TIM-8315 v2

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 provides a comprehensive survey of the state-of-the-art use of Artificial Intelligence (AI) and Machine Learning (ML) in cybersecurity. The paper illuminates key applications of AI and ML in cybersecurity, while also addressing existing challenges and posing unresolved questions for future research. The paper also emphasizes the ethical and legal implications associated with their implementation.

Bergadano, F., & Giacinto, G. (2023). Special issue “AI for cybersecurity: Robust models for authentication, threat and anomaly detection.” Algorithms, 16(7), 327. https://doi.org/10.3390/a16070327
Cybersecurity models include provisions for legitimate user and agent authentication and algorithms for detecting external threats, such as intruders and malicious software. The authors propose a new approach for multi-vector attack detection in the IoT domain, using machine learning algorithms and providing an experimental evaluation.

Burton, S. L. (2022). Artificial intelligence (AI): The new look of customer service in a cybersecurity world. Buletin Stiintific, 27(2), 79–92. https://doi.org/10.2478/bsaft-2022-0010
This article should benefit respondents absorbed in the success of updating and implementing quality customer service (QCS) actions and workflows, practitioners who guide direct customer services initiatives, enterprise governance strategists, QCS and machine learning trainers, and learners who want to know more about QCS swathed in cybersecurity.

Sarker, I. H. (2023). Multi‐aspects AI‐based modeling and adversarial learning for cybersecurity intelligence and robustness: A comprehensive overview. Security & Privacy, 6(5), 1–26. https://doi.org/10.1002/spy2.295
The article’s goal is to explore AI‐based modeling and pertinent methodologies and to focus on the resulting model's applicability for securing our digital systems and society.

Zwass, V. (2023). Editorial Introduction. Journal of Management Information Systems, 40(4), 1035–1038. https://doi.org/10.1080/07421222.2023.2267312
The Journal of Management Information Systems has published a special issue that focuses on two main topics: the interaction between humans and artificial intelligence (AI), and cybersecurity. Other topics covered in the issue include the role of AI in employee performance evaluation, the collaboration between AI and humans in demand forecasting, the effectiveness of fear appeals in shaping employee cybersecurity posture, the relationship between IT innovativeness and the risk of data breaches, the impact of avatars on community identification in virtual social worlds, the performance of foreign IT complementors in mobile app startups, the impact of open-source software communities on cryptocurrency prices, the influence of social relationships on online reviews, the digitalization of loyalty in online reward programs, and the optimization of brand offerings in online marketplaces.

Accenture. (n.d.). What is artificial intelligence? Accenture. https://www.accenture.com/us-en/insights/artificial-intelligence-summary-index
The largest consulting firm defines and discusses artificial intelligence. Types of artificial intelligence are presented (weak or strong) and future trends. The term “Generative AI” is presented and discussed, together with components, benefits and future.

National Institute of Standards and Technology. (2021, July 28). AI RMF Development. https://www.nist.gov/itl/ai-risk-management-framework/ai-rmf-development
As of January 26, 2023, the release of this framework was developed through a consensus-driven, open, transparent, and collaborative process that included a Request for Information, several draft versions for public comments, multiple workshops, and other opportunities to provide input. It is intended to build on, align with, and support AI risk management efforts by others.

Schuett, J. (2023). Three lines of defense against risks from AI. AI & SOCIETY: Journal of Knowledge, Culture and Communication, 1–15. https://doi.org/10.1007/s00146-023-01811-0
This author suggests ways in which AI companies could implement the model. The discussion continues with how the model could help reduce risks from AI: It could identify and close gaps in coverage, increase the effectiveness of risk management practices and enable the board of directors to oversee management more effectively. This article is intended to inform decision-makers at leading AI companies, regulators, and standard-setting bodies.

Preuveneers, D., & Joosen, W. (2024). An ontology-based cybersecurity framework for AI-enabled systems and applications. Future Internet, 16(3), 69. https://doi.org/10.3390/fi16030069
The authors propose an ontology-based cybersecurity framework that extends well-known cybersecurity ontologies to specifically model and manage threats imposed on applications, systems, and services that rely on artificial intelligence (AI). More specifically, our efforts focus on documenting prevalent machine learning (ML) threats and countermeasures, including the mechanisms by which emerging attacks circumvent existing defenses as well as the arms race between them.

Jada, I., & Mayayise, T. O. (2023). The impact of artificial intelligence on organizational cyber security: An outcome of a systematic literature review. Data and Information Management, 8(2), 100063. https://doi.org/10.1016/j.dim.2023.100063
The results of this research revealed that AI can impact cybersecurity throughout its entire life cycle, yielding benefits like automation, threat intelligence, and improved cyber defense. Nevertheless, it also brings challenges like adversarial attacks and the need for high-quality data, which could lead to the inefficiency of AI. These results affirm the positive influence of AI on cybersecurity, enhancing effectiveness and resilience. These findings provide a solid foundation for further research in organizational cybersecurity. These results can help organizations make informed decisions on AI implementations by offering an impartial view of its impacts.

Funk, P. (2022). Artificial intelligence and cybersecurity implications for business management. International Scientific Publications, Bulgaria, 16(1), 252-261. https://doi.org/10.6084/m9.figshare.21550926.v1
This article discusses the implications of AI and cybersecurity for business management on several case scenarios.

Guembe, B., Azeta, A., Misra, S., Osamor, V. C., Fernandez-Sanz, L., & Pospelova, V. (2022). The emerging threat of AI-driven cyberattacks: A review. Applied Artificial Intelligence, 36(1), e2037254. https://doi.org/10.1080/08839514.2022.2037254
The findings in this study show that existing cyber defense infrastructures will become inadequate to address the increasing speed, and complex decision logic of AI-driven attacks. Hence, organizations need to invest in AI cybersecurity infrastructures to combat these emerging threats.

Oche, J. (n.d.). The risk of artificial intelligence in cyber security and the role of humans. Texila International Journal of Academic Research.
This paper presents and analyzes reported failures of artificially intelligent systems and extrapolates our analysis to future AI (Artificial Intelligence). The author opines that both the frequency and the seriousness of future AI failures will steadily increase. AI safety can be improved based on ideas developed by cybersecurity experts.

Gaske, M. R. (2023). Regulation priorities for artificial intelligence foundation models. Vanderbilt Journal of Entertainment & Technology Law, 26(1),1–71.
This article responds to the call in technology law literature for high-level frameworks to guide regulation of the development and use of Artificial Intelligence (AI) technologies. Accordingly, it adapts a generalized form of the fintech Innovation Trilemma framework to argue that a regulatory scheme can prioritize only two of three aims when considering AI oversight: (1) promoting innovation, (2) mitigating systemic risk, and (3) providing clear regulatory requirements. Specifically, this article expressly connects legal scholarship to research in other fields focusing on foundation model AI systems and explores this kind of system's implications for regulation priorities from the geopolitical and commercial competitive contexts.

Moschetta, G., Bouckaert, J., & World Economic Forum. (2024, February 29). AI and cybersecurity: Navigating the risks and opportunities. World Economic Forum. https://www.weforum.org/agenda/2024/02/ai-cybersecurity-how-to-navigate-the-risks-and-opportunities/
In the race for AI dominance, cybersecurity considerations take center stage. Although AI will augment the effectiveness of cyberattacks, their impact can be offset by using AI technologies to enhance cyber defense capabilities. Harnessing the advantages brought by AI will require global public-private cooperation to ensure its applications can be translated in an equitable and secure manner across society.

Radanliev, P., De Roure, D., Maple, C., & Ani, U. (2022). Super-forecasting the ‘technological singularity’ risks from artificial intelligence. Evolving Systems, 13(5), 747–757. https://doi.org/10.1007/s12530-022-09431-7
This article investigates cybersecurity (and risk) in the context of ‘technological singularity’ from artificial intelligence. The investigation constructs multiple risk forecasts that are synthesized in a new framework for counteracting risks from artificial intelligence (AI) itself. The research in this article is not just concerned with securing a system, but also analyzing how the system responds when (internal and external) failure(s) and compromise(s) occur. According to the author, “This is an important methodological principle because not all systems can be secured, and totally securing a system is not feasible.”