Safiullah Jalalzai
Ghazni University

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Exploring the Integration of AI and Cloud Computing: Navigating Opportunities and Overcoming Challenges Musawer Hakimi; Ghulam Ali Amiri; Safiullah Jalalzai; Farid Ahmad Darmel; Zakirullah Ezam
TIERS Information Technology Journal Vol. 5 No. 1 (2024)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v5i1.5496

Abstract

This research seeks to establish how the integration of cloud computing and artificial intelligence identifies opportunities across operational efficiency, cost reduction, and innovation acceleration. This study seeks to establish how this integration is revolutionizing traditional business models and dealing with emerging security, privacy, and regulatory challenges. The applied method in this research was a systematic review strategy whose sources of data will be chosen from IEEE Xplore, Wiley Online Library, Springer, and ScienceDirect. The literature review focused on publications from 2019 to 2024 to deduce current findings that remain relevant. Results have shown that artificial intelligence, when integrated with cloud computing, would significantly enhance operational efficiency through process optimization and reduced cost using scalable cloud solutions. This also provides a greater pace of innovation by allowing real-time data processing and advanced analytics. However, such integration has a specific set of security and privacy concerns related to breaches and compliance with regulations in continuous evolution. It concludes that, though large, the benefits of AI and cloud computing integration must be reined in by strong security measures, updating regulatory frameworks, and continued research into ethical implications.
Opportunities and Challenges in AI-Driven Cybersecurity: A Systematic Literature Shahwali Shahidi; Farid Ahmad Darmel; Safiullah Jalalzai; Ghulam Ali Amiri
Journal of Social Science Utilizing Technology Vol. 2 No. 4 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jssut.v2i4.1541

Abstract

Background. The need for more sophisticated security strategies has become apparent as the number of cyber threats grows. AI is one framework that has been shown to boost security by providing advanced threat detection and response capabilities. Nonetheless, AI integration introduces inherently ethical and privacy-related concerns. Purpose. This research examines the AI implementation factors influencing the overall performance of the AI for cybersecurity and data privacy in both critical infrastructures and financial services. Method. This research derives its data from the extensive literature published from 2019 to 2024 in notable databases such as IEEE, Science Direct, MDPI, and Wiley Library, with more than 300 records. This analysis examined, with the help of artificial intelligence tools, the patterns and recurrent problems about the place of AI in cybersecurity, setting sights on the present challenges in the domains of intrusion detection and mitigation. Results. The results indicate that better threat detection in industry is enabled by AI. However, disadvantages of bias, the need for privacy, and suboptimal data management are evident, necessitating the need for stronger machine and human-readable regulations. Conclusion. Although AI strengthens security in an age of cyber-insecurity, its shortcomings point to the need for further development. Post-quantitative encryption palliatives and integration models will be effectively handled as cybersecurity-harming threats evolve.