interviews, and AI simulations, together show how both people and technology influence cybercrime.The data also looked different in each study. The deepfake research used qualitative data, meaning the focus was on words, experiences, and opinions from experts. The social engineering research used more quantitative data, looking at numbers and patterns from GPT-4 simulations. Both sets of data helped reveal risks and offered ideas for prevention. This article also connects with concepts I’ve studied in class. Routine Activities Theory clearly applies to deepfakes because offenders need a target and a lack of protection to commit crimes. The Big Five model connects to psychology, showing that traits like anxiety or carelessness make people more vulnerable online. The research also ties into class discussions about how technology changes social behavior and how prevention strategies must keep up.
Relation to Marginalized Groups & Contributions to Society
The findings also point out challenges for certain groups. Young people in the metaverse are at higher risk because they are frequent users and may not have enough protection. Women are often targeted with deepfakes in harmful ways, making gender an important part of this discussion. People with certain personality traits may also be more vulnerable, meaning not everyone faces the same risks online. These concerns show why it is important to focus on vulnerable and marginalized groups when designing protections. Overall, the article makes an important contribution by showing how AI is reshaping crime and what can be done to reduce harm. It gives policymakers, law enforcement, and cybersecurity experts helpful insights into how criminals use AI and how to better protect people. By connecting technology to human behavior, the article shows that fighting cybercrime is not just about better software but also about understanding people.
Conclusion
In conclusion, Parti, Dearden, and Choi (2023) highlight the growing problem of AI-enabled cybercrime and explain why we as a society need to pay attention. Their research shows how deepfakes and phishing attacks work, who is most at risk, and how we might prevent them. The studies also show us that some groups, like young people and women, face greater danger online. By studying both the human and technological sides of crime, this article gives us knowledge that can help build better protections for the future.
SOURCES
Parti, K., Dearden, T., & Choi, S. (n.d.). Understanding the use of artificial intelligence in cybercrime. Virtual Commons – Bridgewater State University. https://vc.bridgew.edu/ijcic/vol6/iss2/1
Article Review 2The Role of Awareness and Trust in Improving Information
Security Compliance
Student Name: Clifford Mammah
School of Cybersecurity, Old Dominion University
CYSE 201S: Cybersecurity and the Social Sciences
Instructor Name: Diwakar Yalpi
Date: 11/14/2025
BLUF: Employees are more likely to follow information security rules when they are aware of cybersecurity threats, perceive their workplace culture as supportive, trust management, and feel involved in security processes, ultimately reducing the risk of cybercrime.
The article Controlling Cyber Crime through Information Security Compliance Behavior: Role of Cybersecurity Awareness, Organizational Culture and Trust in Management by M. M. S. Ghaleb (2025) looks at how organizational factors and employee perceptions influence compliance with information security policies. The study focuses on the fact that cybersecurity awareness, supportive organizational culture, employee involvement, and trust in management are critical drivers of compliance behavior.
Relation / Connection to Social Science Principles
The study directly relates to social science principles. It uses determinism by looking at how factors such as awareness and culture determine compliance behavior. The research is grounded in empiricism, collecting measurable data from surveys to understand human behavior in organizations. By testing hypotheses with statistical tools, the study follows objectivity and skepticism, ensuring findings are evidence-based. Parsimony is applied through a focused model examining key variables rather than numerous extraneous factors. Finally, the study highlights relativism, recognizing that organizational culture and employee trust vary across workplaces, influencing behavior differently depending on context.
Research Question / Hypothesis / Independent Variable / Dependent Variable
How does cybersecurity awareness influence employees’ compliance behavior?
How does organizational culture affect compliance behavior?
Does trust in management mediate the relationship between awareness/culture and compliance?
Does employee involvement strengthen the relationship between culture/awareness and compliance?
Hypotheses:
H1: Cybersecurity awareness positively affects compliance behavior.
H2: Organizational culture positively affects compliance behavior.
H3: Employee involvement moderates the effect of awareness/culture on compliance.
H4: Trust in management mediates the effect of awareness/culture on compliance.
Independent Variables: Cybersecurity awareness, organizational culture, employee involvement, and trust in management.
Dependent Variable: Information security compliance behavior of employees.
Types of Research Methods Used
The study used quantitative research methods using survey data from employees in organizations. Respondents completed structured questionnaires testing awareness, perceptions of organizational culture, trust in management, involvement, and compliance behavior. This method allows researchers to measure the relationships between variables and testing of the proposed hypotheses.
Types of Data Analysis Used
The collected survey data were analyzed using regression-based techniques and mediation/moderation analysis. The authors examined the effects of awareness and culture on compliance, mediating effects of trust in management, and moderating effects of employee involvement. The analysis revealed that higher awareness and supportive culture increase compliance, and trust in management significantly mediates these relationships.
Connections to Course Concepts
This study connects with class concepts, including human factors in cybersecurity, insider risk, and organizational behavior. The research reinforces the idea that cybersecurity is not purely technical but also social, showing how culture, trust, and involvement shape employees’ adherence to policies. It highlights the importance of socio-technical systems and the non-technical components of risk management.
Connections to the Concerns or Contributions of Marginalized Groups
Although not the main focus, the study’s variables have implications for marginalized employees. Lower-status or minority employees may experience less trust in management or feel excluded from organizational culture and security initiatives. Making sure that you have inclusive awareness programs and equitable involvement can improve compliance across all groups, highlighting the importance of diversity and equity in cybersecurity strategy.
Overall Societal Contributions of the Study
The study demonstrates that fostering cybersecurity awareness, supportive culture, trust in management, and employee involvement enhances policy compliance, reducing organizational vulnerability to cybercrime. Its societal contributions include improving organizational security posture, reducing cybercrime risk, and informing best practices for human-centric cybersecurity approaches. By emphasizing behavioral and social factors, the research expands our understanding of cybersecurity as a discipline that intersects technology, organizational behavior, and social science.
Source
Ghaleb, M. M. S., & Pardaev, J. (2025). Controlling cyber crime through information security compliance behavior: Role of cybersecurity awareness, organizational culture, and trust in management. International Journal of Cyber Criminology, 19(1), 1–26 .https://cybercrimejournal.com/menuscript/index.php/cybercrimejournal/article/view/437/123