New technology-empowered cybersecurity controls in higher education institutions: a systematic review

New technology-empowered cybersecurity controls in higher education institutions: a systematic review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review New technology-empowered cybersecurity controls in higher education institutions: a systematic review Lebohang Bosiu¹, Lethiwe Nzama² This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7343340/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract The advent of fourth industrial revolution (4IR) has brought with it a myriad of advanced technologies, which have simultaneously given rise to new, technologically sophisticated threats for Higher Education Institutions (HEIs). This meant that HEIs had to adopt new cybersecurity strategies incorporating technologies to counter new threats. However, it is not clear to what extent HEIs have adopted and integrated advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML) into their traditional cybersecurity strategies for mitigating evolving threats within the HEI context. The study sought to explore current adoption of emerging technologies to enhance cybersecurity posture in HEIs. The study also sought to determine the effectiveness of the new emerging technologies in mitigating evolving threats in HEIs. A Systematic Literature Review (SLR) was used to qualitatively examine literature on emerging technologies in HEIs. Guided by the PRISMA framework, the selection process focused on relevant literature from selected databases. A total of 287 studies were retrieved and assessed for eligibility, with 23 studies ultimately included to explore the emerging technologies employed by HEIs to mitigate technological threats. From a thematic analysis of data, findings showed that HEIs have adopted and integrated new technologies such as AI, ML, and cloud services. However, the diffusion and adoption of these technologies face challenges related to system integration and resistance or unwillingness to undergo training for new systems. Factors such as lack of integration of systems, resistance to change and the disjointed regulatory environment lead to slow adoption and lead to a proliferation of much more aggressive and evolved threats in HEIs. There was also an urgent need for training and cybersecurity awareness campaigns to build cybersecurity culture around emerging technologies. Thus, establishing a centralised framework for governance incorporating new technologies to existing cybersecurity controls will address existing challenges of technology adoption in HEIs. cybersecurity higher education institutions emerging technology blockchain artificial intelligence machine learning Figures Figure 1 1 Introduction The advent of the fourth industrial revolution (4IR) has brought with it a myriad of advanced technologies, which has for the last decade transformed the workings of many organisations (Byabazaire et al., 2020 ; Masinde & Roux, 2020 ). Emerging technologies such as artificial intelligence (AI), machine learning (ML), cloud computing, blockchain, internet of things (IoT) and many others have enabled innovative business processes (Arumugam et al., 2023 ). AI, smart devices and analytics have managed to connect systems needed by society while at the same time transforming the efficiency and effectiveness of enterprises (Arumugam et al., 2023 ). Unfortunately, the advent of newer technologies also coincides with the sophistication of threats; in 2025, traditional threats such as ransomware and phishing are AI-driven and more aggressive (Bankert, 2025 ). When technology begins to harbour malicious intent it leads to devastating consequences, such as loss of financial data and other risks (Mukwakwa, 2022 ). Thus, the integration of systems adopted by many organisations also means that businesses had to adopt more advanced security and risk management measures as cyberthreats have also evolved to exploit these technologies to threaten their systems (Cheng & Wang, 2022 ; Da Costa et al., 2024 ; Ogundele & Nzama, 2025 ). In the context of higher education institutions (HEIs), the digital integration of systems and the fast adoption of emerging technologies to drive business decisions and processes has also meant necessary measures had to be adopted (Cheng & Wang, 2022 ; Da Costa et al., 2024 ). However, at the present moment, however, it is not clear to what extent HEIs have adopted and integrated advanced technologies such as AI and ML into their traditional cybersecurity strategies for mitigating evolving threats within the HIE context. Knowledge of present adoption and alignment to emerging technology is necessary for planning and preparation against the current, fast evolving threats in the HEI cybersecurity environment. Thus, the current study is a response to impending dangers of emerging technologies on HEIs cybersecurity posture. Using diffusion of innovation theory (DOI), the study seeks to explore current developments with regard to adopting emerging technologies such as AI, IoT, ML, blockchain, and cloud computing in HEIs. The study seeks to understand the extent to which these new technologies are adopted at organisational level with the goal of boosting existing cybersecurity controls to mitigate evolving threats in these institutions. The work contributes to understanding the use of these technologies in enhancing traditional controls, and as such provides insights into the degree to which such technologies have successfully matched the current waves of advanced threats in HEIs. In doing so, the study hopes to inspire the development of an inclusive integrated framework and guidelines for adopting new advanced technologies into the existing cybersecurity strategies in HEIs. 2 Literature Review 2.1 Emerging Technologies in Cybersecurity The evolution of advanced technologies such as AI, ML, Internet of Things (IoT) and cloud computing have coincided with the advancement and sophistication of cyberattacks in recent years (Cheng & Wang, 2022 ). Both the technological advancements and the advent of the COVID-19 pandemic have added to the increased use of technology and the dependence on the Internet in all aspects of human life (Chapman et al., 2018 ; Ganesen et al., 2022 ; Mabatha, 2023 ). Cybercriminals are reported to have started including IoT hacks and adopting AI-driven phishing, social engineering, ransomware and malware to speed up their attacks, prompting international security organisations to sound warning alarms to organisations about the dangers of these attacks on their valuable assets (Kaloudi & Li, 2020 ). AI-enhanced attacks are faster techniques, which often bypass traditional cybersecurity control measures, leaving little response time for organisations that have not adopted equally sophisticated measures (Bankert, 2025 ). Abbadi and Lachkar ( 2025 ) have noted that advanced AI-driven phishing attacks such as deepfake and malware forms such as Emotet have already cost large corporations in Britain, Australia and Hong Kong millions of dollars in damages. In the U.S, Bankert ( 2025 ) has noted the 67% increase in cybersecurity attacks on the financial sector between 2023 and 2024; at the same time there has also been an increase in AI-generated deepfake technology leading to a rise in financial fraud. Likewise, the growth in similar AI-driven attacks has been noted all over the world, with the Asian Pacific region experiencing a 600% increase of deepfakes in 2024 (UNODC, 2024). The scope and increase of the AI-driven attacks are not only limited to corporations; HEIs have also been affected by the scourge (Aboelnor & Sobaih, 2023 ; Masinde & Roux, 2020 ). In fact, HEIs are more vulnerable to cyberattacks compared to other organisations such as banks, which often prioritise risk management through technology despite also equally holding the same value of assets (Mukwakwa, 2022 ; Ogundele & Nzama, 2025 ). HEIs operate within a culture that encourages academic freedom and openness, while minimising optimisation of cyber risk management (Cheng & Wang, 2022 ; Ulven & Wangen, 2021 ). 2.2 Diffusing new technologies in the HEI cybersecurity environment Developments of new technologies in cybersecurity have adopted the radical nature of the development of technology in general (Mustapha et al., 2024 ; Chinese Academy of Cyberspace Studies, 2019 ). While technological developments bring opportunities for competitiveness and growth for many organisations, they also bring in new cybersecurity challenges, which ultimately require a new sense of urgency for protection (Mukwakwa, 2022 ; Mustapha et al., 2024 ). As such, diffusion of new technologies in organisations is often required to be seamless if organisations are to keep up with the frequent changes, especially when these organisations must also out-compete cybercriminals who also adopt the same technologies to spread more aggressive threats (Hasani et al.,, 2023). To understand the extent to which organisations such as HEIs diffuse new technologies across their processes to minimise threats, DOI is often applied. This theory is a theory developed by Everett Rogers to understand how new innovations spread within organisations (Zoellner, & Porter, 2017 ). According to Zoellner, and Porter ( 2017 ), DOI focuses individuals and organizations alike to perceive technology as new in order for it to be adopted or to be rendered necessary to be spread through the organisational system. Diffusion of new technology may go through the process of perception of its attributes where its relative benefits and compatibility to current systems are assessed and adjudged, to being accepted and adopted by IT professionals and managers (Makovhololo et al,, 2017). In HEIs, IT professionals and managers are responsible for the technology diffusion and adoption processes (Ali et al., 2022 ). According to Ali et al. ( 2022 ), these professionals form part of the group of factors affecting adoption success, which also include top management support; organization size and culture; skills and knowledge; and technological readiness. Often enough, most HEIs struggle to adopt new technologies given the trade-offs between the perceived benefits of adopting new technology and diverting limited resources to other non-IT related projects (Feng et al.,, 2025; Hartati et al.,, 2023). Despite benefits of new technologies, such as faster and safer security systems, most HEIs perceive the cost, time and effort of training new staff to new technology, hiring of new skills and potential disruptions as a disadvantage (Kaputa et al.,, 2022; Ogunlela & Tengesh, 2021). HEI management, especially in under-resourced HEIs, seldom support the adoption given these reasons (Ali & Shah 2024 ). According to Shin and Jones ( 2022 ), complex HEIs governance structures, policies and often bureaucratic decision-making processes may work against the new technology proposals. Thus, the complexity of IT governance and the associated regulatory environment make it less likely for institutions to invest in new technologies, particularly when the perceived benefits, sustainability, and technological readiness are not immediately evident (Hartati et al., 2023 ; Ali et al., 2022 ; Kaputa et al., 2022 ). This research responds to the need for empirical examination of the adoption of new technologies in mitigating evolving threats in HEIs by first exploring the current state of adoption of emerging technologies (AI, IoT, ML, blockchain, and cloud computing) in enhancing cybersecurity controls in HEIs. The study also aims to explore the effectiveness of these new technologies in being able to mitigate evolving threats in HEIs. In achieving these objectives, the study provides ways in which HEIs can successful integrate and implement emerging technologies into the traditional cybersecurity strategy for HEIs to achieve a successful cybersecurity against new threats. 3 Materials and Methods 3.1 Structured Literature Review process To conduct this study, a systematic literature review (SLR) method was applied as a research approach. SLR follows a rigorous and systematic process to ensure the comprehensiveness and reliability of the findings. The review was conducted in several stages, including defining the review's scope; searching literature on reputable academic databases; selecting literature using inclusion-exclusion criteria; extracting and synthesising data extracted from selected literature; and reporting findings. In applying SLR, the study adopted the subjectivist-constructivist position in research, which holds that reality is subject to researchers’ interpretation on matters under study (Lau & Kuziemsky, 2017 ). As a research approach, SLR is secondary research by nature (Paoloni et al., 2020 ). As a methodology, SLR is used to “review current knowledge on a topic about research questions to suggest areas for further examination" (Carrera-Rivera et al., 2022 , p.2). The study approached SLR through a secondary qualitative research to identify, collate and interpret themes and subthemes emerging from selected studies on emerging technologies in HEIs across the world. SLR in the form of a qualitative research helped the researcher identify key information assets and common threat events caused by AI-powered and cloud-based cyberattacks in HEIs. At the same time, the method was used to identify AI-powered, cloud-based, deep learning-enabled biometric verification, ML and blockchain-based cybersecurity measures and controls instituted within HEIs. For effective SLR, the researcher followed a Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol (PRISMA) at the start of the review process as a plan of action. Sargeant and O’Connor ( 2020 ) recognised that such protocols as PRISMA help researchers undertaking SLR to ensure that the research is conducted thoroughly throughout the study. That is, issues of reliability and validity are applied to ensure quality results. In this case, the protocol helped the researcher to formulate research questions and set parameters for the search of keyword strings on selected online databases to get relevant materials on emerging technology for cybersecurity in the context of HEIs. The protocol also helped the researcher to set requirements for inclusion and exclusion of materials for review. 3.2 The search strategy The SLR process involved identifying relevant literature through comprehensive searches based on key themes and keywords related to the topic. Literature was then selected using inclusion and exclusion criteria, focusing on relevance to the study, year of publication, and citation index to ensure quality. Two processes were involved in devising a search strategy for literature. Firstly, quality databases were selected to source review literature. Databases were selected based on their bibliometric performance, and included Web of Science, Scopus, EBSCOhost and Google Scholar. Secondly, search strategy and were created through which the Boolean operator method of searching databases was applied. To find and identify literature in the databases, free text search was used. In review studies, a free-text search involves using words that appear in the article's title and abstract (Grewal et al., 2016 ). This method allows researchers to search for specific terms used by authors themselves, enhancing the search for relevant articles. The keywords and possible combinations were in the form of subject heading and free text search. Boolean operators are essential tools for conducting structured and effective literature reviews (Ugwu & Opah, 2023 ). In this study, “AND”, “OR” and “NOT” operators are three main Boolean operators used. Using "AND" between search terms was meant to narrow the search results to only include articles that contain all the specified terms, while using the "OR" between search terms broadened the search results to include articles that contain either of the specified terms (Gusenbauer & Haddaway, 2020 ). Searching for “cybersecurity controls “AND "higher education institution" AND “emerging technologies” in one search query returned articles that discussed cybersecurity controls in HEIs in the context AI, cloud computing and blockchain, while queries that used OR returned articles either of the terms including cybersecurity risks and attacks. The operator NOT works on excluding terms that may interfere with the search; they exclude articles that contain the second term (Gusenbauer & Haddaway, 2020 ). This tool helped to find articles relevant to the research topic. For searching cybersecurity risks or controls or measures or threats or vulnerabilities, keywords included combined with emerging technologies such as AI, cloud computing and blockchain in the context of HEIs or universities. Where the search results were not forthcoming, the researcher supplemented Boolean operator queries with the filtering options of search and added keywords and synonyms to refine the search. 3.3 Inclusion and exclusion criteria The search results were screened based on predefined inclusion and exclusion criteria. The criteria included type of publication; whether it was a peer-reviewed academic journal; publication date; based on the English language; and on its relevance to the research topic. Literature included ranged from peer reviewed journals; Master’s and PhD theses, conference proceedings; and book chapters. Included literature meant that they were based on previous studies on emerging technologies in cybersecurity within the HEI sector” and that keywords include AI, ML, and cloud-based threats and controls. 3.4 PRISMA statement The study is conducted in line with the PRISMA guidelines. To be consistent with the requirements of the protocol, the researcher provides the PRISMA flow diagram, which is intended to show the flow of the SLR process, including the selection strategy and the number of studies selected for review (British Medical Journal, 2021 ). 3.5. Extraction process The researcher extracted data from the included literature. The extraction process involves ordering and capturing of both the quantitative and qualitative information from selected literature articles into organised arrangements represented on a form or spreadsheet. Quantitative information collated includes study characteristics, which are numerical descriptions and inferences from the study, such as calculations, statistical measurements, and quantification of qualitative data (opinion or attitudinal scores). Qualitative data in the form are represented in the form of verbatim or quotes and descriptions that can be interpreted for meaning. This information is found in study objectives, research questions, literature reviews or specific methodologies, findings, discussion of results, and recommendations. To develop the form, Kraus Breier and Dasi-Rodriguez (2020) recommends the use of advanced software tools such as Covidence and MS Excel. In this study, a data extraction form was developed using MS Excel, and information extracted from each literature material was documented according to key quantitative and qualitative information describing that study. Data extracted from studies is categorised into the following categories: “type of source” (whether academic or books); “citation count/impact factor”; database name the material comes from; “title of the material”; “authors”; “full reference entry”; “year of publication”; “methodology”; “summary of key findings”; “keywords or key points”; “publication type and publisher”; and “country of origin”. 3.6. Data synthesis Data synthesis approach for this study was qualitative. This meant that the extracted data was subjected to a rigorous thematic analysis process from which themes and subthemes were developed. Thematic synthesis as a methodology applied in SLR involves identifying, coding, and categorising themes and patterns within a dataset (Braun & Clarke, 2020 ). Thematic synthesis steps followed in this study included: initial coding, developing and coding descriptive emerging themes and subthemes from the data, generating analytical themes, and generating meaning. The study organised these themes into a coherent framework to address the research objectives. The goal was to inductively identify, describe and interpret patterns, themes and relationships of these materials to each other. Source British Medical Journal ( 2021 ) Table 1 Description of included literature Database Reference Publication Topics/keywords Year Web of Science Ragab, M., Alghamdi, B. M., Alakhtar, R., Alsobhi, H., Maghrabi, L. A., Alghamdi, G., Nooh, S. & Al-Ghamdi, A. A. M Journal article Cybersecurity in HEI verification and biometrics 2025 Web of Science Sabillon, R., Higuera, J. R. B., Cano, J., & Montalvo, J. A. S. Journal article Cybersecurity controls and audits 2024 Web of Science Cheng, E. C. & Wang, T Journal article Institutional strategies for cybersecurity in higher education institutions 2022 Web of Science Mahmood, S., Chadhar, M. & Firmin, S. Journal article Countermeasure strategies to address cybersecurity challenges amidst major crises 2024 EBSCO Host Parambil, M. M. A., Rustamov, J., Ahmed, S. G., Rustamov, Z., Awad, A. I., Zaki, N. & Alnajjar, F. Journal article AI-based and conventional cybersecurity measures 2024 EBSCOhost Rakstiņš, V., Palkova, K., Juļa, L. & Filipenko, N. Journal article Artificial intelligence as cybersecurity measures in academic institutions 2024 Google Scholar Akor, S. O., Nongo, C., Udofot, C. & Oladokun, B. D. Journal article Cybersecurity awareness and emerging technologies in security and management of libraries 2024 EBSCOhost Godfrey F. Mendes Dissertations/Theses Machine Learning-based risky user behaviour detection to mitigate ransomware attacks on higher education institutions 2024 Google Scholar Maranga, M.J., & Nelson, M. Journal article Cloud computing as a cybersecurity strategy and new security tool 2019 Google Scholar Lainjo, B., & Tsmouche, H. Journal article Artificial intelligence on higher learning institutions 2023 Web of Science Shchavinsky, Y. V., Muzhanova, T. M., Yuriy M Yakymenko, Y. M., & Zaporozhchenko, M. M. Journal article Artificial intelligence for improving situational training of cybersecurity specialists 2023 Web of Science Kuleto, V., Ilić, M., Dumangiu, M., Ranković, M., Martins, O.M.D., Păun, D., & Mihoreanu, L. Journal article Artificial intelligence and machine learning 2021 Web of Science Al-Ghamdi, A.S.A., Ragab, M., Sabir, M.F.S., Elhassanein, A., & Gouda, A.A. Journal article Optimized Artificial Neural Network Techniques to Improve Cybersecurity 2022a Web of Science Al-Ghamdi, A.S.A., Ragab, M., & Sabir, M.F.S. Journal article Enhanced artificial intelligence-based cybersecurity intrusion detection 2022b Web of Science Al-Ghamdi, A.S.A.M., & Mahmoud Ragab, M. Journal article Artificial intelligence Techniques based learner authentication 2022 Google Scholar Alexei, L.A., & Alexei, A. Journal article Cloud computing against cyber security threat such as DoS / DDoS attacks, cross-site scripting, spoofing, unauthorized data access and infection with malicious programmes 2021 Web of Science Ntloedibe, T., Foko, T., & Segooa, M.A. Journal article Cloud computing security 2024 Google Scholar Malele, V. Journal article Cybersecurity and cloud computing 2023 Google Scholar Maulani, G., Gunawan, G., Leli, L., Nabila, E.A., & Sari, W.Y. Journal article Blockchain cybersecurity and certification 2021 Web of Science Alammary, A.S. Journal article Blockchain and digital certification 2024 Scopus Khan, A.A., Laghari, A.A., Shaikh, A.A., Bourouis, S., Mamlouk, A.M., & Alshazly, H. Journal article Blockchain, verification and traceability. 2021 EBSCOhost Singh & Kumar. Journal article Cyber protection of r-Resources 2024 EBSCOhost Elliston, J., Chi, H., Bernadin, S., & Taeb, M. Journal article Integrating blockchain Technology into Cybersecurity Education 2022 4 Results Themes around the need for emerging technologies and their types, such as AI-powered threat detection systems, ML and DL applications; IoT connected devices; cloud computing; and biometric authentication and blockchain-based threat counter-measures and asset management controls were identified. Other themes that emerged from the data included the acknowledgement of the emergent of AI-powered cyber threats; redundancy of traditional methods measures; need for knowledge, awareness and training on new technologies; governance and policy frameworks around new technologies; and the urgent need for the integration of new powerful tools to countermeasure these threats Table 2 Identified themes Theme Studies /references Theme 1 : Need for emerging technologies and their types: Akor et al. ( 2024 ); Alammary ( 2024 ); Al-Ghamdi et al. ( 2022a , b ); Alexei & Alexei ( 2021 ); Cheng & Wang (2021); Elliston et al. (2022); Khan et al. ( 2021 ); Kuleto et al. ( 2021 ); Lainjo & Tmouche (2023); Mahmood et al. ( 2024 ); Malele ( 2023 ); Maranga & Nelson ( 2019 ); Maulani et al. ( 2021 ); Mendes ( 2024 ); Parambil et al. ( 2024 ); Ragab et al. ( 2025 ); Rakstiņš et al. ( 2024 ); Shchavinsky et al. ( 2023 ); Singh & Kumar ( 2024 ) Theme 2 : Digital certification and verification Khan et al. ( 2021 ); Maulani et al. ( 2021 ); Parambil et al. ( 2024 ); Ragab et al. ( 2025 ) Theme 3 : Knowledge, awareness and training on new technology Akor et al. ( 2024 ); Cheng & Wang (2021); Elliston et al. (2022); Kuleto et al. ( 2021 ); Mahmood et al. ( 2024 ); Malele ( 2023 ); Ntloedibe et al. ( 2024 ); Parambil et al. ( 2024 ); Singh & Kumar ( 2024 ) Theme 4 : Governance and policies on new technology Alexei & Alexei ( 2021 ); Cheng & Wang (2021); Ntloedibe et al. ( 2024 ); Mahmood et al. ( 2024 ); Malele ( 2023 ) Theme 5 : Integrative approach to implementing new technology Alammary ( 2024 ); Al-Ghamdi et al. ( 2022b ); Elliston et al. (2022); Mahmood et al. ( 2024 ); Parambil et al. ( 2024 ) 4.1.1 Theme 1: Need for emerging technologies and common types in HEIs Results from the analysis show that almost all studies emphasise on the need for adoption of new and advanced technologies such as AI, ML, IoT, blockchain, cloud computing and biometric authentication for security verification processes into HEI cybersecurity posture (Akor et al., 2024 ; Al-Ghamdi et al., 2022a ; Maranga & Nelson, 2019 ; Ntloedibe et al., 2024 ; Parambil et al., 2024 ; Shchavinsky et al., 2023 ). The diffusion of new technologies such as AI in HEIs cybersecurity environment is inspired by both the redundancy of traditional cybersecurity measures and the “evolution in IT space and new security threats” equally powered by the same advanced technologies within the HEI sector (Ntloedibe et al., 2024 , p.4). The application of these technologies is shown to provide challenges for IT practitioners and for traditional cybersecurity tools. According to Lainjo and Tmouche (2023, p.101), “AI is gradually phasing out guidance counsellors and career services personnel”. At the same time, Shchavinsky et al. ( 2023 ) indicated that the importance of AI-based applications in HEI cybersecurity space is motivated by the application of AI by cybercriminals. Shchavinsky et al. ( 2023 , p.215) argued that “the relevance of developing the ability and skills for cybersecurity professionals to respond promptly to threats is associated with the use of AI by cybercriminals”. These are “robust tools generally used to detect, prevent, and mitigate cyber threats” (Akor et al., 2021, p.2). Al-Ghamdi et al. ( 2022a , b ) designed AI-powered threat detection systems to counter intrusions in HEIs. They have developed the AI-based Automated Outlier Detection for Cybersecurity in Higher Education Institutions (AOD-CSHEI) “to determine the presence of intrusions or attacks in the HEIs” (Al-Ghamdi et al. 2022a , p.3385). The authors (Al-Ghamdi et al. 2022b , p.2895) have also designed the Cybersecurity Intrusion Detection Model for Higher Education Institutions (AICID-HEI) to counter “the occurrence of distinct kinds of intrusions in education institutes”. Other applications of AI mentioned by research reviewed include ChatGPT (Generative Pre-Trained Transformer), which was used to increase HEI professionals’ response rate to equally AI-powered threats (Shchavinsky et al., 2023 ). Adding to these detection tools, ML and DL specialise in detecting anomalies and other unidentifiable activities of the network. According to Parambil et al. ( 2024 , p.10), “DL architectures such as the multi-layer perceptron (MLP), deep neural networks (DNNs), and long short-term memory (LSTM) networks highlights the growing interest in leveraging the power of DL for cybersecurity in educational settings”. “AI-centric methods highlight “machine learning” and “AI” as pivotal tools, and these are associated with “educational technology,” indicating a trend toward making use of advanced computational techniques for security and pedagogy” (Alexei & Alexei, 2021 , p.7) Specialised applications of these advanced tools are found in mitigation techniques against “the risk of DoS/DDoS” (Alexei & Alexei, 2021 , p.128); prevention of phishing, social engineering, malware, ransomware, and insider threats (Singh & Kumar 2024 ); determination of “the existence or absence of intrusions in the HEIs network” (Al-Ghamdi et al., 2022a ); and prevention of unauthorised access using “deep learning-based learning” and biometric authentication techniques (Al-Ghamdi & Ragab, 2022 , p.3132). However, some uses of the new technologies into the counter-measure are varied and broad, and are found to overlap across cybersecurity functions. Cloud architecture encompasses multiple areas including cloud security and cloud services (Alexei & Alexei, 2021 ; Maranga & Nelson, 2019 ; Rakstiņš et al., 2024 ). Cloud security features work to prevent threats; their purpose is to reduce the risk of intrusion and attacks (Rakstiņš et al., 2024 ). Other applications are more industry-directed; in the education context cloud-based education systems “address the need to enhance security protocols within cloud-based education systems, focusing on advanced cryptographic techniques to strengthen the security of portals (Alexei & Alexei, 2021 , p.129). In HEIs, cloud-based platforms facilitate collaborative research, enabling academics from different geographical regions to work together in real-time (Singh & Kumar, 2024 ). Then there is also an element of infrastructure highlighted by most studies in the literature (Alexei & Alexei, 2021 ; Maranga & Nelson, 2019 ; Rakstiņš et al., 2024 ; Singh & Kumar, 2024 ). Alexei and Alexei ( 2021 , p.129) have identified the most common cloud service options for most universities to be “Infrastructure as a Service (IaaS), which offers virtual infrastructure to implement and run the software; Platform as a Service (PaaS), which supports the development of applications through programming languages services and tools offered by cloud platform providers; and Software as a Service (SaaS), which allows the use of software by educational institutions through a cloud platform via the Internet”. The availability of these safe options has created the demands from the HEI sector. Maranga and Nelson ( 2019 , p.375) noted that “most universities are running away from buying some servers and software and so opting for cloud computing”. Instead of spending money on their own localised infrastructure with a risk of cyber threats, these institutions have resorted to using these cloud services. In addition, the study found that technologies such as blockchain are being used to manage HEI assets through blockchain-based management systems that support various academic processes. Blockchain-based education and learning management systems support students’ career paths and courses while preventing fraud of certification across the HEI sector (Khan et al.,, 2021). Khan et al. ( 2021 , p.18) developed the Educational blockchain Ledger (HEDU-Ledger) to “provide robust security and protection in terms of maintaining decentralized candidate degree credentials and data records in a distributed ledger”. However, results of the study also showed that the adoption of new technologies such as AL, ML, and cloud-based services also lead to proliferation of risks based on these technologies. Singh and Kumar ( 2024 , p.11805) maintain that the “rise of cloud-based services also introduces new risks” to data independence and privacy. According to Rakstiņš et al., ( 2024 , p.76), “the increasing use of cloud-based services and digital platforms, as well as ageing IT infrastructure, also contribute to the vulnerability” due to large surface attacks within universities. 4.1.2 Theme: Digital certification and verification The findings from thematic analysis also revealed the importance of digital certification and verification in HEIs. Findings from prominent studies by Alammary ( 2024 ), Ragab et al. ( 2025 ), Parambil et al. ( 2024 ), Khan et al. ( 2021 ), and Maulani et al., ( 2021 ) showed the extent to which IT professionals in HEIs prefer to diffuse and adopt advanced systems like ML, AI and blockchain; these technologies are chosen to mitigate threats such as theft of financial and academic data in institutions; forgery of important documents such as academic certificates; and to enhance privacy in HEI e-learning environments, and access control in campuses. With regard to digital certification, Khan et al. ( 2021 , p.2) have observed that “blockchain technology could become a standardized platform to perform tasks including issuing, verifying, auditing, and tracing immutable records, which would enable the universities to quickly and easily get attested and investigate the forge proof versions of certificates”. However, other studies like Ragab et al. ( 2025 ) have emphasised HEI need for verification for physical access controls using technological developments in biometrics. Blockchain application in digital certification and verification control systems has been found to help HEIs with the tracing capabilities for verifying the legitimacy of certification and HEI records. To ensure certificate record traceability, Khan et al. ( 2021 , p.18) tested and implemented the educational blockchain ledger as a “robust security and protection in terms of maintaining decentralised candidate degree credentials and data records in a distributed ledger”. Alammary ( 2024 ) stressed data integrity and Immutability in digital certification and verification technologies like blockchain. “Blockchain’s inherent immutability plays a critical role in ensuring the integrity of students’ records and enrolment data. All transactions, including enrolments, course modifications, and academic records, are recorded on the blockchain ledger” (Alammary, 2024 ). At the same time, Maulani et al. ( 2021 , p.146) also stressed that blockchain technology “provides certificate data and course data that cannot be modified by third parties” and that it works well against forgeries to ensure the authenticity and integrity of digital certificates for HEIs that have moved to the digital accreditation space. In online learning and access control to campuses where biometrics systems are boosted by AI technologies, Parambil et al. ( 2024 ) have highlighted new technologies’ efficiency for enhancing privacy for students using the systems. This is given the fact that e-learning systems often fall prey to evolved “Injection Attack,” “DDoS/DoS,” and “Malware” (Parambil et al., 2024 , p.8). Thus, ML and AI-based systems are shown to intersect “with privacy in educational technology” to enhance their authentication capabilities (Parambil et al., 2024 , p.8). The findings indicate a pattern of adoption and use of new emerging technologies in HEIs, where HEIs show interest in applying technologies like Blockchain to enhance cybersecurity controls in HEIs. They suggest blockchain a capable mitigating tool for evolving threats in HEIs, and that they have the potential to help HEI leaders produce trusted digital certifications for their stakeholders. 4.1.3 Theme3: Knowledge, awareness and training on new technology Knowledge, awareness and training on new technology is one of the recurring themes in the data. Many studies that have focused on the need for training as a way to prepare students and staff for use of new technologies against new technology-powered attacks has been highlighted. There is an urgent need for training and cybersecurity awareness campaigns to build cybersecurity culture around emerging technologies (Cheng & Wang, 2021; Shchavinsky et al., 2023 ); collaborative learning environment in the HEI (Kuleto Ili´c et al., 2021); importance IS training to curb cloud leakages (Mahmood et al.,, 2024; Ntloedibe et al., 2024 ; Rakstiņš et al., 2024 ); importance of e-learning platforms as both a way to provide safe learning spaces and as gateways to large surface attacks (Alexei & Alexei, 2021 ; Parambil et al., 2024 ); and applications of blockchain (Elliston et al., 2022). In terms of knowledge and awareness, or the lack thereof, Rakstiņš et al. ( 2024 , p.76) noted that one of the key risks associated with new technologies such as cloud computing “is the cybersecurity awareness gap, as many faculty, staff and students lack sufficient awareness and training on cybersecurity best practices”. Ntloedibe et al. ( 2024 , p.1) linked challenges of cloud leakages in HEIs to “a lack of effective training” and failure to implement IS awareness workshops”. The study highlighted training as crucial to managing information for universities as it helps them on “how IS should be managed” (Ntloedibe et al., 2024 , p.1). From the studies, issues themed around collaborative and customised learning also emerged. It is found that through the implementation of AI and ML powered training and cybersecurity awareness workshops, a cybersecurity culture in HEIs can be fostered. Kuleto et al. ( 2021 , p.9) highlighted that “MLs are capable of providing a collaborative learning environment in the HEI, and that such provide researchers with an accessible research environment”. What also comes out strongly in the result is the fact that ML helps HEI design and customise their training programmes (Kuleto et al., 2021 ). HEIs that take advantage of ML algorithms to design their own personalised training has the potential achieve cybersecurity effectiveness. Kuleto et al. ( 2021 , p.11) found that “AI and ML are essential technologies enhancing learning, primarily through students’ skills, collaborative learning in HEI, improving the institution’s security and efficiency, and providing a good research environment”. On this issue, Parambil et al. ( 2024 ) points to the prioritisation of privacy within e-learning platforms as an important issue to drive safety learning. On the other hand, other technologies like cloud computing services and blockchain contribute to HEIs’ secure infrastructure and asset resources crucial for safe learning spaces. The presence of knowledge and training programmes on these technologies was highlighted in some review studies in this research (Elliston et al., 2022; Mahmood et al., 2024 ). In their study, Elliston et al. (2022, p.13) indicated that there is a need to “educate current IT or cybersecurity students on the application of blockchain in supply chain”. In their findings, they reported that “students were made aware of the applications of blockchain” (Elliston et al., 2022, p.2). Mahmood et al. ( 2024 ) mentions cloud service training and awareness training portals, among other training on new technologies, as helpful to students and staff for preparedness of cyberattacks. “Implementing innovative technologies to improve the methods of developing technical and managerial competencies of cybersecurity specialists in higher education institutions is justified in accordance with the strategic direction of education reform in Ukraine” (Shchavinsky et al., 2023 , p.215). Results also showed challenges faced by HEIs in implementing educational training programmes for better adoption of new technologies. The study by Elliston et al. (2022) also found that while students were “aware of crypto currencies like Bitcoin, they had never heard of blockchain and did not know how to write code in Solidity”. The results of this study on this theme show the importance of training on new technologies such as cloud computing and blockchain applications for both students and staff in universities for better and safe learning culture. Training on such new developments in technology contributes to safeguarding institutional assets against continually evolving threats. 4.1.4 Theme 4: Governance and policies on new technology The results showed the need for the integration of the newly adopted technologies into institutional governance for effective and efficient cybersecurity. Conversely, the results also showed the inadequate cybersecurity governance on this area. HEIs often fail to institute knowledge and guidelines for best practices to deal with cybersecurity issues powered by technologies such as AI, ML, and cloud computing. According to Alexei and Alexei ( 2021 , p.128), HEIs often fail to “update systems or manage security patches while also failing to implement access policies at the application or resource level”. Various studies analysed in this review suggested the enforcement of physical controls (Alammary, 2024 ; Cheng & Wang, 202). Alammary ( 2024 , p.16) proposed the Cross-Institutional blockchain Enrolment System (BCHEEN) as a decentralized platform to enhance cross-institutional enrolment processes, and which “uses smart contracts to enforce access control policies autonomously for data access”. Smart contracts are autonomous protocols that define policies, and have the power to mitigate human factors and errors (Alammary, 2024 ). On the other hand, Cheng and Wang (2021, p.1) recommended “strengthening of institutional governance for cybersecurity” as a strategy to respond to AI-based cyberthreats. They recommended institutional governing body for cybersecurity is earmarked to “examine on their institutional relevancy with input from stakeholders” (Cheng & Wang, 2021, p.7). However, this approach to dealing with AI-based threats is traditional and seldom empower IT governance professionals to adopt more robust new technology-based measures to counter AI-based threats. Other studies emphasised the establishment of “strong security policies and closely monitor employee activities” to have better employee compliance and management control of cybersecurity incidents (Mahmood et al., 2024 ; Ntloedibe et al., 2024 ) and establishing ethical guidelines and frameworks (Parambil et al., 2024 ). Parambil et al. ( 2024 , p.1) suggested “interdisciplinary collaboration, continuous monitoring of AI models and the need for comprehensive guidelines to ensure responsible and ethical use of AI in cybersecurity are paramount. The studies emphasised introducing changes in policies and procedures were significant counterstrategies implemented in HERS to mitigate cybersecurity issues can be enacted. The process involves enacting training and awareness programmes to reduce the risk of cyber-attacks and data breaches. 4.1.5 Theme 5: Integrative approach to implementing new technology Integrating traditional cybersecurity practices into new technology-driven ones would benefit HEIs. Data from results also pointed to HEIs still clinging to traditional cybersecurity practices that often find it difficult to counter evolving technologies. At the same time, new technologies meant to enhance cybersecurity effectiveness are also questioned, especially on grounds of their ethical standpoint and effectiveness. In comparing the effectiveness of traditional method approach to cybersecurity against AI-based approaches, Parambil et al. ( 2024 , p.1) found that “while AI-based techniques offer promising solutions for threat detection, authentication, and privacy preservation, their successful implementation requires careful consideration of data privacy, fairness, transparency, and robustness”. New technologies such as AI, given their openness for use as open sources, introduce ethical challenges into the practice. Their effectiveness in dealing with threats such as phishing and malware in open environments such as HEIs provide IT practitioners and leaders with privacy concerns. Thus, newer versions of technologies such as blockchain “have known identities, providing an added layer of trust compared to traditional permissionless” ones (Alammary, 2024 , p.15). Studies highlighted he importance of integrating traditional cybersecurity methods with newer emerging technologies. Parambil et al. ( 2024 ) believes that there is a need for a proactive, multi-layered approach to ensure HEI resilience against evolving cyber threats. There is interconnectedness of focus in HEI cybersecurity approach. Alexei and Alexei ( 2021 , p.7) note that “traditional methods emphasize infrastructure (“cloud computing”), threats (“cybersecurity threats”), and protective measures (“encryption technologies”), seeking to address cybersecurity challenges using established frameworks”. 5 Discussion This study showed a visible footprint of HEI technological adoption in the field of cybersecurity. In the era of more aggressive cybersecurity methods of attacks and evolved threats such as malware, data breaches and intrusions affecting cybersecurity standing of many HEIs around the world, the adoption rate of new advanced technologies to counter them has been noted. Such new developments have been highlighted in many studies, especially in technological developments encompassing AI and ML powered systems such as the AICID-HEI technique for countering intrusions (Akor et al., 2024 ; Al-Ghamdi et al., 2022a , 2022b ; Cheng & Wang, 2021; Maranga & Nelson, 2019 ). Despite such developments, the study results have also showed challenges related to adoption and effectiveness. Reviewed studies have failed to reveal the extent of standardisation and integrated system of adoption of various technologies as most have been shown to be adopted across board without any guidance. Alexei and Alexei ( 2021 ), Maranga and Nelson ( 2019 ), and Rakstiņš et al. ( 2024 ) have shown that the adoption of new technology tended to overlap across cybersecurity functions, indicating there is lack of integration of new systems with existing ones. According to the DOI theory, difficulties of integration and adoption are also exacerbated by the complex and technical nature of radical technologies such as blockchain and AI, of which HEIs lack the will to train staff for (Shin & Jones, 2022 ). HEIs’ perceived issues of cost and time as well as the effort to prepare staff through training as some of the factors affecting adoption and integration of new technologies (Kaputa et al., 2022 ; Ogunlela & Tengesh, 2021). The technology adoption issues reveal the nature of HEIs’ unwillingness to accept and manage risks, which other industries such as the financial sectors have a built willingness and systems to manage (Ogundele & Nzama, 2025 ). Shin and Jones ( 2022 ) have also pointed to the convolution of HEI governance structure and their regulation environment as affecting the decisions that may lead to technology adoption. According to Hartati et al. ( 2023 ), HEIs struggle to integrate their new technologies into their existing IT governance structures to optimise their operations for a secure and safer learning environment. HEIs seldom regulate or optimise their new technological systems through policies for seamless adoption. In the African context, where adoption of technologies is lower, challenges include disjointed infrastructure and lack of policies around adoption (Patel & Ragolane, 2024 ). Lack of regulation, governance and policies make it difficult to integrate or adopt technologies (Patel & Ragolane, 2024 ; Ogundele & Nzama, 2025 ). Emerging technologies have shown to be effective against evolving threats in HEIs (Parambil et al., 2024 ). Technologies integrated with AI can prevent cyber threats through automated detection while ML and DL architectures are successfully applied in intrusion detection strategies to detect anomalies in the network (Parambil et al., 2024 ). Naldi et al. ( 2024 ) have found that implementing AI systems in HEIs improved institutional efficiency, prompted dynamic changes in educational needs, and enhanced the students' learning experience. However, despite the effective applications of these technologies, the success rate depends on various factors, which include financial and non-financial investment in resources for quality of the training models and skills to do so. DOI points to factors such as perceived compatibility and lack of trialability of new technologies as affecting HEIs from adopting technology (Ali et al., 2022 ; Karunagaran et al., 2019 ). In such cases, “policymakers and stakeholders should invest in financial technology solutions that enable real-time monitoring of risks” (Ogundele & Nzama, 2025 , p.12). Conclusions The study submit that although the adoption of AI, ML, IoT, blockchain, and cloud computing and many other new emerging technologies is gaining tract among HEIs, the diffusion and adoption faces challenges of integration and unwillingness to training for new systems. Factors such as lack of integration of systems, resistance to change and the disjointed regulatory environment led to slow adoption and to a proliferation of much more aggressive and evolved threats in HEIs. Thus, there is a need to incorporate and integrate emerging technologies with traditional cybersecurity strategies. Thus, a proactive approach is essential for ensuring new technologies help build the resilience against evolved threats. However, there is also a need to consider intervening factors when integrating new systems with those that exist in the system. The integration of emerging technologies, such as cloud services, into the processes of mitigating threats need an integrated policy environment, which in some institutions does not even exist. HEIs need extend the application established in traditional frameworks to accommodate new infrastructure. The authors believes that by integrating and establishing a centralised framework for governance and incorporating new technologies to existing cybersecurity controls will address existing challenges of technology adoption in HEIs. Declarations Acknowledgements The authors would like to express their sincere gratitude to all individuals who contributed directly or indirectly to this study. Special appreciation is extended to the supervisor for their unwavering guidance, constructive feedback, and continuous support throughout the research process. Their mentorship played a vital role in shaping both the study and the manuscript. Author contributions Lebohang Bosiu wrote the manuscript and Lethiwe Nzama-Sithole supervised the research project. Her contribution has been invaluable in reviewing this paper and assisting me with the research Funding Not applicable. Data availability The data applied for this study is stored at the university’s cloud system and can be provided anytime upon request. Ethics approval and consent to participate Not applicable. Consent for publication We, the authors of this manuscript, give consent for the publication of this paper in the Discover Education Journal Competing interests The authors declare no competing interests. Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). 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Future Internet, 13(0), 1-40. doi:10.3390/fi13020039 United Nations Office on Drugs and Crime. (2024). Transnational organized crime and the convergence of cyber-enabled fraud, underground banking and technological innovation in Southeast Asia: A shifting threat landscape. UNODC. https://www.unodc.org/ Zoellner, J. M., & Porter, K. J. (2017). Translational research: Concepts and methods in dissemination and implementation research. In A. M. Coulston, C. J. Boushey, M. G. Ferruzzi, & L.M. Delahanty (Eds.), Nutrition in the Prevention and Treatment of Disease (pp.125-143). Cambridge: Academic Press. doi:10.1016/B978-0-12-802928-2.00006-0. Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":418337,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7343340/v1/1c0fc30b32be2cb79497e7f1.png"},{"id":97677264,"identity":"5584d1da-beeb-4097-852c-55515989b7c5","added_by":"auto","created_at":"2025-12-08 09:52:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1216381,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7343340/v1/773dc4bb-7dd4-46dc-a706-82e75b4167ed.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"New technology-empowered cybersecurity controls in higher education institutions: a systematic review","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe advent of the fourth industrial revolution (4IR) has brought with it a myriad of advanced technologies, which has for the last decade transformed the workings of many organisations (Byabazaire et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Masinde \u0026amp; Roux, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Emerging technologies such as artificial intelligence (AI), machine learning (ML), cloud computing, blockchain, internet of things (IoT) and many others have enabled innovative business processes (Arumugam et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). AI, smart devices and analytics have managed to connect systems needed by society while at the same time transforming the efficiency and effectiveness of enterprises (Arumugam et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Unfortunately, the advent of newer technologies also coincides with the sophistication of threats; in 2025, traditional threats such as ransomware and phishing are AI-driven and more aggressive (Bankert, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). When technology begins to harbour malicious intent it leads to devastating consequences, such as loss of financial data and other risks (Mukwakwa, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thus, the integration of systems adopted by many organisations also means that businesses had to adopt more advanced security and risk management measures as cyberthreats have also evolved to exploit these technologies to threaten their systems (Cheng \u0026amp; Wang, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Da Costa et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ogundele \u0026amp; Nzama, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the context of higher education institutions (HEIs), the digital integration of systems and the fast adoption of emerging technologies to drive business decisions and processes has also meant necessary measures had to be adopted (Cheng \u0026amp; Wang, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Da Costa et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, at the present moment, however, it is not clear to what extent HEIs have adopted and integrated advanced technologies such as AI and ML into their traditional cybersecurity strategies for mitigating evolving threats within the HIE context. Knowledge of present adoption and alignment to emerging technology is necessary for planning and preparation against the current, fast evolving threats in the HEI cybersecurity environment.\u003c/p\u003e\u003cp\u003eThus, the current study is a response to impending dangers of emerging technologies on HEIs cybersecurity posture. Using diffusion of innovation theory (DOI), the study seeks to explore current developments with regard to adopting emerging technologies such as AI, IoT, ML, blockchain, and cloud computing in HEIs. The study seeks to understand the extent to which these new technologies are adopted at organisational level with the goal of boosting existing cybersecurity controls to mitigate evolving threats in these institutions. The work contributes to understanding the use of these technologies in enhancing traditional controls, and as such provides insights into the degree to which such technologies have successfully matched the current waves of advanced threats in HEIs. In doing so, the study hopes to inspire the development of an inclusive integrated framework and guidelines for adopting new advanced technologies into the existing cybersecurity strategies in HEIs.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"2 Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Emerging Technologies in Cybersecurity\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe evolution of advanced technologies such as AI, ML, Internet of Things (IoT) and cloud computing have coincided with the advancement and sophistication of cyberattacks in recent years (Cheng \u0026amp; Wang, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Both the technological advancements and the advent of the COVID-19 pandemic have added to the increased use of technology and the dependence on the Internet in all aspects of human life (Chapman et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ganesen et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mabatha, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Cybercriminals are reported to have started including IoT hacks and adopting AI-driven phishing, social engineering, ransomware and malware to speed up their attacks, prompting international security organisations to sound warning alarms to organisations about the dangers of these attacks on their valuable assets (Kaloudi \u0026amp; Li, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). AI-enhanced attacks are faster techniques, which often bypass traditional cybersecurity control measures, leaving little response time for organisations that have not adopted equally sophisticated measures (Bankert, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAbbadi and Lachkar (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) have noted that advanced AI-driven phishing attacks such as deepfake and malware forms such as Emotet have already cost large corporations in Britain, Australia and Hong Kong millions of dollars in damages. In the U.S, Bankert (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) has noted the 67% increase in cybersecurity attacks on the financial sector between 2023 and 2024; at the same time there has also been an increase in AI-generated deepfake technology leading to a rise in financial fraud. Likewise, the growth in similar AI-driven attacks has been noted all over the world, with the Asian Pacific region experiencing a 600% increase of deepfakes in 2024 (UNODC, 2024). The scope and increase of the AI-driven attacks are not only limited to corporations; HEIs have also been affected by the scourge (Aboelnor \u0026amp; Sobaih, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Masinde \u0026amp; Roux, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In fact, HEIs are more vulnerable to cyberattacks compared to other organisations such as banks, which often prioritise risk management through technology despite also equally holding the same value of assets (Mukwakwa, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ogundele \u0026amp; Nzama, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). HEIs operate within a culture that encourages academic freedom and openness, while minimising optimisation of cyber risk management (Cheng \u0026amp; Wang, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ulven \u0026amp; Wangen, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Diffusing new technologies in the HEI cybersecurity environment\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eDevelopments of new technologies in cybersecurity have adopted the radical nature of the development of technology in general (Mustapha et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Chinese Academy of Cyberspace Studies, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). While technological developments bring opportunities for competitiveness and growth for many organisations, they also bring in new cybersecurity challenges, which ultimately require a new sense of urgency for protection (Mukwakwa, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mustapha et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As such, diffusion of new technologies in organisations is often required to be seamless if organisations are to keep up with the frequent changes, especially when these organisations must also out-compete cybercriminals who also adopt the same technologies to spread more aggressive threats (Hasani et al.,, 2023). To understand the extent to which organisations such as HEIs diffuse new technologies across their processes to minimise threats, DOI is often applied. This theory is a theory developed by Everett Rogers to understand how new innovations spread within organisations (Zoellner, \u0026amp; Porter, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). According to Zoellner, and Porter (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), DOI focuses individuals and organizations alike to perceive technology as new in order for it to be adopted or to be rendered necessary to be spread through the organisational system. Diffusion of new technology may go through the process of perception of its attributes where its relative benefits and compatibility to current systems are assessed and adjudged, to being accepted and adopted by IT professionals and managers (Makovhololo et al,, 2017).\u003c/p\u003e\u003cp\u003eIn HEIs, IT professionals and managers are responsible for the technology diffusion and adoption processes (Ali et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). According to Ali et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), these professionals form part of the group of factors affecting adoption success, which also include top management support; organization size and culture; skills and knowledge; and technological readiness. Often enough, most HEIs struggle to adopt new technologies given the trade-offs between the perceived benefits of adopting new technology and diverting limited resources to other non-IT related projects (Feng et al.,, 2025; Hartati et al.,, 2023). Despite benefits of new technologies, such as faster and safer security systems, most HEIs perceive the cost, time and effort of training new staff to new technology, hiring of new skills and potential disruptions as a disadvantage (Kaputa et al.,, 2022; Ogunlela \u0026amp; Tengesh, 2021). HEI management, especially in under-resourced HEIs, seldom support the adoption given these reasons (Ali \u0026amp; Shah \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). According to Shin and Jones (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), complex HEIs governance structures, policies and often bureaucratic decision-making processes may work against the new technology proposals. Thus, the complexity of IT governance and the associated regulatory environment make it less likely for institutions to invest in new technologies, particularly when the perceived benefits, sustainability, and technological readiness are not immediately evident (Hartati et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ali et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kaputa et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis research responds to the need for empirical examination of the adoption of new technologies in mitigating evolving threats in HEIs by first exploring the current state of adoption of emerging technologies (AI, IoT, ML, blockchain, and cloud computing) in enhancing cybersecurity controls in HEIs. The study also aims to explore the effectiveness of these new technologies in being able to mitigate evolving threats in HEIs. In achieving these objectives, the study provides ways in which HEIs can successful integrate and implement emerging technologies into the traditional cybersecurity strategy for HEIs to achieve a successful cybersecurity against new threats.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Materials and Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Structured Literature Review process\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTo conduct this study, a systematic literature review (SLR) method was applied as a research approach. SLR follows a rigorous and systematic process to ensure the comprehensiveness and reliability of the findings. The review was conducted in several stages, including defining the review's scope; searching literature on reputable academic databases; selecting literature using inclusion-exclusion criteria; extracting and synthesising data extracted from selected literature; and reporting findings. In applying SLR, the study adopted the subjectivist-constructivist position in research, which holds that reality is subject to researchers\u0026rsquo; interpretation on matters under study (Lau \u0026amp; Kuziemsky, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAs a research approach, SLR is secondary research by nature (Paoloni et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As a methodology, SLR is used to \u0026ldquo;review current knowledge on a topic about research questions to suggest areas for further examination\" (Carrera-Rivera et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, p.2). The study approached SLR through a secondary qualitative research to identify, collate and interpret themes and subthemes emerging from selected studies on emerging technologies in HEIs across the world. SLR in the form of a qualitative research helped the researcher identify key information assets and common threat events caused by AI-powered and cloud-based cyberattacks in HEIs. At the same time, the method was used to identify AI-powered, cloud-based, deep learning-enabled biometric verification, ML and blockchain-based cybersecurity measures and controls instituted within HEIs.\u003c/p\u003e\u003cp\u003eFor effective SLR, the researcher followed a Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol (PRISMA) at the start of the review process as a plan of action. Sargeant and O\u0026rsquo;Connor (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) recognised that such protocols as PRISMA help researchers undertaking SLR to ensure that the research is conducted thoroughly throughout the study. That is, issues of reliability and validity are applied to ensure quality results. In this case, the protocol helped the researcher to formulate research questions and set parameters for the search of keyword strings on selected online databases to get relevant materials on emerging technology for cybersecurity in the context of HEIs. The protocol also helped the researcher to set requirements for inclusion and exclusion of materials for review.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.2 The search strategy\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe SLR process involved identifying relevant literature through comprehensive searches based on key themes and keywords related to the topic. Literature was then selected using inclusion and exclusion criteria, focusing on relevance to the study, year of publication, and citation index to ensure quality. Two processes were involved in devising a search strategy for literature. Firstly, quality databases were selected to source review literature. Databases were selected based on their bibliometric performance, and included Web of Science, Scopus, EBSCOhost and Google Scholar. Secondly, search strategy and were created through which the Boolean operator method of searching databases was applied.\u003c/p\u003e\u003cp\u003eTo find and identify literature in the databases, free text search was used. In review studies, a free-text search involves using words that appear in the article's title and abstract (Grewal et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This method allows researchers to search for specific terms used by authors themselves, enhancing the search for relevant articles. The keywords and possible combinations were in the form of subject heading and free text search. Boolean operators are essential tools for conducting structured and effective literature reviews (Ugwu \u0026amp; Opah, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this study, \u0026ldquo;AND\u0026rdquo;, \u0026ldquo;OR\u0026rdquo; and \u0026ldquo;NOT\u0026rdquo; operators are three main Boolean operators used. Using \"AND\" between search terms was meant to narrow the search results to only include articles that contain all the specified terms, while using the \"OR\" between search terms broadened the search results to include articles that contain either of the specified terms (Gusenbauer \u0026amp; Haddaway, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSearching for \u0026ldquo;cybersecurity controls \u0026ldquo;AND \"higher education institution\" AND \u0026ldquo;emerging technologies\u0026rdquo; in one search query returned articles that discussed cybersecurity controls in HEIs in the context AI, cloud computing and blockchain, while queries that used OR returned articles either of the terms including cybersecurity risks and attacks. The operator NOT works on excluding terms that may interfere with the search; they exclude articles that contain the second term (Gusenbauer \u0026amp; Haddaway, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This tool helped to find articles relevant to the research topic. For searching cybersecurity risks or controls or measures or threats or vulnerabilities, keywords included combined with emerging technologies such as AI, cloud computing and blockchain in the context of HEIs or universities. Where the search results were not forthcoming, the researcher supplemented Boolean operator queries with the filtering options of search and added keywords and synonyms to refine the search.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Inclusion and exclusion criteria\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe search results were screened based on predefined inclusion and exclusion criteria. The criteria included type of publication; whether it was a peer-reviewed academic journal; publication date; based on the English language; and on its relevance to the research topic. Literature included ranged from peer reviewed journals; Master\u0026rsquo;s and PhD theses, conference proceedings; and book chapters. Included literature meant that they were based on previous studies on emerging technologies in cybersecurity within the HEI sector\u0026rdquo; and that keywords include AI, ML, and cloud-based threats and controls.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.4 PRISMA statement\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe study is conducted in line with the PRISMA guidelines. To be consistent with the requirements of the protocol, the researcher provides the PRISMA flow diagram, which is intended to show the flow of the SLR process, including the selection strategy and the number of studies selected for review (British Medical Journal, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Extraction process\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe researcher extracted data from the included literature. The extraction process involves ordering and capturing of both the quantitative and qualitative information from selected literature articles into organised arrangements represented on a form or spreadsheet. Quantitative information collated includes study characteristics, which are numerical descriptions and inferences from the study, such as calculations, statistical measurements, and quantification of qualitative data (opinion or attitudinal scores). Qualitative data in the form are represented in the form of verbatim or quotes and descriptions that can be interpreted for meaning. This information is found in study objectives, research questions, literature reviews or specific methodologies, findings, discussion of results, and recommendations.\u003c/p\u003e\u003cp\u003eTo develop the form, Kraus Breier and Dasi-Rodriguez (2020) recommends the use of advanced software tools such as Covidence and MS Excel. In this study, a data extraction form was developed using MS Excel, and information extracted from each literature material was documented according to key quantitative and qualitative information describing that study. Data extracted from studies is categorised into the following categories: \u0026ldquo;type of source\u0026rdquo; (whether academic or books); \u0026ldquo;citation count/impact factor\u0026rdquo;; database name the material comes from; \u0026ldquo;title of the material\u0026rdquo;; \u0026ldquo;authors\u0026rdquo;; \u0026ldquo;full reference entry\u0026rdquo;; \u0026ldquo;year of publication\u0026rdquo;; \u0026ldquo;methodology\u0026rdquo;; \u0026ldquo;summary of key findings\u0026rdquo;; \u0026ldquo;keywords or key points\u0026rdquo;; \u0026ldquo;publication type and publisher\u0026rdquo;; and \u0026ldquo;country of origin\u0026rdquo;.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.6. Data synthesis\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eData synthesis approach for this study was qualitative. This meant that the extracted data was subjected to a rigorous thematic analysis process from which themes and subthemes were developed. Thematic synthesis as a methodology applied in SLR involves identifying, coding, and categorising themes and patterns within a dataset (Braun \u0026amp; Clarke, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thematic synthesis steps followed in this study included: initial coding, developing and coding descriptive emerging themes and subthemes from the data, generating analytical themes, and generating meaning. The study organised these themes into a coherent framework to address the research objectives. The goal was to inductively identify, describe and interpret patterns, themes and relationships of these materials to each other.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003cp\u003eBritish Medical Journal (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescription of included literature\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDatabase\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePublication\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTopics/keywords\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeb of Science\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRagab, M., Alghamdi, B. M., Alakhtar, R., Alsobhi, H., Maghrabi, L. A., Alghamdi, G., Nooh, S. \u0026amp; Al-Ghamdi, A. A. M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJournal article\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCybersecurity in HEI verification and biometrics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeb of Science\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSabillon, R., Higuera, J. R. B., Cano, J., \u0026amp; Montalvo, J. A. S.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJournal article\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCybersecurity controls and audits\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeb of Science\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCheng, E. C. \u0026amp; Wang, T\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJournal article\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInstitutional strategies for cybersecurity in higher education institutions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeb of Science\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMahmood, S., Chadhar, M. \u0026amp; Firmin, S.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJournal article\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCountermeasure strategies to address cybersecurity challenges amidst major crises\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEBSCO Host\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eParambil, M. M. A., Rustamov, J., Ahmed, S. G., Rustamov, Z., Awad, A. I., Zaki, N. \u0026amp; Alnajjar, F.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJournal article\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAI-based and conventional cybersecurity measures\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEBSCOhost\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRakstiņš, V., Palkova, K., Juļa, L. \u0026amp; Filipenko, N.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJournal article\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eArtificial intelligence as cybersecurity measures in academic institutions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGoogle Scholar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAkor, S. O., Nongo, C., Udofot, C. \u0026amp; Oladokun, B. D.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJournal article\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCybersecurity awareness and emerging technologies in security and management of libraries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEBSCOhost\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGodfrey F. Mendes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDissertations/Theses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMachine Learning-based risky user behaviour detection to mitigate ransomware attacks on higher education institutions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGoogle Scholar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMaranga, M.J., \u0026amp; Nelson, M.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJournal article\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCloud computing as a cybersecurity strategy and new security tool\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGoogle Scholar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLainjo, B., \u0026amp; Tsmouche, H.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJournal article\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eArtificial intelligence on higher learning institutions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeb of Science\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShchavinsky, Y. V., Muzhanova, T. M., Yuriy M Yakymenko, Y. M., \u0026amp; Zaporozhchenko, M. M.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJournal article\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eArtificial intelligence for improving situational training of cybersecurity specialists\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeb of Science\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKuleto, V., Ilić, M., Dumangiu, M., Ranković, M., Martins, O.M.D., Păun, D., \u0026amp; Mihoreanu, L.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJournal article\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eArtificial intelligence and machine learning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeb of Science\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAl-Ghamdi, A.S.A., Ragab, M., Sabir, M.F.S., Elhassanein, A., \u0026amp; Gouda, A.A.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJournal article\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOptimized Artificial Neural Network Techniques to Improve Cybersecurity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2022a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeb of Science\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAl-Ghamdi, A.S.A., Ragab, M., \u0026amp; Sabir, M.F.S.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJournal article\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEnhanced artificial intelligence-based cybersecurity intrusion detection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2022b\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeb of Science\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAl-Ghamdi, A.S.A.M., \u0026amp; Mahmoud Ragab, M.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJournal article\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eArtificial intelligence Techniques based learner authentication\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGoogle Scholar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAlexei, L.A., \u0026amp; Alexei, A.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJournal article\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCloud computing against cyber security threat such as DoS / DDoS attacks, cross-site scripting, spoofing, unauthorized data access and infection with malicious programmes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeb of Science\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNtloedibe, T., Foko, T., \u0026amp; Segooa, M.A.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJournal article\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCloud computing security\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGoogle Scholar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMalele, V.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJournal article\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCybersecurity and cloud computing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGoogle Scholar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMaulani, G., Gunawan, G., Leli, L., Nabila, E.A., \u0026amp; Sari, W.Y.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJournal article\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBlockchain cybersecurity and certification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeb of Science\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAlammary, A.S.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJournal article\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBlockchain and digital certification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScopus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKhan, A.A., Laghari, A.A., Shaikh, A.A., Bourouis, S., Mamlouk, A.M., \u0026amp; Alshazly, H.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJournal article\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBlockchain, verification and traceability.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEBSCOhost\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSingh \u0026amp; Kumar.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJournal article\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCyber protection of r-Resources\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEBSCOhost\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eElliston, J., Chi, H., Bernadin, S., \u0026amp; Taeb, M.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJournal article\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIntegrating blockchain Technology into Cybersecurity Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Results","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThemes around the need for emerging technologies and their types, such as AI-powered threat detection systems, ML and DL applications; IoT connected devices; cloud computing; and biometric authentication and blockchain-based threat counter-measures and asset management controls were identified. Other themes that emerged from the data included the acknowledgement of the emergent of AI-powered cyber threats; redundancy of traditional methods measures; need for knowledge, awareness and training on new technologies; governance and policy frameworks around new technologies; and the urgent need for the integration of new powerful tools to countermeasure these threats\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIdentified themes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTheme\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStudies /references\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTheme 1\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eNeed for emerging technologies and their types:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAkor et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Alammary (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Al-Ghamdi et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003eb\u003c/span\u003e); Alexei \u0026amp; Alexei (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); Cheng \u0026amp; Wang (2021); Elliston et al. (2022); Khan et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); Kuleto et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); Lainjo \u0026amp; Tmouche (2023); Mahmood et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Malele (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); Maranga \u0026amp; Nelson (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); Maulani et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); Mendes (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Parambil et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Ragab et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); Rakstiņš et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Shchavinsky et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); Singh \u0026amp; Kumar (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTheme 2\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eDigital certification and verification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKhan et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); Maulani et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); Parambil et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Ragab et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTheme 3\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eKnowledge, awareness and training on new technology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAkor et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Cheng \u0026amp; Wang (2021); Elliston et al. (2022); Kuleto et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); Mahmood et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Malele (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); Ntloedibe et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Parambil et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Singh \u0026amp; Kumar (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTheme 4\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eGovernance and policies on new technology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAlexei \u0026amp; Alexei (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); Cheng \u0026amp; Wang (2021); Ntloedibe et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Mahmood et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Malele (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTheme 5\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eIntegrative approach to implementing new technology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAlammary (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Al-Ghamdi et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e); Elliston et al. (2022); Mahmood et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Parambil et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e4.1.1 Theme 1: Need for emerging technologies and common types in HEIs\u003c/div\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eResults from the analysis show that almost all studies emphasise on the need for adoption of new and advanced technologies such as AI, ML, IoT, blockchain, cloud computing and biometric authentication for security verification processes into HEI cybersecurity posture (Akor et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Al-Ghamdi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Maranga \u0026amp; Nelson, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ntloedibe et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Parambil et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Shchavinsky et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The diffusion of new technologies such as AI in HEIs cybersecurity environment is inspired by both the redundancy of traditional cybersecurity measures and the \u0026ldquo;evolution in IT space and new security threats\u0026rdquo; equally powered by the same advanced technologies within the HEI sector (Ntloedibe et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, p.4). The application of these technologies is shown to provide challenges for IT practitioners and for traditional cybersecurity tools. According to Lainjo and Tmouche (2023, p.101), \u0026ldquo;AI is gradually phasing out guidance counsellors and career services personnel\u0026rdquo;. At the same time, Shchavinsky et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) indicated that the importance of AI-based applications in HEI cybersecurity space is motivated by the application of AI by cybercriminals. Shchavinsky et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, p.215) argued that \u0026ldquo;the relevance of developing the ability and skills for cybersecurity professionals to respond promptly to threats is associated with the use of AI by cybercriminals\u0026rdquo;. These are \u0026ldquo;robust tools generally used to detect, prevent, and mitigate cyber threats\u0026rdquo; (Akor et al., 2021, p.2).\u003c/p\u003e\u003cp\u003eAl-Ghamdi et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003eb\u003c/span\u003e) designed AI-powered threat detection systems to counter intrusions in HEIs. They have developed the AI-based Automated Outlier Detection for Cybersecurity in Higher Education Institutions (AOD-CSHEI) \u0026ldquo;to determine the presence of intrusions or attacks in the HEIs\u0026rdquo; (Al-Ghamdi et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e, p.3385). The authors (Al-Ghamdi et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e, p.2895) have also designed the Cybersecurity Intrusion Detection Model for Higher Education Institutions (AICID-HEI) to counter \u0026ldquo;the occurrence of distinct kinds of intrusions in education institutes\u0026rdquo;. Other applications of AI mentioned by research reviewed include ChatGPT (Generative Pre-Trained Transformer), which was used to increase HEI professionals\u0026rsquo; response rate to equally AI-powered threats (Shchavinsky et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Adding to these detection tools, ML and DL specialise in detecting anomalies and other unidentifiable activities of the network. According to Parambil et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, p.10), \u0026ldquo;DL architectures such as the multi-layer perceptron (MLP), deep neural networks (DNNs), and long short-term memory (LSTM) networks highlights the growing interest in leveraging the power of DL for cybersecurity in educational settings\u0026rdquo;. \u0026ldquo;AI-centric methods highlight \u0026ldquo;machine learning\u0026rdquo; and \u0026ldquo;AI\u0026rdquo; as pivotal tools, and these are associated with \u0026ldquo;educational technology,\u0026rdquo; indicating a trend toward making use of advanced computational techniques for security and pedagogy\u0026rdquo; (Alexei \u0026amp; Alexei, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, p.7)\u003c/p\u003e\u003cp\u003eSpecialised applications of these advanced tools are found in mitigation techniques against \u0026ldquo;the risk of DoS/DDoS\u0026rdquo; (Alexei \u0026amp; Alexei, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, p.128); prevention of phishing, social engineering, malware, ransomware, and insider threats (Singh \u0026amp; Kumar \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); determination of \u0026ldquo;the existence or absence of intrusions in the HEIs network\u0026rdquo; (Al-Ghamdi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e); and prevention of unauthorised access using \u0026ldquo;deep learning-based learning\u0026rdquo; and biometric authentication techniques (Al-Ghamdi \u0026amp; Ragab, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, p.3132). However, some uses of the new technologies into the counter-measure are varied and broad, and are found to overlap across cybersecurity functions. Cloud architecture encompasses multiple areas including cloud security and cloud services (Alexei \u0026amp; Alexei, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Maranga \u0026amp; Nelson, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rakstiņš et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Cloud security features work to prevent threats; their purpose is to reduce the risk of intrusion and attacks (Rakstiņš et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Other applications are more industry-directed; in the education context cloud-based education systems \u0026ldquo;address the need to enhance security protocols within cloud-based education systems, focusing on advanced cryptographic techniques to strengthen the security of portals (Alexei \u0026amp; Alexei, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, p.129). In HEIs, cloud-based platforms facilitate collaborative research, enabling academics from different geographical regions to work together in real-time (Singh \u0026amp; Kumar, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Then there is also an element of infrastructure highlighted by most studies in the literature (Alexei \u0026amp; Alexei, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Maranga \u0026amp; Nelson, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rakstiņš et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Singh \u0026amp; Kumar, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlexei and Alexei (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, p.129) have identified the most common cloud service options for most universities to be \u0026ldquo;Infrastructure as a Service (IaaS), which offers virtual infrastructure to implement and run the software; Platform as a Service (PaaS), which supports the development of applications through programming languages services and tools offered by cloud platform providers; and Software as a Service (SaaS), which allows the use of software by educational institutions through a cloud platform via the Internet\u0026rdquo;. The availability of these safe options has created the demands from the HEI sector. Maranga and Nelson (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, p.375) noted that \u0026ldquo;most universities are running away from buying some servers and software and so opting for cloud computing\u0026rdquo;. Instead of spending money on their own localised infrastructure with a risk of cyber threats, these institutions have resorted to using these cloud services.\u003c/p\u003e\u003cp\u003eIn addition, the study found that technologies such as blockchain are being used to manage HEI assets through blockchain-based management systems that support various academic processes. Blockchain-based education and learning management systems support students\u0026rsquo; career paths and courses while preventing fraud of certification across the HEI sector (Khan et al.,, 2021). Khan et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, p.18) developed the Educational blockchain Ledger (HEDU-Ledger) to \u0026ldquo;provide robust security and protection in terms of maintaining decentralized candidate degree credentials and data records in a distributed ledger\u0026rdquo;. However, results of the study also showed that the adoption of new technologies such as AL, ML, and cloud-based services also lead to proliferation of risks based on these technologies. Singh and Kumar (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, p.11805) maintain that the \u0026ldquo;rise of cloud-based services also introduces new risks\u0026rdquo; to data independence and privacy. According to Rakstiņš et al., (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, p.76), \u0026ldquo;the increasing use of cloud-based services and digital platforms, as well as ageing IT infrastructure, also contribute to the vulnerability\u0026rdquo; due to large surface attacks within universities.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e4.1.2 Theme: Digital certification and verification\u003c/div\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe findings from thematic analysis also revealed the importance of digital certification and verification in HEIs. Findings from prominent studies by Alammary (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), Ragab et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), Parambil et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), Khan et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and Maulani et al., (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) showed the extent to which IT professionals in HEIs prefer to diffuse and adopt advanced systems like ML, AI and blockchain; these technologies are chosen to mitigate threats such as theft of financial and academic data in institutions; forgery of important documents such as academic certificates; and to enhance privacy in HEI e-learning environments, and access control in campuses. With regard to digital certification, Khan et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, p.2) have observed that \u0026ldquo;blockchain technology could become a standardized platform to perform tasks including issuing, verifying, auditing, and tracing immutable records, which would enable the universities to quickly and easily get attested and investigate the forge proof versions of certificates\u0026rdquo;. However, other studies like Ragab et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) have emphasised HEI need for verification for physical access controls using technological developments in biometrics. Blockchain application in digital certification and verification control systems has been found to help HEIs with the tracing capabilities for verifying the legitimacy of certification and HEI records. To ensure certificate record traceability, Khan et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, p.18) tested and implemented the educational blockchain ledger as a \u0026ldquo;robust security and protection in terms of maintaining decentralised candidate degree credentials and data records in a distributed ledger\u0026rdquo;.\u003c/p\u003e\u003cp\u003eAlammary (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) stressed data integrity and Immutability in digital certification and verification technologies like blockchain. \u0026ldquo;Blockchain\u0026rsquo;s inherent immutability plays a critical role in ensuring the integrity of students\u0026rsquo; records and enrolment data. All transactions, including enrolments, course modifications, and academic records, are recorded on the blockchain ledger\u0026rdquo; (Alammary, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). At the same time, Maulani et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, p.146) also stressed that blockchain technology \u0026ldquo;provides certificate data and course data that cannot be modified by third parties\u0026rdquo; and that it works well against forgeries to ensure the authenticity and integrity of digital certificates for HEIs that have moved to the digital accreditation space. In online learning and access control to campuses where biometrics systems are boosted by AI technologies, Parambil et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) have highlighted new technologies\u0026rsquo; efficiency for enhancing privacy for students using the systems. This is given the fact that e-learning systems often fall prey to evolved \u0026ldquo;Injection Attack,\u0026rdquo; \u0026ldquo;DDoS/DoS,\u0026rdquo; and \u0026ldquo;Malware\u0026rdquo; (Parambil et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, p.8). Thus, ML and AI-based systems are shown to intersect \u0026ldquo;with privacy in educational technology\u0026rdquo; to enhance their authentication capabilities (Parambil et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, p.8). The findings indicate a pattern of adoption and use of new emerging technologies in HEIs, where HEIs show interest in applying technologies like Blockchain to enhance cybersecurity controls in HEIs. They suggest blockchain a capable mitigating tool for evolving threats in HEIs, and that they have the potential to help HEI leaders produce trusted digital certifications for their stakeholders.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e4.1.3 Theme3: Knowledge, awareness and training on new technology\u003c/div\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eKnowledge, awareness and training on new technology is one of the recurring themes in the data. Many studies that have focused on the need for training as a way to prepare students and staff for use of new technologies against new technology-powered attacks has been highlighted. There is an urgent need for training and cybersecurity awareness campaigns to build cybersecurity culture around emerging technologies (Cheng \u0026amp; Wang, 2021; Shchavinsky et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); collaborative learning environment in the HEI (Kuleto Ili\u0026acute;c et al., 2021); importance IS training to curb cloud leakages (Mahmood et al.,, 2024; Ntloedibe et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Rakstiņš et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); importance of e-learning platforms as both a way to provide safe learning spaces and as gateways to large surface attacks (Alexei \u0026amp; Alexei, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Parambil et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); and applications of blockchain (Elliston et al., 2022). In terms of knowledge and awareness, or the lack thereof, Rakstiņš et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, p.76) noted that one of the key risks associated with new technologies such as cloud computing \u0026ldquo;is the cybersecurity awareness gap, as many faculty, staff and students lack sufficient awareness and training on cybersecurity best practices\u0026rdquo;. Ntloedibe et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, p.1) linked challenges of cloud leakages in HEIs to \u0026ldquo;a lack of effective training\u0026rdquo; and failure to implement IS awareness workshops\u0026rdquo;.\u003c/p\u003e\u003cp\u003eThe study highlighted training as crucial to managing information for universities as it helps them on \u0026ldquo;how IS should be managed\u0026rdquo; (Ntloedibe et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, p.1). From the studies, issues themed around collaborative and customised learning also emerged. It is found that through the implementation of AI and ML powered training and cybersecurity awareness workshops, a cybersecurity culture in HEIs can be fostered. Kuleto et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, p.9) highlighted that \u0026ldquo;MLs are capable of providing a collaborative learning environment in the HEI, and that such provide researchers with an accessible research environment\u0026rdquo;. What also comes out strongly in the result is the fact that ML helps HEI design and customise their training programmes (Kuleto et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). HEIs that take advantage of ML algorithms to design their own personalised training has the potential achieve cybersecurity effectiveness. Kuleto et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, p.11) found that \u0026ldquo;AI and ML are essential technologies enhancing learning, primarily through students\u0026rsquo; skills, collaborative learning in HEI, improving the institution\u0026rsquo;s security and efficiency, and providing a good research environment\u0026rdquo;. On this issue, Parambil et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) points to the prioritisation of privacy within e-learning platforms as an important issue to drive safety learning.\u003c/p\u003e\u003cp\u003eOn the other hand, other technologies like cloud computing services and blockchain contribute to HEIs\u0026rsquo; secure infrastructure and asset resources crucial for safe learning spaces. The presence of knowledge and training programmes on these technologies was highlighted in some review studies in this research (Elliston et al., 2022; Mahmood et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In their study, Elliston et al. (2022, p.13) indicated that there is a need to \u0026ldquo;educate current IT or cybersecurity students on the application of blockchain in supply chain\u0026rdquo;. In their findings, they reported that \u0026ldquo;students were made aware of the applications of blockchain\u0026rdquo; (Elliston et al., 2022, p.2). Mahmood et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) mentions cloud service training and awareness training portals, among other training on new technologies, as helpful to students and staff for preparedness of cyberattacks. \u0026ldquo;Implementing innovative technologies to improve the methods of developing technical and managerial competencies of cybersecurity specialists in higher education institutions is justified in accordance with the strategic direction of education reform in Ukraine\u0026rdquo; (Shchavinsky et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, p.215). Results also showed challenges faced by HEIs in implementing educational training programmes for better adoption of new technologies. The study by Elliston et al. (2022) also found that while students were \u0026ldquo;aware of crypto currencies like Bitcoin, they had never heard of blockchain and did not know how to write code in Solidity\u0026rdquo;. The results of this study on this theme show the importance of training on new technologies such as cloud computing and blockchain applications for both students and staff in universities for better and safe learning culture. Training on such new developments in technology contributes to safeguarding institutional assets against continually evolving threats.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e4.1.4 Theme 4: Governance and policies on new technology\u003c/div\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe results showed the need for the integration of the newly adopted technologies into institutional governance for effective and efficient cybersecurity. Conversely, the results also showed the inadequate cybersecurity governance on this area. HEIs often fail to institute knowledge and guidelines for best practices to deal with cybersecurity issues powered by technologies such as AI, ML, and cloud computing. According to Alexei and Alexei (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, p.128), HEIs often fail to \u0026ldquo;update systems or manage security patches while also failing to implement access policies at the application or resource level\u0026rdquo;. Various studies analysed in this review suggested the enforcement of physical controls (Alammary, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Cheng \u0026amp; Wang, 202). Alammary (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, p.16) proposed the Cross-Institutional blockchain Enrolment System (BCHEEN) as a decentralized platform to enhance cross-institutional enrolment processes, and which \u0026ldquo;uses smart contracts to enforce access control policies autonomously for data access\u0026rdquo;. Smart contracts are autonomous protocols that define policies, and have the power to mitigate human factors and errors (Alammary, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOn the other hand, Cheng and Wang (2021, p.1) recommended \u0026ldquo;strengthening of institutional governance for cybersecurity\u0026rdquo; as a strategy to respond to AI-based cyberthreats. They recommended institutional governing body for cybersecurity is earmarked to \u0026ldquo;examine on their institutional relevancy with input from stakeholders\u0026rdquo; (Cheng \u0026amp; Wang, 2021, p.7). However, this approach to dealing with AI-based threats is traditional and seldom empower IT governance professionals to adopt more robust new technology-based measures to counter AI-based threats. Other studies emphasised the establishment of \u0026ldquo;strong security policies and closely monitor employee activities\u0026rdquo; to have better employee compliance and management control of cybersecurity incidents (Mahmood et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ntloedibe et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and establishing ethical guidelines and frameworks (Parambil et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Parambil et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, p.1) suggested \u0026ldquo;interdisciplinary collaboration, continuous monitoring of AI models and the need for comprehensive guidelines to ensure responsible and ethical use of AI in cybersecurity are paramount. The studies emphasised introducing changes in policies and procedures were significant counterstrategies implemented in HERS to mitigate cybersecurity issues can be enacted. The process involves enacting training and awareness programmes to reduce the risk of cyber-attacks and data breaches.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e4.1.5 Theme 5: Integrative approach to implementing new technology\u003c/div\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIntegrating traditional cybersecurity practices into new technology-driven ones would benefit HEIs. Data from results also pointed to HEIs still clinging to traditional cybersecurity practices that often find it difficult to counter evolving technologies. At the same time, new technologies meant to enhance cybersecurity effectiveness are also questioned, especially on grounds of their ethical standpoint and effectiveness. In comparing the effectiveness of traditional method approach to cybersecurity against AI-based approaches, Parambil et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, p.1) found that \u0026ldquo;while AI-based techniques offer promising solutions for threat detection, authentication, and privacy preservation, their successful implementation requires careful consideration of data privacy, fairness, transparency, and robustness\u0026rdquo;. New technologies such as AI, given their openness for use as open sources, introduce ethical challenges into the practice. Their effectiveness in dealing with threats such as phishing and malware in open environments such as HEIs provide IT practitioners and leaders with privacy concerns. Thus, newer versions of technologies such as blockchain \u0026ldquo;have known identities, providing an added layer of trust compared to traditional permissionless\u0026rdquo; ones (Alammary, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, p.15).\u003c/p\u003e\u003cp\u003eStudies highlighted he importance of integrating traditional cybersecurity methods with newer emerging technologies. Parambil et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) believes that there is a need for a proactive, multi-layered approach to ensure HEI resilience against evolving cyber threats. There is interconnectedness of focus in HEI cybersecurity approach. Alexei and Alexei (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, p.7) note that \u0026ldquo;traditional methods emphasize infrastructure (\u0026ldquo;cloud computing\u0026rdquo;), threats (\u0026ldquo;cybersecurity threats\u0026rdquo;), and protective measures (\u0026ldquo;encryption technologies\u0026rdquo;), seeking to address cybersecurity challenges using established frameworks\u0026rdquo;.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Discussion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis study showed a visible footprint of HEI technological adoption in the field of cybersecurity. In the era of more aggressive cybersecurity methods of attacks and evolved threats such as malware, data breaches and intrusions affecting cybersecurity standing of many HEIs around the world, the adoption rate of new advanced technologies to counter them has been noted. Such new developments have been highlighted in many studies, especially in technological developments encompassing AI and ML powered systems such as the AICID-HEI technique for countering intrusions (Akor et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Al-Ghamdi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e; Cheng \u0026amp; Wang, 2021; Maranga \u0026amp; Nelson, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Despite such developments, the study results have also showed challenges related to adoption and effectiveness.\u003c/p\u003e\u003cp\u003eReviewed studies have failed to reveal the extent of standardisation and integrated system of adoption of various technologies as most have been shown to be adopted across board without any guidance. Alexei and Alexei (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Maranga and Nelson (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and Rakstiņš et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) have shown that the adoption of new technology tended to overlap across cybersecurity functions, indicating there is lack of integration of new systems with existing ones. According to the DOI theory, difficulties of integration and adoption are also exacerbated by the complex and technical nature of radical technologies such as blockchain and AI, of which HEIs lack the will to train staff for (Shin \u0026amp; Jones, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). HEIs\u0026rsquo; perceived issues of cost and time as well as the effort to prepare staff through training as some of the factors affecting adoption and integration of new technologies (Kaputa et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ogunlela \u0026amp; Tengesh, 2021). The technology adoption issues reveal the nature of HEIs\u0026rsquo; unwillingness to accept and manage risks, which other industries such as the financial sectors have a built willingness and systems to manage (Ogundele \u0026amp; Nzama, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Shin and Jones (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) have also pointed to the convolution of HEI governance structure and their regulation environment as affecting the decisions that may lead to technology adoption. According to Hartati et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), HEIs struggle to integrate their new technologies into their existing IT governance structures to optimise their operations for a secure and safer learning environment. HEIs seldom regulate or optimise their new technological systems through policies for seamless adoption. In the African context, where adoption of technologies is lower, challenges include disjointed infrastructure and lack of policies around adoption (Patel \u0026amp; Ragolane, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Lack of regulation, governance and policies make it difficult to integrate or adopt technologies (Patel \u0026amp; Ragolane, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ogundele \u0026amp; Nzama, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEmerging technologies have shown to be effective against evolving threats in HEIs (Parambil et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Technologies integrated with AI can prevent cyber threats through automated detection while ML and DL architectures are successfully applied in intrusion detection strategies to detect anomalies in the network (Parambil et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Naldi et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) have found that implementing AI systems in HEIs improved institutional efficiency, prompted dynamic changes in educational needs, and enhanced the students' learning experience. However, despite the effective applications of these technologies, the success rate depends on various factors, which include financial and non-financial investment in resources for quality of the training models and skills to do so. DOI points to factors such as perceived compatibility and lack of trialability of new technologies as affecting HEIs from adopting technology (Ali et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Karunagaran et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In such cases, \u0026ldquo;policymakers and stakeholders should invest in financial technology solutions that enable real-time monitoring of risks\u0026rdquo; (Ogundele \u0026amp; Nzama, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e, p.12).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe study submit that although the adoption of AI, ML, IoT, blockchain, and cloud computing and many other new emerging technologies is gaining tract among HEIs, the diffusion and adoption faces challenges of integration and unwillingness to training for new systems. Factors such as lack of integration of systems, resistance to change and the disjointed regulatory environment led to slow adoption and to a proliferation of much more aggressive and evolved threats in HEIs. Thus, there is a need to incorporate and integrate emerging technologies with traditional cybersecurity strategies. Thus, a proactive approach is essential for ensuring new technologies help build the resilience against evolved threats. However, there is also a need to consider intervening factors when integrating new systems with those that exist in the system. The integration of emerging technologies, such as cloud services, into the processes of mitigating threats need an integrated policy environment, which in some institutions does not even exist. HEIs need extend the application established in traditional frameworks to accommodate new infrastructure. The authors believes that by integrating and establishing a centralised framework for governance and incorporating new technologies to existing cybersecurity controls will address existing challenges of technology adoption in HEIs.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their sincere gratitude to all individuals who contributed directly or indirectly to this study. Special appreciation is extended to the supervisor for their unwavering guidance, constructive feedback, and continuous support throughout the research process. Their mentorship played a vital role in shaping both the study and the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLebohang Bosiu wrote the manuscript and Lethiwe Nzama-Sithole supervised the research project. Her contribution has been invaluable in reviewing this paper and assisting me with the research\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data applied for this study is stored at the university\u0026rsquo;s cloud system and can be provided anytime upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe, the authors of this manuscript, give consent for the publication of this paper in the Discover Education Journal\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDisclaimer/Publisher\u0026rsquo;s Note:\u003c/strong\u003e The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbbadi, D., \u0026amp; Lachkar, A. (2025). Cybersecurity Challenges and the Protection of the Arabic Language in the Age of Artificial Intelligence: \u003cem\u003eDigital Security and Safeguarding Methods\u003c/em\u003e. doi:10.20944/preprints202503.0609.v1 \u003c/li\u003e\n\u003cli\u003eAboelnor, M., \u0026amp; Sobaih, A. E. (2023). A Quadruple \u0026ldquo;E\u0026rdquo; approach for effective cyber-hygiene behaviour and attitude toward online learning among higher-education students in Saudi Arabia amid COVID-19 pandemic. \u003cem\u003eElectronics\u003c/em\u003e, \u003cem\u003e1-17\u003c/em\u003e. doi:10.3390/electronics12102268\u003c/li\u003e\n\u003cli\u003eAkor, S. O., Nongo, C., Udofot, C. \u0026amp; Oladokun, B. D. (2024). 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Use of Boolean operators for accessing the databases of university of technology libraries by postgraduate students in South-East, Nigeria. Journal of Library Services and Technologies, 5(2), 24-35. doi:10.47524/llst.v5i2.25\u003c/li\u003e\n\u003cli\u003eUlven, J. B., \u0026amp; Wangen, G. (2021). A systematic review of cybersecurity risks in higher education. Future Internet, 13(0), 1-40. doi:10.3390/fi13020039 \u003c/li\u003e\n\u003cli\u003eUnited Nations Office on Drugs and Crime. (2024). Transnational organized crime and the convergence of cyber-enabled fraud, underground banking and technological innovation in Southeast Asia: A shifting threat landscape. UNODC. https://www.unodc.org/ \u003c/li\u003e\n\u003cli\u003eZoellner, J. M., \u0026amp; Porter, K. J. (2017). Translational research: Concepts and methods in dissemination and implementation research. In A. M. Coulston, C. J. Boushey, M. G. Ferruzzi, \u0026amp; L.M. 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Cambridge: Academic Press. doi:10.1016/B978-0-12-802928-2.00006-0.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"cybersecurity, higher education institutions, emerging technology, blockchain, artificial intelligence, machine learning","lastPublishedDoi":"10.21203/rs.3.rs-7343340/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7343340/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe advent of fourth industrial revolution (4IR) has brought with it a myriad of advanced technologies, which have simultaneously given rise to new, technologically sophisticated threats for Higher Education Institutions (HEIs). This meant that HEIs had to adopt new cybersecurity strategies incorporating technologies to counter new threats. However, it is not clear to what extent HEIs have adopted and integrated advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML) into their traditional cybersecurity strategies for mitigating evolving threats within the HEI context. The study sought to explore current adoption of emerging technologies to enhance cybersecurity posture in HEIs. The study also sought to determine the effectiveness of the new emerging technologies in mitigating evolving threats in HEIs. A Systematic Literature Review (SLR) was used to qualitatively examine literature on emerging technologies in HEIs. Guided by the PRISMA framework, the selection process focused on relevant literature from selected databases. A total of 287 studies were retrieved and assessed for eligibility, with 23 studies ultimately included to explore the emerging technologies employed by HEIs to mitigate technological threats. From a thematic analysis of data, findings showed that HEIs have adopted and integrated new technologies such as AI, ML, and cloud services. However, the diffusion and adoption of these technologies face challenges related to system integration and resistance or unwillingness to undergo training for new systems. Factors such as lack of integration of systems, resistance to change and the disjointed regulatory environment lead to slow adoption and lead to a proliferation of much more aggressive and evolved threats in HEIs. There was also an urgent need for training and cybersecurity awareness campaigns to build cybersecurity culture around emerging technologies. Thus, establishing a centralised framework for governance incorporating new technologies to existing cybersecurity controls will address existing challenges of technology adoption in HEIs.\u003c/p\u003e","manuscriptTitle":"New technology-empowered cybersecurity controls in higher education institutions: a systematic review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-03 21:22:18","doi":"10.21203/rs.3.rs-7343340/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-14T05:39:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-04T16:12:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-30T09:02:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29779399579784342020689314906404791154","date":"2025-12-30T06:25:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-28T19:47:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318144987255730122548032029460373508591","date":"2025-12-25T21:52:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-24T13:50:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-23T20:47:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"312582823693888759435306059794499987856","date":"2025-12-23T20:35:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149401094796106282910427209962729177793","date":"2025-12-23T15:50:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176102264431285976299712195896571911419","date":"2025-12-02T07:08:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-01T19:09:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-29T09:13:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-27T06:37:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Education","date":"2025-10-27T06:33:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5b4d84e6-5855-497f-bbb6-d0da53d1e3c8","owner":[],"postedDate":"December 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-18T11:40:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-03 21:22:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7343340","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7343340","identity":"rs-7343340","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}