TIM-8315 v2

Kovaci, P.D. (2023). Threat actors seeking to exploit AI capabilities. Types and their goals. Strategic Impact, 89(4), 53–63. https://doi.org/10.53477/1842-9904-23-21
This article points to cybercriminals who are interested in making profit, terrorist groups cause ideological violence and nation-states target geopolitical influence. Moreover, there is an increasing interest in developing AI-driven cyber warfare capabilities for either attacking enemies or enhancing the defense measures against such attacks. This trend will most likely aggravate the global cyber arms race as countries compete to surpass each other in the development and deployment of AI-driven cyber capabilities.

Guembe, B., Azeta, A., Misra, S., Osamor, V. C., Fernandez-Sanz, L., & Pospelova, V. (2022). The emerging threat of AI-driven cyber attacks: A review. Applied Artificial Intelligence, 36(1), 1–34. https://doi.org/10.1080/08839514.2022.2037254
This paper aims to investigate the emerging threat of AI-powered cyberattacks and provide insights into malicious use of AI in cyberattacks. The study was performed through a three-step process by selecting only articles based on quality, exclusion, and inclusion criteria that focus on AI-driven cyberattacks.

Mohamed, N. (2023). Current trends in AI and ML for cybersecurity: A state-of-the-art survey. Cogent Engineering, 10(2), 1–30. https://doi.org/10.1080/23311916.2023.2272358
This paper raises awareness about the ethical and legal considerations associated with leveraging AI and ML in the cybersecurity domain.

Radanliev, P. (2024). Artificial intelligence and quantum cryptography. Journal of Analytical Science & Technology, 14, 1–17. https://doi.org/10.1186/s40543-024-00416-6
This summary highlights the key themes in the intersection of AI and quantum cryptography, including the potential benefits of AI-driven cryptography, the challenges that need to be addressed, and the prospects of this interdisciplinary research area.

Dawson, M. (2021). Cybersecurity impacts for artificial intelligence use within industry 4.0. Buletin Stiintific, 26(1), 24–31. https://doi.org/10.2478/bsaft-2021-0003
This research paper aims to serve as a guide for information technology managers and senior management to foresee the cybersecurity risks that will result from the incorporation of these new technological advances into the organization.

Trope, R., & Jones, C. (2023). Look before you leap: Reflections on ill-informed introductions of AI and other digital technologies. Business Lawyer, 79(1), 167–179.
This article examines judicial and administrative agency enforcements and government initiatives in cybersecurity and artificial intelligence (AI) from May 2022 to May 2023. Topics include sanctions decision in Mata v. Avianca, Inc. where lawyers failed to investigate the generative artificial intelligence (AI) application ChatGPT4 before adopting it for legal research, settlements in cybersecurity and privacy cases, and government initiatives in the U.S., the European Union, and China.

Kaplan, A. D., Kessler, T. T., Brill, J. C., & Hancock, P. A. (2023). Trust in artificial intelligence: Meta-analytic findings. Human Factors, 65(2), 337–359. https://doi.org/10.1177/00187208211013988
The results of this meta-analysis determined several factors that influence trust, including some that have no bearing on AI performance. Additionally, areas were highlighted where there is currently no empirical research. Application: Findings from this analysis will allow designers to build systems that elicit higher or lower levels of trust, as they require.

Atluri, V., Dahlstrom, P., Gaffey, B., de la Torre, V. G., Kaka, N., Lajous, T., Singla, A., Sukharevsky, A., Travasoni, A., & Vieira, B. (2024). Beyond the hype: Capturing the potential of AI and gen AI in TMT. McKinsey Insights.
The emergence of generative AI (gen AI) presents both a challenge and a significant opportunity for leaders looking to steer their organizations into the future.

Sarker, I. H. (2023). Machine learning for intelligent data analysis and automation in cybersecurity: Current and future prospects. Annals of Data Science, 10(6), 1473–1498. https://doi.org/10.1007/s40745-022-00444-2
This article provides an extensive view of machine learning algorithms, emphasizing how they can be employed for intelligent data analysis and automation in cybersecurity through their potential to extract valuable insights from cyber data. It also explores several potential real-world use cases where data-driven intelligence, automation, and decision-making enable next-generation cyber protection to be more proactive than traditional approaches. The future prospects of machine learning in cybersecurity are eventually emphasized based on the study, along with relevant research directions.

Creating a More Resilient Future with Intelligent Cybersecurity Automation. (2022). Government Technology, 35(8), 44–45.
This article discusses that many government leaders are turning to intelligent security orchestration, automation, and response technologies to manage threats, quickly respond to security incidents and automate security operations to improve resilience, propel innovation, and address staffing shortages. It mentions the challenges regarding growing demands on cybersecurity teams and how intelligent automation can close the gaps.

Nobles, C., & Mcandrew, I. (2023). The intersectionality of offensive cybersecurity and human factors: A position paper. Bulletin Scientific, 28(2), 215–233. https://doi.org/10.2478/bsaft-2023-0022
This position paper calls for an augmentation of scholarly research at the nexus of offensive cybersecurity and human factors, formulating defense mechanisms centered around the human element and promulgating inclusive cybersecurity policies that harmonize human and technological considerations.

Kebande, V. R., & Awad, A. I. (2024). Industrial internet of things ecosystems security and digital forensics: Achievements, open challenges, and future directions. ACM Computing Surveys, 56(5), 1–37. https://doi.org/10.1145/3635030
The dimensions of this study are twofold: 1) the presentation of an overview of the cutting-edge security of IIoT ecosystems; and 2) the survey of the literature on digital forensics. The key achievements, open challenges, and future directions are identified in each case. The challenges and directions for future studies will provide important guidance for cybersecurity researchers and practitioners.

Rana, P., & Patil, B. P. (2023). Cyber security threats in IoT: A review. Journal of High Speed Networks, 29(2), 105–120. https://doi.org/10.3233/JHS-222042
This article discusses the use of IoT forensics in cybercrime investigations in a variety of areas, including cybercrime research, artificial intelligence, system learning, cloud computing, fog computing, and blockchain technology all play a role in this discussion. Finally, some open research on challenging situations in IoT is detailed to enhance cybercrime investigations, providing a cutting-edge course for future research.