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Çakır, Ahmet Mert. "AI Driven Cybersecurity." Human Computer Interaction 8, no. 1 (2024): 119. https://doi.org/10.62802/jg7gge06.
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The advent of Artificial Intelligence (AI) has revolutionized the field of cybersecurity by introducing advanced mechanisms for detecting, preventing, and mitigating cyber threats. This research explores the intersection of AI and cybersecurity, highlighting the transformative potential of AI-driven solutions in combating increasingly sophisticated cyberattacks. By leveraging machine learning, deep learning, and neural network algorithms, AI enhances real-time threat detection, predictive analytics, and anomaly detection across diverse digital infrastructures. This study evaluates current AI-driven cybersecurity frameworks, emphasizing their efficacy in handling dynamic threat landscapes and addressing the limitations of traditional methods. Additionally, it examines ethical considerations, such as the potential misuse of AI by malicious actors and the need for transparent AI systems. Through comprehensive analysis, this research underscores the importance of developing resilient AI models to secure critical data and infrastructure in an era of rapidly evolving cyber risks. The findings provide actionable insights for policymakers, organizations, and technology developers, advocating for collaborative efforts to harness AI’s potential while addressing its inherent challenges.
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Kim, Geunhye, and Kyudong Park. "Effect of AI." Tehnički glasnik 18, no. 1 (2024): 29–36. http://dx.doi.org/10.31803/tg-20230218142012.
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Abstract: Artificial intelligence (AI) is considered a vital factor that will fundamentally alter the cybersecurity environment. AI technology is progressing much faster than expected, and AI-based security services are being introduced into the global security market on a daily basis. However, how AI can contribute to the cybersecurity field and what changes it will bring remain unknown. Nonetheless, cybersecurity is not merely a technical issue but also a process for dealing with regulations, policies, and security risks; therefore, the introduction of AI technology introduction can make a fundamental difference in cybersecurity policy as a whole. This study primarily aims to better understand the concept and characteristics of AI from the cybersecurity perspective and identify its future implications on cybersecurity environment at the national policy level. This study predicts what modifications will be made to national cybersecurity strategies (NCSS) when machine learning (ML) is introduced and implemented. It also provides a basic policy recommendation that offers potential responses to these changes. The study first describes the emergence of AI in the cybersecurity field and explains AI-ML technical services and AI security policy elements. Second, through NCSS material analysis, this study categorizes NCSS into 11 categories and selects the critical functions of each dimension. Finally, it predicts the changes that will occur when AI is introduced within the selected NCSS category. It also introduces the priorities and considerations required for these changes.
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Puthal, Deepak, and Saraju Mohanty. "Cybersecurity Issues in AI." IEEE Consumer Electronics Magazine 10, no. 4 (2021): 33–35. http://dx.doi.org/10.1109/mce.2021.3066828.
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Samara Simha Reddy Beesam and Suchitha Reddy Aeniga. "Bridging AI and Cybersecurity." International Journal of Science and Research Archive 14, no. 2 (2025): 1179–85. https://doi.org/10.30574/ijsra.2025.14.2.0382.
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This has led to an entirely new era of quicker-than-extremely-quick firm AI will probably come, which in turn looms every great improvement in cybersecurity practice. Built into a security system: AI not only means that massive filing cabinets can be replaced by microfiche readers which are good enough for government work--it also adds incredible capabilities to the stored data itself. This means that although the quantities of data may soar a million billion-fold in any single category at a given point nowadays all those same numbers still do not cause a corresponding increase in different types of information. Em sophisticated Detection Mechanisms are an advanced alternative to traditional security measures, and so is the provision of automated response systems. Whilst machine learning algorithms excel at identifying subtle Threat patterns and potential vulnerabilities before exploit,ation, the addition of AI to security has a two-edged sword effect. The time taken to develop a basic attack vector is now practically non-existent, and attackers are increasingly using AI techniques to produce more subtle and complex threats. This has sparked an unprecedented technological arms race between defensive AI systems and AI-powered cyber threats, pushing organizations to adopt continuous means. They still need a field as complex as this one has now become. This means getting AI researchers, cybersecurity people, and regulators together, drawing up arrangements that will keep nations secure in every detail of security because they permit more freedom than rules ever could desire to allow if not carefully approach what we mean by security in terms pass out they'll use this power only extend scope without lawful constraints It provides a challenge of precisely how we might carry forward workable resolutions between autonomous systems born of Ai and human checks that are critically essential but which cannot be implemented too much computer-style in security practice. To be effective, it must also be ethically compliant Down the line, AI technology continues to advance. As the security landscape changes so rapidly, cyber defenders will face tough challenges in developing suitable measures. We can win in this changing digital arena only if we have cutting-edge technologies and human-type abilities.
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Rios-Campos, Carlos, Sonia Carmina Venegas Paz, Gonzalo Orozco Vilema, et al. "Cybersecurity and artificial intelligence (AI)." South Florida Journal of Development 5, no. 8 (2024): e4276. http://dx.doi.org/10.46932/sfjdv5n8-021.
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The general objective of the research was to determine the advances related to the cybersecurity and artificial intelligence (AI). The specific objectives of the research are to identify the countries that invest the most in cybersecurity and the most prominent organizations in cybersecurity worldwide. Methodology, in this research, 37 documents have been selected, carried out in the period 2018 – 2024; including: scientific articles, review articles and information from websites of recognized organizations. Results, AI and cybersecurity are two very important aspects today, so it is necessary to study them in depth; cybersecurity is a very important issue for governments and organizations worldwide, which is why many efforts are made to successfully fight cyberattacks; artificial intelligence is being used in various fields of human activity, so it is necessary to evaluate its present and future impact; artificial intelligence has an important impact on cybersecurity, which is why various authors focus on studying their interrelationship. Conclusions, about the general objective of the research, to determine the advances related to the cybersecurity and artificial intelligence (AI). Advances in cryptographic and Artificial Intelligence (AI) techniques, advanced AI methods, data representation, adoption of AI-based cybersecurity, biometric authentication, advanced artificial intelligence (AI), and machine learning (ML), Big Data Analytics, an in-depth learning algorithm for training a neural network for detecting suspicious user activities. About the first specific objective of the research, to identify the countries that invest the most in cybersecurity. The 3 countries that invest the most in cybersecurity are: United States, China and United Kingdom. The 3 countries where organizations worldwide that have made adequate cybersecurity investments according to board members as of June 2023 are: Singapore, Brazil and Australia. About the second specific objective, the most prominent organizations in cybersecurity worldwide. Palo Alto Networks, Fortinet and Crowdstrike are the most important companies in cybersecurity worldwide 2022, by market capitalization Apr 4, 2024.
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Shahid, Shahabuddin. "Vulnerabilities of AI in Cybersecurity." International Journal of Scientific Engineering and Research 13, no. 6 (2025): 31–32. https://doi.org/10.70729/se25604130052.
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Abel Uzoka, Emmanuel Cadet, and Pascal Ugochukwu Ojukwu. "Applying artificial intelligence in Cybersecurity to enhance threat detection, response, and risk management." Computer Science & IT Research Journal 5, no. 10 (2024): 2511–38. http://dx.doi.org/10.51594/csitrj.v5i10.1677.
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This paper explores the application of Artificial Intelligence (AI) in cybersecurity, emphasizing its potential to enhance threat detection, response, and risk management. The study's primary objective is to analyze how AI-driven tools and techniques can improve the efficiency and effectiveness of cybersecurity measures in organizations. Employing a comprehensive literature review and case study analysis, the research investigates current AI applications in threat detection, including machine learning algorithms, anomaly detection systems, and predictive analytics. The findings reveal that AI significantly reduces response times to cyber threats, increases accuracy in identifying vulnerabilities, and enables more proactive risk management strategies. The paper also examines the strategic implications of integrating AI into cybersecurity frameworks, highlighting the challenges related to data privacy, ethical considerations, and the need for skilled personnel to manage AI systems. Furthermore, it discusses the future prospects for AI in cybersecurity, suggesting that as AI technologies evolve, they will likely play an even more critical role in defending against sophisticated cyber-attacks. The paper concludes by providing recommendations for organizations to effectively integrate AI into their cybersecurity strategies, ensuring they remain resilient in the face of evolving cyber threats. This study contributes to the ongoing discourse on AI in cybersecurity by offering insights into its strategic applications and laying the groundwork for future research in this rapidly developing field. Keywords: Artificial Intelligence (AI), Cybersecurity, Threat Detection, AI Governance, Model Training, Data Privacy, Bias in AI, AI Research, Continuous Learning, Cybersecurity Strategy, AI Ethics, Machine Learning, Anomaly Detection, AI Scalability, AI in Cybersecurity.
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Rathod, Sachin Dashrath. "AI In Enhancing Cyber Security." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04063.
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Abstract:- Artificial intelligence( AI) is a important technology that helps cybersecurity armies automate repetitive tasks, accelerate trouble discovery and response, and meliorate the delicacy of their conduct to strengthen the security posture against various security issues and cyberattacks. This composition presents a regular literature review and a detailed analysis of AI use cases for cybersecurity provisioning. The review reacted in 2395 studies, of which 236 were linked as primary. This composition classifies the linked AI use cases predicated on a NIST cybersecurity frame using a thematic analysis approach. This type frame will give albums with a comprehensive overview of the eventuality of AI to meliorate cybersecurity in different surrounds. The review also identifies future disquisition openings in arising cybersecurity operation areas, advanced AI styles, data for representation, and the development of new infrastructures for the successful handover of AI- predicated cybersecurity in moment's period of digital transformation.
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Student, Speaker: Isaik Agudelo Watstein, and Advisor: Leon Guillermo Marin Ortega Faculty. "CYBERSECURITY WITH A SOCIAL VISION PROTECTING RIGHTS IN THE DIGITAL AGE." International Journal of Engineering Technology Research & Management (IJETRM) 09, no. 05 (2025): 302–9. https://doi.org/10.5281/zenodo.15468798.
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Nowadays, cybersecurity goes beyond computers and focuses on safeguarding people’s dignity and individualrights. Since we rely more on digital tools and AI, cybersecurity's social, ethical, and legal sides need to beconsidered with priority. This article addresses how cybersecurity ties in with the dignity of people and cites lawssuch as Law 1273 from 2009 and Law 1581 from 2012 in the context of Colombia. In addition, it analyzes theworldwide effects of the Budapest Treaty and compares them to international efforts like the CCPA in the UnitedStates. The study highlights that AI can assist in cybersecurity and also become a danger, and as a result,recommends strict ethical norms and fair laws for everyone. Lastly, the article outlines various strategies neededto strengthen online security and respect for people’s rights.
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Mohammed, Dr Farheen. "Comprehensible AI in Cyber Security: Bridging the Trust Gap." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47167.
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Abstract Artificial Intelligence (AI) has become a pivotal component of modern cybersecurity, supporting real-time threat detection, anomaly recognition, and automated incident response. Despite its capabilities, the black-box nature of many AI models—especially deep learning and complex machine learning algorithms—has introduced a significant trust gap between machine-generated decisions and human interpretation. Comprehensible AI has emerged as a key approach to bridging this divide by offering transparency, interpretability, and accountability within AI-driven cybersecurity systems. This paper examines the integration of AI in cybersecurity, focusing on how explainability enhances trust, strengthens threat intelligence, and supports regulatory compliance. Through a comprehensive literature review and methodological evaluation, the study investigates current challenges and recent advancements in the application of AI to cybersecurity. Findings demonstrate that incorporating explainable AI not only improves threat detection capabilities but also promotes effective collaboration between human analysts and AI systems. The paper concludes by outlining future directions for research aimed at improving model interpretability without sacrificing performance. Keywords Comprehensible AI, Cybersecurity, AI Transparency, AI Interpretability, AI-driven Threat Detection, Anomaly Detection, Machine Learning Security, Trustworthy AI
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Adhikari, Dipak, and Suman Thapaliya. "An Overview of AI Applications in Cybersecurity for IT Management." NPRC Journal of Multidisciplinary Research 1, no. 4 (2024): 121–33. http://dx.doi.org/10.3126/nprcjmr.v1i4.70951.
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Background: Cybersecurity serves as a defense mechanism against unauthorized access, theft, information disclosure, and service disruptions for electronic data, networks, and computer systems. Traditional cybersecurity strategies have struggled to counter increasingly sophisticated and automated cyberattacks, necessitating the exploration of advanced solutions such as artificial intelligence (AI). Aim: This research aims to highlight the role of AI technology and systems in enhancing cybersecurity measures, addressing how AI-based solutions can defend against cyber threats and identifying the associated challenges and future research directions. Methodology: A systematic literature review was conducted, focusing on recent advancements in AI applications for cybersecurity. Key databases and search engines were utilized to gather relevant articles, which were then filtered based on criteria such as language, publication quality, citation count, and accessibility. The collected literature was analyzed to extract insights on AI methods and their uses in cybersecurity. Results: The study found that AI offers several benefits in addressing cybersecurity concerns. AI systems can identify novel and complex cyber threats, handle substantial volumes of security data, and establish baselines for normal network behavior to detect deviations. Various AI techniques, including neural networks, expert systems, intelligent agents, and machine learning, were identified as effective tools in combating cyber threats. AI has demonstrated significant improvements in the detection and prevention of cyberattacks, enhancing overall cybersecurity effectiveness. Findings: While AI enhances cybersecurity capabilities, it also introduces new challenges such as the need for substantial data and resources, managing false alarms, and vulnerability to adversarial attacks. The study underscores the importance of continuous research to develop robust AI systems that can adapt to evolving cyber threats. Future research should focus on addressing the limitations of AI in cybersecurity, including adversarial threats, data integrity, and human-machine collaboration.
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Zaleppa, Paige, Siddharth Kaza, Blair Taylor, and Md Sajidul Islam Sajid. "Using AI Assistants in the Creation of an Academic Program of Study (PoS) in CyberAI." Journal of The Colloquium for Information Systems Security Education 12, no. 1 (2025): 6. https://doi.org/10.53735/cisse.v12i1.213.
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Artificial Intelligence (AI) is playing an increasingly vital role in cybersecurity. As AI becomes more prevalent, cybersecurity professionals need AI skills, and academic institutions need to provide students with the opportunities to gain them. To meet this demand, the NSA National Centers of Academic Excellence in Cybersecurity (NCAE-C) program, in collaboration with the National Science Foundation (NSF), launched an initiative to outline the AI content cybersecurity academic programs need to teach their students. The initiative aims to build knowledge units (KUs) and recommend a Program of Study (PoS) in Cybersecurity and Artificial Intelligence (Cyber AI). This paper outlines the development of an AI assistant that was used to collaborate on the KU creation process for the CyberAI PoS. We will discuss the methodology behind the integration of the AI assistant, evaluate its contributions, and explore future directions for using AI assistants to develop curricular guidelines for academic programs.
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Kumar, Dr Naveen. "Enhancing Transparency and Trust in Cybersecurity: Developing Explainable AI Models for Threat Detection." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49406.
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Abstract The increasing reliance on artificial intelligence (AI) in cybersecurity has significantly improved threat detection and response. However, many AI-driven Defense mechanisms function as "black boxes," making it difficult for security professionals to interpret their decisions. This lack of transparency reduces trust in AI systems and limits their adoption in critical security operations. Despite advancements in explainable AI (XAI), there is a significant research gap in applying XAI techniques specifically to cybersecurity. This study aims to bridge this gap by developing and evaluating explainable AI models for cybersecurity applications. The research employs a combination of interpretable machine learning algorithms, feature attribution methods, and human-in-the-loop approaches to enhance model transparency. Various cybersecurity datasets, including network intrusion detection and malware classification data, are used to assess the effectiveness of these models. Key findings indicate that incorporating explainability techniques improves user trust and facilitates better decision-making without compromising model performance. Additionally, the study highlights the trade-offs between explainability and predictive accuracy, offering insights into optimizing AI models for real-world cybersecurity applications. In conclusion, this research demonstrates that integrating explainable AI into cybersecurity frameworks enhances transparency and user confidence, leading to more effective threat mitigation. Future work will focus on refining these models and developing standardized evaluation metrics for explainability in AI-driven security systems. Keyword:- Artificial Intelligence (AI), Cybersecurity, Explainable AI (XAI), Threat Detection, and Model Transparency
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Michael, Katina, Roba Abbas, and George Roussos. "AI in Cybersecurity: The Paradox." IEEE Transactions on Technology and Society 4, no. 2 (2023): 104–9. http://dx.doi.org/10.1109/tts.2023.3280109.
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Cao, Xiyu. "The application of artificial intelligence in internet security." Applied and Computational Engineering 18, no. 1 (2023): 230–35. http://dx.doi.org/10.54254/2755-2721/18/20230995.
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The global integration of the internet has led to a significant increase in the importance of cybersecurity. Artificial Intelligence (AI) has emerged as a viable solution for enhancing cybersecurity measures. AI has the potential to improve the speed and effectiveness of detecting and responding to cyber threats. This study explores the intersection of network security and AI, with a focus on the various ways in which AI can be used to enhance network security, such as intrusion detection, malware detection, and behavioural analytics. The study also examines the potential risks associated with AI in cybersecurity, including the possibility of AI being utilized for cyber-attacks. Additionally, the study discusses the challenges associated with implementing AI for network security, such as the lack of available large datasets for AI training, network infrastructure complexity, and the requirement for skilled AI professionals in cybersecurity. Ethical considerations arising from the use of AI in network security are also addressed. The study emphasizes the need for a balanced approach towards integrating AI into cybersecurity measures, taking into account potential benefits and challenges.
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Nejood Abedyasir ibadi. "Innovative Strategies for Enhancing Cybersecurity in Information Systems: A Holistic Approach in Computer Engineering." Journal of Information Systems Engineering and Management 10, no. 35s (2025): 826–39. https://doi.org/10.52783/jisem.v10i35s.6151.
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The compounded nature of cyber threats, such as ransomware, phishing, and supply chain attacks, has revealed the inadequacy of conventional security controls. AI and machine learning-based solutions offer promising improvements in near-instant threat detection and neutralization, responding to the increasing demand for adaptive cybersecurity measures.This research assesses AI-based cybersecurity models, derives security insights from historical cyber-attacks, measures the effectiveness of regulatory compliance frameworks, and formulates a multi-layered AI-based security strategy. The research is centered on AI as a means of improving cybersecurity, vulnerabilities revealed by historical cyber-attacks, and blending AI-based threat detection with compliance.A mixed-methods research design is used, including case study analysis, expert interviews, surveys, and machine learning model assessments. Case studies of significant cyber-attacks identify vulnerabilities and mitigation measures. Machine learning models are tested on the UNSW-NB15 dataset to determine their performance in identifying cyber threats, and surveys offer information on AI adoption in cybersecurity. The research concludes that AI-based models are much more effective than conventional security measures, with Random Forest and XGBoost delivering more than 95% accuracy in detecting cyber threats. Expert interviews reveal that 90% of cybersecurity experts support AI-based intrusion detection, but only 31% of companies have deployed it. Compliance frameworks like NIST's Risk Management Framework and Zero Trust models offer systematic security solutions but lack real-time AI-based integration.This study illustrates how AI-powered models provide higher accuracy in detecting cyber threats, addressing significant cybersecurity loopholes. It points to the disparity between AI potential and organizational adoption while underlining the importance of integrating AI-powered security with compliance frameworks for a more responsive cybersecurity framework.The results highlight the need for AI-based cybersecurity tools that integrate real-time threat identification, automated protection, and compliance support. Companies need to step up AI implementation, cybersecurity awareness training, and regulation integration to build robust defenses against emerging threats.
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Sasikala, D., and K. Venkatesh Sharma. "Deployment of Artificial Intelligence with Bootstrapped Meta-Learning in Cyber Security." Journal of Trends in Computer Science and Smart Technology 4, no. 3 (2022): 139–52. http://dx.doi.org/10.36548/jtcsst.2022.3.003.
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Cybersecurity is an extensive and vivacious domain in the commercial progression of the ecosphere. By up-to-date inhabitants, networking settings and assets, cybersecurity fits with the exigent task to realize the necessities of the imminent populace. Intelligent cybersecurity / intellectual smart cybersecurity has risen as a pioneering tool to deal with latest ambiguities in programmed cybersecurity enduring capability by bringing together Artificial Intelligence (AI) in Cybersecurity Computerization. The mechanism that enterprises in this cutting-edge technology handles the mechanism capability to acquire via depleting Bootstrapped Meta-learning and reinforced with rewards as Supreme Cybersecurity vintages, besides least resource utilizations as well as time limits. AI empowered cybersecurity technology is a vital constituent of the imminent cybersecurity revolution ahead. During this operation, a proficient computerization of AI application in the arena of cybersecurity sustenance is ready for attaining the supreme output welfares as results, also inhibiting the real assets. Setting the precise real-time issues are trailed by cracking it for affluence and escalation or magnification of cybersecurity thus by prominent universal preeminent impending cybersecurity. A meta-learning/AI-based automated security strategy is vital in the protection of critical infrastructure, users and assets disinclined to outbreaks.
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Donepudi, Praveen Kumar. "Crossing Point of Artificial Intelligence in Cybersecurity." American Journal of Trade and Policy 2, no. 3 (2015): 121–28. http://dx.doi.org/10.18034/ajtp.v2i3.493.
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There is a wide scope of interdisciplinary crossing points between Artificial Intelligence (AI) and Cybersecurity. On one hand, AI advancements, for example, deep learning, can be introduced into cybersecurity to develop smart models for executing malware classification and intrusion detection and threatening intelligent detecting. Then again, AI models will confront different cyber threats, which will affect their sample, learning, and decision making. Along these lines, AI models need specific cybersecurity defense and assurance advances to battle ill-disposed machine learning, preserve protection in AI, secure united learning, and so forth. Because of the above two angles, we audit the crossing point of AI and Cybersecurity. To begin with, we sum up existing research methodologies regarding fighting cyber threats utilizing artificial intelligence, including receiving customary AI techniques and existing deep learning solutions. At that point, we analyze the counterattacks from which AI itself may endure, divide their qualities, and characterize the relating protection techniques. And finally, from the aspects of developing encrypted neural networks and understanding safe deep learning, we expand the current analysis on the most proficient method to develop a secure AI framework. This paper centers mainly around a central question: "By what means can artificial intelligence applications be utilized to upgrade cybersecurity?" From this question rises the accompanying set of sub-questions: What is the idea of artificial intelligence and what are its fields? What are the main areas of artificial intelligence that can uphold cybersecurity? What is the idea of data mining and how might it be utilized to upgrade cybersecurity? Hence, this paper is planned to reveal insight into the idea of artificial intelligence and its fields, and how it can profit by applications of AI brainpower to upgrade and improve cybersecurity. Using an analytical distinct approach of past writing on the matter, the significance of the need to utilize AI strategies to improve cybersecurity was featured and the main fields of application of artificial intelligence that upgrade cybersecurity, for example, machine learning, data mining, deep learning, and expert systems.
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Mallikarjuna, Paramesha, Liladhar Rane Nitin, and Rane Jayesh. "Artificial Intelligence, Machine Learning, and Deep Learning for Cybersecurity Solutions: A Review of Emerging Technologies and Applications." Partners Universal Multidisciplinary Research Journal (PUMRJ) 01, no. 02 (2024): 84–109. https://doi.org/10.5281/zenodo.12827076.
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The increasing intricacy and advancement of online dangers have required the creation of more advanced cybersecurity methods, with artificial intelligence (AI) becoming a crucial asset in this area. This document offers an in-depth overview of the most recent developments and upcoming technologies in AI-powered cybersecurity solutions, including machine learning (ML), deep learning (DL), natural language processing (NLP), and reinforcement learning (RL). Various aspects of cybersecurity utilize these AI technologies, including threat detection, response, network security, and data protection. The research carefully examines new studies to pinpoint main developments and uses, emphasizing the growing dependence on AI to tackle various cybersecurity issues. An examination of keyword co-occurrence uncovers the primary topics and connections in AI-driven cybersecurity studies, while a cluster analysis organizes these topics into separate subcategories, offering a systematic look at the research field. The results highlight the important contribution of AI in improving cybersecurity measures and provide useful guidance for future research. The integration of AI technologies is predicted to enhance security measures and drive innovation in diverse domains as cyber threats keep evolving.
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Muhammad Asif Ibrahim. "Incorporating the Future: Optimizing Cybersecurity through Seamless Integration of Artificial Intelligence." International Journal for Electronic Crime Investigation 7, no. 4 (2024): 51–60. http://dx.doi.org/10.54692/ijeci.2023.0704166.
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Cyber-attacks are becoming more sophisticated and common in today's environment. Artificial intelligence (AI) is being used by enterprises to boost their defenses against these developing threats. AI is rapidly altering the cybersecurity field, providing several benefits in terms of improving security measures. However, its implementation causes significant changes in cybersecurity occupations and necessitates the acquisition of new skills by specialists. This article investigates the impact of AI on cybersecurity employment, presents real-world instances of AI integration in the sector, analyzes the future of AI in cybersecurity, and identifies the problems nvolved with its adoption.
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Asere, Gbenga Femi, Kehinde Adetayo Nuga, and Madu Medugu. "The Role of Artificial Intelligence in Cybersecurity: Understanding the Dynamics, Impacts, and Remediations." Journal of Computer, Software, and Program 2, no. 1 (2025): 1–9. https://doi.org/10.69739/jcsp.v2i1.120.
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This study explores the transformative role of Artificial Intelligence (AI) in enhancing cybersecurity measures. The integration of AI into cybersecurity frameworks offers significant advancements in threat detection, prevention, and response. Leveraging machine learning algorithms and sophisticated data analytics, AI systems can analyze large datasets in real-time to identify patterns and anomalies that indicate potential security threats. This capability allows for the early detection of cyber threats that traditional security measures might miss. AI also improves threat intelligence by learning from new data and evolving attack methodologies, enhancing predictive accuracy. The research highlights how AI-driven automation can expedite incident response, thereby reducing the damage and costs associated with security breaches. Additionally, AI strengthens authentication processes through behavioral biometrics and anomaly detection, offering robust protection against identity theft and fraud. However, the study also addresses the challenges posed by AI in cybersecurity, including the potential for adversaries to use AI for developing sophisticated attacks and the ethical concerns surrounding AI algorithms’ biases and transparency. The research argues for a balanced approach that maximizes AI’s benefits while mitigating its risks. Ensuring transparency, accountability, and continuous improvement of AI models is critical for maintaining trust and efficacy in AI-powered cybersecurity solutions. This research concludes that while AI significantly enhances cybersecurity capabilities, addressing its inherent challenges is essential for its successful and ethical application in the cybersecurity domain.
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Lee, Sangsoo. "AI-Based CYBERSECURITY: Benefits and Limitations." J-Institute 6, no. 1 (2021): 18–28. http://dx.doi.org/10.22471/ai.2021.6.1.18.
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Elamin, Mustafa Osman I., and Osman M. O. Ismaiel. "The AI Revolution in Cybersecurity: Transforming Threat Detection, Defense Mechanisms, and Risk Management in the Digital Era." International Journal of Religion 6, no. 1 (2025): 228–46. https://doi.org/10.61707/1dmvn671.
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The rapid integration of Artificial Intelligence (AI) in cybersecurity has redefined traditional security protocols, providing advanced mechanisms to combat increasingly sophisticated cyber threats. This article investigates the transformative role of AI in enhancing cybersecurity by focusing on three core areas: threat detection, defence mechanisms, and risk management. The primary aim is to assess how AI technologies—specifically machine learning, deep learning, and natural language processing—can improve the detection, prevention, and mitigation of cyber threats beyond traditional methods. By leveraging AI-driven solutions, cybersecurity can anticipate emerging threats, quickly adapt defensive strategies, and significantly reduce response times. To achieve this, the study will analyse recent advances in AI applications within cybersecurity, using a systematic literature review and case study analysis. The literature review will highlight existing knowledge gaps, explore the current limitations of conventional cybersecurity measures, and identify how AI fills these gaps. Case studies of real-world AI deployment in cybersecurity will be critically examined to understand the practical effectiveness and challenges associated with these technologies. The expected results include an in-depth understanding of AI's specific advantages in threat detection accuracy, predictive analysis, and anomaly identification. Furthermore, we anticipate uncovering key limitations and ethical considerations, such as data privacy issues, potential biases in AI algorithms, and the risk of adversarial attacks exploiting AI vulnerabilities. By examining these aspects, the article aims to present a balanced perspective on the future of AI in cybersecurity, suggesting critical improvements and policy recommendations.
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Researcher. "AI IN CYBERSECURITY: ADVANCEMENTS AND CHALLENGES." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 992–1001. https://doi.org/10.5281/zenodo.13999861.
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This article explores the transformative role of artificial intelligence (AI) in cybersecurity, highlighting its potential to revolutionize threat detection and mitigation in an increasingly sophisticated cyber threats era. It examines the rapid growth of AI adoption in the cybersecurity sector, driven by the rising costs of data breaches and the limitations of traditional security measures. The article delves into advanced AI-powered capabilities such as anomaly detection, malware identification, phishing prevention, behavioral analytics, and threat intelligence integration. While acknowledging AI's promising future in cybersecurity, including predictive threat intelligence and automated incident response, the article also addresses critical challenges such as data bias, explainability issues, adversarial attacks, ethical considerations, and the cybersecurity skills gap. By presenting both the advancements and obstacles in AI-driven cybersecurity, this study provides a comprehensive overview of this rapidly evolving field's current landscape and future trajectory.
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Chigozie Kingsley Ejeofobiri, Adedoyin Adetumininu Fadare, Olalekan Olorunfemi Fagbo, Valerie Ojinika Ejiofor, and Adetutu Temitope Fabusoro. "The role of Artificial Intelligence in enhancing cybersecurity: A comprehensive review of threat detection, response, and prevention techniques." International Journal of Science and Research Archive 13, no. 2 (2024): 310–16. http://dx.doi.org/10.30574/ijsra.2024.13.2.2161.
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As cyber threats continue to grow in scale and sophistication, traditional cybersecurity solutions have become increasingly insufficient to mitigate evolving risks. Artificial Intelligence (AI) has emerged as a powerful tool for enhancing cybersecurity by improving threat detection, automating response mechanisms, and preventing attacks before they occur. This review explores the intersection of AI and cybersecurity, focusing on AI-driven techniques in threat detection, automated response systems, and preventive measures. Furthermore, the paper discusses the challenges of deploying AI in cybersecurity, including adversarial attacks and ethical considerations, and provides future directions for research.
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Marzoog Al-Mukhtar, Wijdan Noaman. "AI in Cybersecurity: Transformative Approaches to Safeguarding Information Technology Systems." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 15, no. 3 (2024): 391–412. https://doi.org/10.61841/turcomat.v15i3.14945.
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AI in cybersecurity has significantly changed how businesses secure their IT systems against increasingly sophisticated and ever-evolving cyberattacks. AI solutions that leverage machine learning, deep learning, and data analytics analyze patterns in threat behavior to identify and predict threats, enabling immediate responses that help organizations stay ahead of emerging threats. However, this revolutionary approach also introduces several challenges related to ethics and technology, including security vulnerabilities, data quality issues, bias in AI models, and questions of responsibility and privacy. As AI continues to progress, innovations such as behavioral biometrics, quantum computing, and autonomous security systems could become viable means of strengthening future cyber defenses. This paper discusses the current application of artificial intelligence in cybersecurity, reports on the challenges faced by AI systems, and outlines potential future developments that could revolutionize cybersecurity policies. It aims to raise awareness among practitioners and scholars about the importance of AI technologies in cybersecurity, providing a comprehensive analysis of AI-driven cybersecurity solutions.
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Sesham, Kartik and K Muddu Swamy. "THE ROLE OF ARTIFICIAL INTELLIGENCE IN ENHANCING E-GOVERNANCE AND CYBERSECURITY IN SMART CITIES: INSIGHTS FROM STAKEHOLDERS." International Journal of Engineering Research and Science & Technology 21, no. 2 (2025): 589–98. https://doi.org/10.62643/ijerst.2025.v21.i2.pp589-598.
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One crucial technology of the Fourth Industrial Revolution (Industry 4.0) is artificial intelligence (AI), which guards computer network systems against viruses, phishing, cyberattacks, damage, and unauthorised access. Through e-Government, AI has the ability to improve the cyber capabilities and security of nationstates, local governments, and non-state organisations. According to current research, there is a mixed link between cybersecurity, e-Government, and AI; however, this relationship is thought to be context-specific. Numerous stakeholders with diverse backgrounds and specialities in AI, e-Government, and cybersecurity impact and are influenced by these fields. This research examines the close connection between cybersecurity, e-Government, and AI in order to close this context-specific gap. Additionally, this research looks at how e-Government mediates the link between AI and cybersecurity as well as how stakeholder participation modifies that relationship. According to the findings of the PLS-SEM path modelling investigation, e-Government somewhat mediates the relationship between cybersecurity and artificial intelligence. Similar findings were made about the moderating effect of stakeholder participation on the link between cybersecurity and e-Government and AI. Since all stakeholders have an interest in a dynamic, open, and safe cyberspace while using e-services, it follows that stakeholder participation is crucial to AI and e-Government. This paper offers useful recommendations for enhancing cybersecurity measures for smart city government agencies.
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Çaylı, Osman. "AI-Enhanced Cybersecurity Vulnerability-Based Prevention, Defense, and Mitigation using Generative AI." Orclever Proceedings of Research and Development 5, no. 1 (2024): 655–67. https://doi.org/10.56038/oprd.v5i1.616.
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The rapid evolution of cyberattacks, driven by increasingly sophisticated techniques and the proliferation of readily available AI tools, presents significant challenges for organizations worldwide. Traditional cybersecurity approaches often prove insufficient in addressing the speed, adaptability, and complexity of modern threats. The VULTURE project directly tackles these challenges by proposing a revolutionary AI-powered cybersecurity platform that leverages the capabilities of generative AI (GenAI) and large language models (LLMs) to enhance vulnerability prediction, automate penetration testing, improve intrusion detection, and enable advanced cyber-physical risk profiling. This paper will examine VULTURE's architecture, key technological innovations, anticipated impact, and future research directions. The increasing sophistication and frequency of cyberattacks underscore the urgent need for innovative and adaptable cybersecurity solutions. Traditional approaches, often based on static rules and signature-based detection, struggle to keep pace with rapidly evolving threats, particularly the emergence of AI-driven attacks that can bypass conventional defenses and exploit previously unknown vulnerabilities (zero-day exploits). The shortage of skilled cybersecurity professionals further exacerbates these challenges, limiting organizations' ability to effectively respond to emerging threats. The VULTURE project proposes a novel approach to cybersecurity leveraging the power of Large Language Models (LLMs). This paper explores the technical innovations presented in the VULTURE proposal, focusing on the application of LLMs for vulnerability prediction and automated penetration testing. We analyze the proposed methodologies and discuss their potential impact, highlighting opportunities and challenges. Further research is necessary to validate the efficacy and scalability of the proposed methods.
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Taylor, Rodriguez Vance. "Examination of Applications of Artificial Intelligence in Cybersecurity: Strengthening National Defense with AI." International Journal of Computer Science and Information Technology Research 11, no. 3 (2023): 77–90. https://doi.org/10.5281/zenodo.8210374.
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<strong>Abstract:</strong> The rapid advancement of technology has led to an increasing dependence on digital infrastructure for critical national defense systems. However, this dependence also exposes these systems to sophisticated cyber threats, necessitating robust cybersecurity measures. Artificial Intelligence (AI) has emerged as a promising solution for enhancing cybersecurity in the realm of national defense. This research paper explores the application of AI in cybersecurity for national defense, aiming to provide an overview of the current landscape, challenges, and potential opportunities. It discusses the potential of machine learning algorithms to detect and prevent emerging cyber threats in real-time, as well as the use of natural language processing techniques to analyze vast amounts of security logs and identify patterns indicative of malicious activities. Moreover, the research paper examines the limitations and ethical considerations associated with the integration of AI in national defense cybersecurity. It addresses concerns regarding data privacy, bias in AI algorithms, and the potential for adversarial attacks targeting AI-powered systems. To provide a comprehensive analysis, case studies and real-world examples where AI have been successfully applied to strengthen cybersecurity in national defense contexts are explored. These examples illustrate how AI can improve threat detection, response time, and overall system resilience. Lastly, the paper concludes with an outlook on the future of AI in cybersecurity for national defense, discussing potential research directions and the importance of interdisciplinary collaboration between AI experts, cybersecurity specialists, and defense policymakers. In summary, this research paper sheds light on the significant role of AI in addressing cybersecurity challenges faced by national defense systems. It emphasizes the potential benefits, explores associated challenges, and highlights the need for responsible and ethical implementation. By leveraging AI technologies effectively, nations can strengthen their cybersecurity posture and safeguard critical national defense infrastructure in an increasingly interconnected and digitally-driven world. <strong>Keywords:</strong> Artificial Intelligence; Cybersecurity; International Policy, National Defense, National Security. <strong>Title:</strong> Examination of Applications of Artificial Intelligence in Cybersecurity: Strengthening National Defense with AI <strong>Author:</strong> Taylor Rodriguez Vance <strong>International Journal of Computer Science and Information Technology Research</strong> <strong>ISSN 2348-1196 (print), ISSN 2348-120X (online)</strong> <strong>Vol. 11, Issue 3, July 2023 - September 2023</strong> <strong>Page No: 77-90</strong> <strong>Research Publish Journals</strong> <strong>Website: www.researchpublish.com</strong> <strong>Published Date: 03-August-2023</strong> <strong>DOI: https://doi.org/10.5281/zenodo.8210374</strong> <strong>Paper Download Link (Source)</strong> <strong>https://www.researchpublish.com/papers/examination-of-applications-of-artificial-intelligence-in-cybersecurity-strengthening-national-defense-with-ai</strong>
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KUMAR, N. ARAVIND, and KADARLA NIHARIKA. "STAKEHOLDER PERSPECTIVES ON THE INFLUENCE OF ARTIFICIAL INTELLIGENCE IN E-GOVERNANCE AND CYBERSECURITY FOR SMART CITIES." International Journal of Engineering, Science and Advanced Technology 24, no. 10 (2024): 379–87. http://dx.doi.org/10.36893/ijesat.2024.v24i10.048.
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One of the most important technologies of the Fourth Industrial Revolution (Industry 4.0) is artificial intelligence (AI), which guards computer network systems against damage, phishing, malware, cyberattacks, and unauthorised access. Through eGovernment, artificial intelligence (AI) has the potential to improve the cyber capabilities and security of states, local governments, and non-state enterprises. Research currently available shows a mixed link between cybersecurity, e-Government, and AI; however, it is thought that this relationship depends on the situation. Different stakeholders with varying levels of knowledge and experience in their respective fields have an impact on and influence AI, e-Governance, and cybersecurity. This research explores the direct link between cybersecurity, eGovernment, and AI in order to close this context-specific gap. This research also looks at the moderating influence of stakeholder participation on the link between AI, e-Governance, and cybersecurity, as well as the mediating function that e-Governance plays in this relationship. PLS-SEM route modelling research findings showed that e-Government has a somewhat mediating effect between cybersecurity and AI. Similarly, the link between e-Governance and cybersecurity as well as AI and e-Governance was shown to be moderated by the engagement of stakeholders. Because all stakeholders have an interest in a thriving, transparent, and safe cyberspace while utilising e-services, it is implied that stakeholder participation in AI and e-Governance is crucial. This report offers smart city governments useful recommendations for bolstering their cybersecurity defences.
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Yakubova, Madinabonu. "The Legal Challenges of Regulating AI in Cybersecurity: A Comparative Analysis of Uzbekistan and Global Approaches." International Journal of Law and Policy 2, no. 7 (2024): 7–10. http://dx.doi.org/10.59022/ijlp.202.
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This paper delves into the complex legal landscape surrounding the regulation of artificial intelligence (AI) in cybersecurity, with a particular focus on Uzbekistan's regulatory framework in comparison to global approaches. As AI technologies become increasingly integral to cybersecurity systems, they present a host of unique legal and ethical concerns that traditional legislative models are ill-equipped to address. Through a comparative analysis methodology, this study evaluates Uzbekistan's current legal stance on AI in cybersecurity against international best practices, identifying critical gaps and proposing potential legislative solutions. The research reveals that while Uzbekistan has made significant strides in digital development, its legal framework lacks specific provisions for AI-driven cybersecurity measures, potentially leaving critical infrastructure vulnerable to emerging threats. The paper concludes by advocating for a balanced approach that fosters innovation while ensuring adequate safeguards against AI-related cybersecurity risks, positioning Uzbekistan as a potential leader in AI governance within Central Asia.
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Sheetal Temara. "Harnessing the power of artificial intelligence to enhance next-generation cybersecurity." World Journal of Advanced Research and Reviews 23, no. 2 (2024): 797–811. http://dx.doi.org/10.30574/wjarr.2024.23.2.2428.
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Cybersecurity ecosystem is an important facet in protecting sensitive information and securing critical infrastructure for countering modern cyber threats. With the increasing complexity and frequency of security incidents, there is an escalating demand for development of innovative solutions beyond current human capabilities pertaining to cybersecurity measures. Artificial Intelligence or AI can be utilized in a myriad of areas of cybersecurity. It emerged as a technological innovation to enhance cyber protection by facilitating faster and real-time threat detection for known and unknown threats, automating processes to minimize human error, and optimal decision-making. Harnessing the power of AI in cybersecurity creates formidable defense capabilities against the constantly changing cyber threats of future while empowering the cybersecurity personnel with threat intelligence and proactive foresight to safeguard critical assets and confidential information with unparalleled precision and effectiveness. This research paper aims to investigate the potential of AI-enabled cybersecurity systems and focuses on deducing the benefits of using AI in enhancing cybersecurity processes for organizations seeking to manage their risk profile. Through a comprehensive literature review, the wide-ranging applications of AI in cybersecurity have been analyzed such as intrusion detection, predictive simulation, and automated emergency response management. The study examines the benefits of implementing AI-based cyber defenses such as improved promptness and accuracy in vulnerability assessment and threat management, reduced false positives, and recognize patterns. The future potential of AI in cybersecurity will take a leap forward in expanding protection mechanisms to evaluate the strengths and weaknesses of attack vectors to prevent an adversarial attack.
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Sheetal, Temara. "Harnessing the power of artificial intelligence to enhance next-generation cybersecurity." World Journal of Advanced Research and Reviews 23, no. 2 (2024): 797–811. https://doi.org/10.5281/zenodo.14848303.
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Cybersecurity ecosystem is an important facet in protecting sensitive information and securing critical infrastructure for countering modern cyber threats. With the increasing complexity and frequency of security incidents, there is an escalating demand for development of innovative solutions beyond current human capabilities pertaining to cybersecurity measures. Artificial Intelligence or AI can be utilized in a myriad of areas of cybersecurity. It emerged as a technological innovation to enhance cyber protection by facilitating faster and real-time threat detection for known and unknown threats, automating processes to minimize human error, and optimal decision-making. Harnessing the power of AI in cybersecurity creates formidable defense capabilities against the constantly changing cyber threats of future while empowering the cybersecurity personnel with threat intelligence and proactive foresight to safeguard critical assets and confidential information with unparalleled precision and effectiveness. This research paper aims to investigate the potential of AI-enabled cybersecurity systems and focuses on deducing the benefits of using AI in enhancing cybersecurity processes for organizations seeking to manage their risk profile. Through a comprehensive literature review, the wide-ranging applications of AI in cybersecurity have been analyzed such as intrusion detection, predictive simulation, and automated emergency response management. The study examines the benefits of implementing AI-based cyber defenses such as improved promptness and accuracy in vulnerability assessment and threat management, reduced false positives, and recognize patterns. The future potential of AI in cybersecurity will take a leap forward in expanding protection mechanisms to evaluate the strengths and weaknesses of attack vectors to prevent an adversarial attack.
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Ademola, ojo Emmanuel, and K. Somorin. "AI and Cybersecurity in Investigative Journalism: A Literature Review." Advances in Multidisciplinary & Scientific Research Journal Publications 10, no. 1 (2024): 9–16. http://dx.doi.org/10.22624/aims/sij/v10n1p3.
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This literature review examines the intersection of artificial intelligence (AI) and cybersecurity in the context of investigative journalism. The use of AI technologies in investigative journalism has revolutionized the way journalists analyze data and uncover stories, while also posing new challenges in cybersecurity and the detection of deepfakes. The academic literature highlights the importance of training journalists in cybersecurity best practices, investing in AI technologies, and detecting and debunking deepfakes to safeguard the integrity of journalistic work. By integrating AI and cybersecurity measures into newsrooms, journalists can better protect themselves against cyber threats and combat misinformation, ensuring the continued relevance and credibility of journalism in the digital age. Keywords: Artificial Intelligence, Cybersecurity, Investigative Journalism, Deepfakes, Data Analysis, Misinformation, News Organizations, Cyber Threats, Journalism Integrity. Journal Reference Format: Ademola, O.E. & Somorin, K. (2024): AI and Cybersecurity in Investigative Journalism: A Literature Review. Social Informatics, Business, Politics, L:aw, Environmental Sciences & Technology Journal. Vol. 10, No. 1. Pp 9-16. www.isteams/socialinformaticsjournal. dx.doi.org/10.22624/AIMS/SIJ/V10N1P3
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Ademola, Ojo Emmanuel, and K. Somorin. "AI and Cybersecurity in Investigative Journalism: A Literature Review." Advances in Multidisciplinary & Scientific Research Journal Publications 10, no. 1 (2024): 9–18. http://dx.doi.org/10.22624/aims/sij/v10n1p2.
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This literature review examines the intersection of artificial intelligence (AI) and cybersecurity in the context of investigative journalism. The use of AI technologies in investigative journalism has revolutionized the way journalists analyze data and uncover stories, while also posing new challenges in cybersecurity and the detection of deepfakes. The academic literature highlights the importance of training journalists in cybersecurity best practices, investing in AI technologies, and detecting and debunking deepfakes to safeguard the integrity of journalistic work. By integrating AI and cybersecurity measures into newsrooms, journalists can better protect themselves against cyber threats and combat misinformation, ensuring the continued relevance and credibility of journalism in the digital age. Keywords: Artificial Intelligence, Cybersecurity, Investigative Journalism, Deepfakes, Data Analysis, Misinformation, News Organizations, Cyber Threats, Journalism Integrity. Journal Reference Format: Ademola, O.E. & Somorin, K. (2024): AI and Cybersecurity in Investigative Journalism: A Literature Review. Social Informatics, Business, Politics, L:aw, Environmental Sciences & Technology Journal. Vol. 10, No. 1. Pp 9-18 www.isteams/socialinformaticsjournal. dx.doi.org/10.22624/AIMS/SIJ/V10N1P2
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Heydari, Vahid, and Kofi Nyarko. "Empowering the Next Generation: A Strategic Roadmap for AI in Cybersecurity Education." Journal of The Colloquium for Information Systems Security Education 12, no. 1 (2025): 8. https://doi.org/10.53735/cisse.v12i1.202.
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The integration of artificial intelligence (AI) into cybersecurity is revolutionizing how institutions address increasingly complex cyber threats. As the demand for expertise in both AI and cybersecurity grows, Historically Black Colleges and Universities (HBCUs) have a unique opportunity to develop programs that equip students with the necessary skills to meet these evolving challenges. This paper presents a strategic roadmap for developing AI in Cybersecurity programs at HBCUs, emphasizing interdisciplinary collaboration, hands-on learning, adversarial defense, explainability, ethical leadership, and diversity. To strengthen the roadmap, this paper incorporates real-world case studies from existing AI-cybersecurity programs and proposes strategies to overcome key challenges such as faculty expertise gaps, funding limitations, and resource scalability. Additionally, a framework for evaluating program effectiveness is introduced, offering measurable metrics such as student outcomes, industry collaboration, and curriculum adaptability. By implementing this roadmap, HBCUs can establish sustainable AI in Cybersecurity programs that align with industry needs while fostering leadership and innovation in the cybersecurity workforce.
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Vishwamitra, Nishant, Ebuka Okpala, Song Liao, et al. "Enhancing AI-Centered Social Cybersecurity Education through Learning Platform Design." Journal of The Colloquium for Information Systems Security Education 12, no. 1 (2025): 9. https://doi.org/10.53735/cisse.v12i1.204.
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Artificial Intelligence (AI) technologies have become increasingly pervasive in our daily lives. Recent breakthroughs such as large language models (LLMs) are being increasingly used globally to enhance their work methods and boost productivity. However, the advent of these technologies has also brought forth new challenges in the critical area of social cybersecurity. While AI has broadened new frontiers in addressing social issues, such as cyberharassment and cyberbullying, it has also worsened existing social issues such as the generation of hateful content, bias, and demographic prejudices. Although the interplay between AI and social cybersecurity has gained much attention from the research community, very few educational materials have been designed to engage students by integrating AI and socially relevant cybersecurity through an interdisciplinary approach. In this paper, we present our newly designed open-learning platform, which can be used to meet the ever-increasing demand for advanced training in the intersection of AI and social cybersecurity. The designed platform, which consists of hands-on labs and education materials, incorporates the latest research results in AI-based social cybersecurity, such as cyberharassment detection, AI bias and prejudice, and adversarial attacks on AI-powered systems, are implemented using Jupyter Notebook, an open-source interactive computing platform for effective hands-on learning. Through a user study of 201 students from two universities, we demonstrate that students have a better understanding of AI-based social cybersecurity issues and mitigation after doing the labs, and they are enthusiastic about learning to use AI algorithms in addressing social cybersecurity challenges for social good.
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Alanezi, Mafaz, and Ruah Mouad Alyas AL-Azzawi. "AI-Powered Cyber Threats: A Systematic Review." Mesopotamian Journal of CyberSecurity 4, no. 3 (2024): 166–88. https://doi.org/10.58496/mjcs/2024/021.
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The joining of artificial intelligence (AI) across different areas has fundamentally improved productivity and development. Nevertheless, this progression has increased cybersecurity threats, especially those determined by AI itself. These AI-powered threats exploit the advancements intended to obtain computerized frameworks, in this manner subverting their honesty. This systematic review focuses on the intricacies of AI-driven cyber threats, which use complex AI abilities to lead to intricate and tricky cyberattacks. Our review integrates existing examinations to determine the extension, location procedures, effects, and relief systems connected with AI-initiated threats. We feature the powerful exchange between AI improvement and cybersecurity, underlining the requirement for cutting edge protective frameworks that advance pairs with increasing threats. The discoveries highlight the basic job of AI in both carrying out and countering cybersecurity measures, representing a dualistic effect that requires ceaseless development in cybersecurity techniques.
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Taylor, Rodriguez Vance. "Artificial Intelligence in Cybersecurity: A Survey of National Research, Investment and Policy Implementation." International Journal of Computer Science and Information Technology Research 11, no. 2 (2023): 18–25. https://doi.org/10.5281/zenodo.7858980.
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<strong>Abstract:</strong> As AI technology advances, so do the challenges and risks associated with cybersecurity. To address these challenges, policymakers have been developing AI policies that aim to regulate and govern the development and use of AI in various domains, including cybersecurity. In this paper, we explore the current state of AI policy and its implications for cybersecurity. This paper provides an analysis of the strengths and weaknesses of existing AI policies and an examination of their potential impact on cybersecurity. The goal of this research is to provide insights and recommendations to policymakers and stakeholders on how to develop effective AI policies that promote cybersecurity while fostering innovation and growth in the AI industry. <strong>Keywords:</strong> Artificial Intelligence; Cybersecurity; international policy, Artificial Intelligence investments, national defense. <strong>Title:</strong> Artificial Intelligence in Cybersecurity: <em>A Survey of National Research, Investment and Policy Implementation</em> <strong>Author:</strong> Taylor Rodriguez Vance <strong>International Journal of Computer Science and Information Technology Research</strong> <strong>ISSN 2348-1196 (print), ISSN 2348-120X (online)</strong> <strong>Vol. 11, Issue 2, April 2023 - June 2023</strong> <strong>Page No: 18-25</strong> <strong>Research Publish Journals</strong> <strong>Website: www.researchpublish.com</strong> <strong>Published Date: 24-April-2023</strong> <strong>DOI: https://doi.org/10.5281/zenodo.7858980</strong> <strong>Paper Download Link (Source)</strong> <strong>https://www.researchpublish.com/papers/artificial-intelligence-in-cybersecurity-a-survey-of-national-research-investment-and-policy-implementation</strong>
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Zainan Abidin, Aida Wati, Nor Hapiza Mohd Ariffin, Zan Azma Nasruddin, Nurul Fadly Habidin, and Marina Yusoff. "Evaluating and Modelling Artificial Intelligence and Emotional Intelligence to Improve Cybersecurity Employee Ethical Competence Model." Journal of Advanced Research Design 130, no. 1 (2025): 13–25. https://doi.org/10.37934/ard.130.1.1325.
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Implementing an ethical competency model in a cybersecurity organisation is crucial for ensuring that employees are prepared to succeed in the complex ethical terrain of cybersecurity decision-making. The model comprises an ethical framework customised for cybersecurity, highlighting moral awareness across decision-making processes. This paper explores the integration of Artificial Intelligence (AI) and Emotional Intelligence (EI) to enhance ethical competence in cybersecurity professionals, highlighting the critical need for a balanced approach to technical efficiency and ethical decision-making. Through empirical research, including expert surveys and literature reviews, the study identifies critical AI and EI skills, develops a measurement instrument and proposes a model to assess and improve ethical competence in cybersecurity organisations. The research underscores the importance of AI in analysing cybersecurity threats and the role of EI in managing human factors, advocating for targeted training programs that combine AI capabilities with emotional and ethical awareness. Seven expert panels with an average of at least two years working as cybersecurity professionals were surveyed. The methodology involved applying exploratory factor analysis, reliability analysis and calculating the importance index with the developed questionnaire. EFA identified two constructs for each skill: AI in cybersecurity development and the integration challenges and EI in decision-making and leadership function in the operations. The instrument reliability was also consistent, with Alpha values ranging between .687 to .941. The findings suggest that developing an ethical competence model that integrates AI and EI can significantly contribute to establishing a cybersecurity environment that is both technologically advanced and ethically sound, addressing the complex ethical dilemmas faced by cybersecurity professionals.
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Mohammed, Anwar. "AI in Cybersecurity: Enhancing Audits and Compliance Automation." AI in Cybersecurity: Enhancing Audits and Compliance Automation 7, no. 1 (2021): 1–10. https://doi.org/10.5281/zenodo.14760189.
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As organizations increasingly rely on digital infrastructures, the need for robust cybersecurity measures has become paramount. Cybersecurity audits play a crucial role in identifying vulnerabilities and ensuring compliance with regulatory standards. This paper explores the integration of artificial intelligence (AI) in enhancing cybersecurity audits, focusing on automating vulnerability detection and compliance assessments. By examining current methodologies and proposing an AI-driven framework, this research highlights the benefits, challenges, and future implications of utilizing AI in cybersecurity audits.
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Wang, Mingzheng. "Generative AI: A New Challenge for Cybersecurity." Journal of Computer Science and Technology Studies 6, no. 2 (2024): 13–18. http://dx.doi.org/10.32996/jcsts.2024.6.2.3.
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The rapid development of Generative Artificial Intelligence (GAI) technology has shown tremendous potential in various fields, such as image generation, text generation, and video generation, and it has been widely applied in various industries. However, GAI also brings new risks and challenges to cybersecurity. This paper analyzes the application status of GAI technology in the field of cybersecurity and discusses the risks and challenges it brings, including data security risks, scientific and technological ethics and moral challenges, Artificial Intelligence (AI) fraud, and threats from cyberattacks. On this basis, this paper proposes some countermeasures to maintain cybersecurity and address the threats posed by GAI, including: establishing and improving standards and specifications for AI technology to ensure its security and reliability; developing AI-based cybersecurity defense technologies to enhance cybersecurity defense capabilities; improving the AI literacy of the whole society to help the public understand and use AI technology correctly. From the perspective of GAI technology background, this paper systematically analyzes its impact on cybersecurity and proposes some targeted countermeasures and suggestions, possessing certain theoretical and practical significance.
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Siva, Kumar Mamillapalli. "Adversarial and Offensive AI in Cyber Security." International Journal on Science and Technology 15, no. 4 (2024): 1–7. https://doi.org/10.5281/zenodo.14866278.
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As Artificial Intelligence (AI) continues to advance swiftly and integrates into various domains, cybersecurity becomes increasingly crucial for navigating both the benefits and pitfalls of AI technologies. This paper explores how adversarial and offensive artificial intelligence (AI) techniques affect cybersecurity defense methods. The research offers important insights from analyzing recent cyber-attacks that use adversarial AI, showing a marked rise in the complexity of these threats when compared to traditional security frameworks. Key results indicate that older cybersecurity defenses are becoming less effective against attacks that are boosted by AI, making it essential to create flexible strategies that use offensive AI techniques to forecast and stop possible breaches. By understanding the possible risks posed by AI, organizations can strengthen their defenses against new cyber threats. Additionally, this study emphasizes the need for a major change in how organizations approach cybersecurity, suggesting a proactive approach that recognizes the dual-use nature of AI tools. The wider implications of this research indicate that applying advanced AI methods in cybersecurity not only improves system resilience but also fosters a culture of ongoing improvement and awareness, ultimately protecting organization essential infrastructure from changing cyber threats.
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Aniruddha, Pathak*. "Dual-Edge Evolution: A Comprehensive Analysis of Artificial Intelligence's Impact on Modern Cybersecurity Defence Systems and Emerging Threats." International Journal of Scientific Research and Technology 2, no. 4 (2025): 276–83. https://doi.org/10.5281/zenodo.15204168.
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This comprehensive study explores the transformative role of Artificial Intelligence (AI) in modern cybersecurity, examining both its defensive capabilities and potential vulnerabilities. The research investigates the dual nature of AI in cybersecurity, where it serves as both a powerful defense mechanism and a tool that can be exploited for sophisticated cyber attacks. Through analysis of current implementations and emerging trends, the study reveals how AI-powered systems are revolutionizing threat detection, response mechanisms, and predictive security measures. The investigation demonstrates that machine learning algorithms, when properly trained on extensive datasets, can identify and respond to cyber threats in real-time, significantly reducing the detection and response timeframe from days to seconds. The research also highlights the critical importance of data protection and ethical considerations in AI-driven cybersecurity systems, particularly addressing concerns about algorithmic bias and privacy preservation. Furthermore, the study examines the integration of Natural Language Processing (NLP) in cybersecurity applications, especially its role in detecting phishing attempts and analyzing threat intelligence. The findings indicate that while AI substantially enhances cybersecurity capabilities through automated security protocols and adaptive algorithms, it also introduces new challenges that require careful consideration. The research emphasizes the necessity of maintaining human oversight in cybersecurity operations, despite AI's increasing autonomy and capability. A notable contribution is the comparative analysis of pre- and post-AI/ML integration metrics in cybersecurity operations, which demonstrates significant improvements in threat detection accuracy, incident response time, and resource allocation efficiency. Looking toward the future, the study forecasts the growing importance of explainable AI (XAI) and its integration with emerging technologies like blockchain and quantum computing in cybersecurity frameworks. The research concludes that the benefits of AI integration in cybersecurity significantly outweigh the challenges, provided organizations implement proper infrastructure, training, and resource allocation.
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Bhanushali, Aastha. "AI-Driven Cybersecurity: A Cornerstone of National Security Amidst Emerging Threats and Innovative Solutions." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 1265–69. https://doi.org/10.22214/ijraset.2025.67522.
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In the current digital environment, cybersecurity has emerged as a key component of national security. Advanced defences have become necessary due to the quick evolution of cyberthreats, such as ransomware, cyberwarfare, and AI-driven attacks. Artificial intelligence (AI) including into cybersecurity systems has transformed security automation, incident response, and threat detection. The influence of AI in cybersecurity, new threats, legal frameworks governing cyber laws, real-world case studies of cyber frauds, and AI-driven cybersecurity solutions are all covered in this research paper. Additionally, policy proposals and statistical trends are examined to offer a strategic perspective on bolstering national cyber defences. This study provides a thorough overview of AI's involvement in digital protection and national security by referencing government rules, cybersecurity reports, and peer-reviewed literature.
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Kingsley David Onyewuchi Ofoegbu, Olajide Soji Osundare, Chidiebere Somadina Ike, Ololade Gilbert Fakeyede, and Adebimpe Bolatito Ige. "Empowering users through AI-driven cybersecurity solutions: enhancing awareness and response capabilities." Engineering Science & Technology Journal 4, no. 6 (2023): 707–27. http://dx.doi.org/10.51594/estj.v4i6.1528.
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Artificial Intelligence (AI)-driven cybersecurity solutions are revolutionizing how organizations and individuals address the growing complexity and sophistication of cyber threats. This paper explores the role of AI in empowering users by enhancing their awareness and response capabilities, making cybersecurity more accessible and effective. AI-driven tools and systems are designed to detect, predict, and mitigate potential threats in real-time, thereby enabling users to respond more swiftly and effectively to cyber incidents. By leveraging machine learning algorithms and advanced analytics, AI can identify patterns and anomalies that may indicate security breaches, often before they fully materialize. This proactive approach not only improves threat detection but also reduces the response time, minimizing potential damage. One of the key benefits of AI in cybersecurity is its ability to personalize security measures according to individual user behavior. By continuously analyzing user activity, AI systems can tailor security protocols to better protect against specific threats, thereby enhancing overall security posture. Additionally, AI-driven solutions offer users insights into potential vulnerabilities and provide guidance on best practices for maintaining security, thus fostering greater awareness and proactive behavior among users. The integration of AI into cybersecurity also addresses the challenge of managing large volumes of data generated by modern digital environments. AI systems can process and analyze this data far more efficiently than traditional methods, enabling quicker and more accurate identification of threats. Furthermore, AI can assist in automating routine security tasks, such as patch management and threat hunting, freeing up human resources to focus on more complex issues that require human judgment. In conclusion, AI-driven cybersecurity solutions are instrumental in empowering users by enhancing their awareness and response capabilities. These systems offer a proactive, personalized, and efficient approach to cybersecurity, making it possible for users to stay ahead of potential threats. As cyber threats continue to evolve, the role of AI in cybersecurity will become increasingly critical, ensuring that both individuals and organizations can maintain a robust defense against the ever-changing landscape of digital threats. Keywords: AL-Driven, Cybersecurity, Awareness, Empowering, Response Capabilities.
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Yadav, Dr Neha. "AI in Cybersecurity: A Literature review." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–7. https://doi.org/10.55041/isjem03378.
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Abstract - In cybersecurity, artificial intelligence is revolutionizing incident response, risk management, and threat identification in a progressively hostile cyber threat landscape. This research presents a thorough literature review on AI in cybersecurity, focusing on both aspects of the balance sheet. This paper discusses how AI-driven technologies like machine learning, deep learning, natural language processing, and expert systems improve security frameworks through predictive analytics, real-time threat intelligence, and anomaly detection. The research explores various uses of AI such as network protection, cloud safety, healthcare, and finance, highlighting how AI-driven solutions enhance the resilience of cybersecurity against attacks. Nonetheless, there are drawbacks too, primarily associated with algorithmic prejudices and aggressive attacks. This paper discusses AI cybersecurity tools like Cylance, Darktrace, and IBM Watson, analyzing their influences and effects on security operations. The study also explores recent advancements and enhancements in AI-driven cybersecurity, ethical concerns, and regulatory structures. To establish a safe digital space, this document highlights the importance of a unified strategy that integrates AI with human expertise, ethics, and regulatory adherence. Key Words: Artificial Intelligence, Machine Learning, Deep Learning, Expert Systems, Natural Language Processing, Threat Detection, AI-driven Threat Intelligence, Dynamic Threats, Cyber Attack, Cyber Security.
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Avaneesh, Mohapatra, and Reddy Gagan. "The implications of Artificial Intelligence (AI) on cybersecurity: A detailed review for multidomain industry." World Journal of Advanced Research and Reviews 23, no. 2 (2024): 1926–37. https://doi.org/10.5281/zenodo.14868896.
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Cyber threats are becoming increasingly complicated and diverse, posing serious risks to individuals, businesses, and organizations in the cybersecurity sector. The cybersecurity industry needs to change to combat these new dangers as cybercriminals are always coming up with new ways to get past protections. This paper investigates how artificial intelligence (AI) may both simplify and improve cybersecurity efforts. AI has transformed the industry by offering cutting-edge defensive technologies, but it also gives cybercriminals new tools at their disposal to automate and enhance their hacking methods. The effect of AI on authentication procedures—which are crucial for protecting network access—is given special attention. This paper emphasizes the critical need for creative remedies to defend against more complex cyberattacks by examining the dual nature of AI in cybersecurity.
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Khot, Abhishek. "Artificial Intelligence in Cybersecurity." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (2024): 2025–29. http://dx.doi.org/10.22214/ijraset.2024.63434.
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Abstract: Artificial Intelligence (AI) has become integral to cybersecurity, offering advanced solutions for monitoring, detecting, reporting, and countering cyber threats. As cyberattacks grow in number and sophistication, traditional security measures prove inadequate. AI's ability to quickly adapt and learn makes it a vital tool in defending against these evolving threats. It automates routine tasks, accelerates threat detection and response, and improves the accuracy of security measures. However, AI also presents risks, such as potential misuse by cybercriminals, necessitating continuous human oversight. The increasing incidence of cyberattacks highlights the need for robust AI-enabled cybersecurity systems to protect sensitive data across industries
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Blessing Austin-Gabriel, Adeoye Idowu Afolabi, Christian Chukwuemeka Ike, and Nurudeen Yemi Hussain. "AI and machine learning for adaptive elearning platforms in cybersecurity training for entrepreneurs." Computer Science & IT Research Journal 5, no. 12 (2024): 2715–29. https://doi.org/10.51594/csitrj.v5i12.1787.
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This paper explores the application of Artificial Intelligence (AI) and Machine Learning (ML) in adaptive eLearning platforms designed for cybersecurity training, with a focus on entrepreneurs. Due to limited resources and technical expertise, entrepreneurs face unique challenges in protecting their businesses from cyber threats. Traditional training methods often fail to meet their needs, highlighting the importance of AI-driven platforms that offer personalized learning experiences. The paper examines the benefits of AI-powered eLearning systems, including improved engagement, real-time assessments, and adaptation to diverse learning styles. It also addresses emerging trends in AI and ML for cybersecurity education, the integration of adaptive eLearning into entrepreneurial support systems, and the ethical and regulatory implications of AI-driven learning. Finally, recommendations are provided for policymakers and educators to support the growth of AI in cybersecurity training. The findings suggest that AI-powered platforms can offer scalable, effective solutions for entrepreneurs to enhance their cybersecurity skills and protect their digital assets. Keywords: Artificial Intelligence, Machine Learning, Cybersecurity Training, Adaptive eLearning, Entrepreneurs, Personalized Learning.