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Journal articles on the topic 'AI-Driven Cloud Security'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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1

Middae, Vijaya lakshmi. "Enhancing Cloud Security with AI-Driven Big Data Analytics." American Journal of Engineering and Technology 07, no. 05 (2025): 185–91. https://doi.org/10.37547/tajet/volume07issue05-18.

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Since cloud computing is changing so rapidly, maintaining strong security is now a major issue for companies everywhere. Massive volumes of mixed data are constantly created in cloud environments at every layer, involving virtual machines, containers, storage, identity management and application activities. It is usually not possible for traditional security systems and old monitoring tools to manage vast and changing data flow in real time. Con- ventional methods fail to discover advanced persistent threats, attacks by team members and new vulnerabilities because they do not easily adjust to

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2

Olabanji, Samuel Oladiipo, Yewande Alice Marquis, Chinasa Susan Adigwe, Samson Abidemi Ajayi, Tunbosun Oyewale Oladoyinbo, and Oluwaseun Oladeji Olaniyi. "AI-Driven Cloud Security: Examining the Impact of User Behavior Analysis on Threat Detection." Asian Journal of Research in Computer Science 17, no. 3 (2024): 57–74. http://dx.doi.org/10.9734/ajrcos/2024/v17i3424.

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This study explores the comparative effectiveness of AI-driven user behavior analysis and traditional security measures in cloud computing environments. It specifically examines their accuracy, speed, and predictive capabilities in detecting and responding to cyber threats. As reliance on cloud-based solutions intensifies, the integration of Artificial Intelligence (AI) and machine learning into cloud security has become increasingly vital. The research focuses on how AI-driven security systems, with their advanced pattern recognition and anomaly detection, compare to traditional methods in id

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Rehan, Hassan. "AI-Driven Cloud Security: The Future of Safeguarding Sensitive Data in the Digital Age." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 1, no. 1 (2024): 47–66. http://dx.doi.org/10.60087/jaigs.v1i1.p66.

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As organizations increasingly rely on cloud computing for storage, processing, and deployment of sensitive data, ensuring robust security measures becomes paramount. This paper explores the intersection of artificial intelligence (AI) and cloud security, presenting AI-driven solutions as the future of safeguarding sensitive data in the digital age. Leveraging AI algorithms and machine learning techniques, cloud security can adapt and evolve to counter emerging threats in real-time, enhancing detection, prevention, and response capabilities. This paper discusses various AI-driven approaches to

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Rehan, Hassan. "AI-Driven Cloud Security: The Future of Safeguarding Sensitive Data in the Digital Age." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 1, no. 1 (2024): 132–51. http://dx.doi.org/10.60087/jaigs.v1i1.89.

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As organizations increasingly rely on cloud computing for storage, processing, and deployment of sensitive data, ensuring robust security measures becomes paramount. This paper explores the intersection of artificial intelligence (AI) and cloud security, presenting AI-driven solutions as the future of safeguarding sensitive data in the digital age. Leveraging AI algorithms and machine learning techniques, cloud security can adapt and evolve to counter emerging threats in real-time, enhancing detection, prevention, and response capabilities. This paper discusses various AI-driven approaches to

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Chukwuemeka Nwachukwu, Kehinde Durodola-Tunde, and Chukwuebuka Akwiwu-Uzoma. "AI-driven anomaly detection in cloud computing environments." International Journal of Science and Research Archive 13, no. 2 (2024): 692–710. http://dx.doi.org/10.30574/ijsra.2024.13.2.2184.

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The rapid adoption of cloud computing has changed the way businesses manage and store data, but it has also introduced new security challenges. One of the most pressing concerns in cloud environments is the detection of anomalies, which can signal potential security breaches, system failures, or performance issues. Traditional anomaly detection methods often fall short due to the complexity, scalability, and dynamic nature of cloud infrastructures. In recent years, Artificial Intelligence (AI)-driven anomaly detection techniques, particularly those leveraging machine learning and deep learning

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Srivastava Manish Singh, Sarthak. "Implementing AI - Driven Strategies in DevSecOps for Enhanced Cloud Security." International Journal of Science and Research (IJSR) 13, no. 2 (2024): 1281–85. http://dx.doi.org/10.21275/sr24216023228.

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Oduri, Sailesh. "AI-Driven Security Protocols for Modern Cloud Engineers." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 10, no. 2 (2019): 2002–8. http://dx.doi.org/10.61841/turcomat.v10i2.14739.

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In the era of digital transformation, cloud computing has become integral to modern enterprises, offering scalable resources and flexibility. However, this rapid adoption has also introduced a new landscape of security challenges, including data breaches, insider threats, and misconfigurations, all of which can compromise sensitive information and disrupt operations. Traditional security measures often fall short in addressing these complex threats, prompting the need for more advanced solutions. This article explores the pivotal role of AI-driven security protocols in fortifying cloud infrast

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Dathwal, Prashant. "Frameworks for implementing AI-driven cloud orchestration." American Journal of Engineering and Technology 07, no. 06 (2025): 81–87. https://doi.org/10.37547/tajet/volume07issue06-08.

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This article presents an analysis of frameworks designed for AI-driven orchestration of cloud resources, focusing on contemporary methods and architectural models aimed at improving the efficiency, adaptability, and energy performance of cloud computing environments. The study includes a comprehensive review of applied machine learning techniques, deep learning, reinforcement learning algorithms, evolutionary algorithms, and hybrid approaches used for workload prediction, resource allocation optimization, and autonomous decision-making. The paper identifies key integration challenges, computat

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J, Anurag. "Review of AI-driven Cloud Optimization." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34000.

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Cloud automation is the key to realization a fully-optimized performance of modern cloud platforms while cloud resources utilization. Resource allocation efficiency is valuable. We are however faced with increasing pressure for computational resources. The Long Short-Term Memory (LSTM) algorithms have found a great use case in the dynamic resource allocation problem when the problem is solved by the proactive provisioning of resources based on historical usage patterns taking advantage of recurrent neural networks. Furthermore, the concern over quality-of-service delivery (QoS) and energy effi

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Researcher. "AI-DRIVEN THREAT DETECTION IN CLOUD-BASED APPLICATIONS." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 1045–55. https://doi.org/10.5281/zenodo.14286673.

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This comprehensive article examines the evolution and implementation of artificial intelligence and machine learning technologies in cloud security environments. The article analyzes the transformation from traditional security approaches to AI-driven solutions, focusing on deep learning architectures, reinforcement learning applications, and emerging technologies. It addresses the critical challenges in training AI models for cloud security, including data-related issues and their mitigation strategies. The article demonstrates significant improvements in threat detection, response capabiliti

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Bhavandla, Laxmana Kumar. "Challenges in Ensuring Cloud Security in AI-Driven Systems." International Journal of Computer Science and Mobile Computing 12, no. 10 (2023): 89–100. https://doi.org/10.47760/ijcsmc.2023.v12i10.008.

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This present paper seeks to discuss some of the control and legal issues arising when establishing cloud security in AI systems. They look at the issues related to the differences in laws governing data protection, requirements concerning the location of data, and specialized industry policies. As cloud computing continues to be integrated into AI, it becomes important for companies to understand and remain within legal parameters as well as protect their work. If properly managed, these challenges do not pose a threat towards secure and compliant AI-driven cloud.

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Harshvardhan Chunawala and Pratikkumar Chunawala. "Enhancing Cybersecurity in Cloud Environments Using AI-Driven Threat Detection and Response." International Journal of Futuristic Innovation in Engineering, Science and Technology (IJFIEST) 3, no. 1 (2024): 13–30. http://dx.doi.org/10.59367/2420ra43.

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As cloud computing becomes increasingly integral to modern infrastructure, the importance of robust cybersecurity measures within cloud environments cannot be overstated. Traditional security approaches often fall short in addressing the dynamic and complex nature of cloud-based threats. This paper explores the application of artificial intelligence (AI) to enhance cybersecurity in cloud environments, with a focus on AI-driven threat detection and response systems. By leveraging machine learning algorithms and deep learning models, AI can analyze vast amounts of data in real-time, identifying

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AIRCC. "EMPOWERING CLOUD-NATIVE SECURITY: THE TRANSFORMATIVE ROLE OF ARTIFICIAL INTELLIGENCE." International Journal of Artificial Intelligence & Applications (IJAIA) 15, no. 6 (2024): 1–11. https://doi.org/10.5121/ijaia.2024.15601.

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Cloud-native applications, built to leverage the scalability and flexibility of cloud infrastructure, havetransformed how organizations develop, deploy, and manage software. However, their dynamic anddistributed nature presents unique security challenges, such as container vulnerabilities, API exploits, andmisconfigurations. Artificial Intelligence (AI) has emerged as a critical enabler in addressing thesechallenges. This white paper explores the role of AI in securing cloud-native applications, examining itscapabilities in threat detection, automated response, compliance enforcement, and anom

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Phanireddy, Sandeep. "AI-Driven Identity Access Management (IAM)." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 05, no. 06 (2021): 1–9. https://doi.org/10.55041/ijsrem8931.

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Organizations worldwide depend on Identity and Access Management (IAM) systems to control who can access which resources and under what conditions. However, rapid digital transformation, the shift to cloud-based services, and the rising complexity of user behaviors have challenged traditional IAM approaches. AI-driven IAM methods promise a more flexible, adaptive, and risk-sensitive framework. By applying machine learning and intelligent analytics to user patterns, device signals, and threat intelligence, these next-generation IAM systems can proactively detect anomalies, reduce manual tasks,

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Ejeofobiri, Chigozie Kingsley, Joy Ezinwanneamaka Ike, Mukhtar Dolapo Salawudeen, David Agyemfra Atakora, Joseph Darko Kessie, and Tolulope Onibokun. "Securing Cloud Databases Using AI and Attribute-Based Encryption." Journal of Frontiers in Multidisciplinary Research 6, no. 1 (2025): 39–47. https://doi.org/10.54660/.ijfmr.2025.6.1.39-47.

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As cloud databases continue to gain widespread adoption, ensuring data security remains a critical challenge due to risks such as unauthorized access, data breaches, and insider threats. Traditional encryption techniques provide data confidentiality but lack the flexibility needed for dynamic and fine-grained access control. Attribute-Based Encryption (ABE) offers a robust solution by enabling access control based on user attributes rather than static roles, ensuring that only authorized users with matching attributes can decrypt sensitive data. This review explores the integration of Artifici

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Bukunmi Temiloluwa Ofili, Emmanuella Osaruwenese Erhabor, and Oghogho Timothy Obasuyi. "Enhancing federal cloud security with AI: Zero trust, threat intelligence and CISA Compliance." World Journal of Advanced Research and Reviews 25, no. 2 (2025): 2377–400. https://doi.org/10.30574/wjarr.2025.25.2.0620.

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The increasing adoption of cloud computing by federal agencies has introduced significant security challenges, necessitating robust strategies to protect sensitive government data. Traditional perimeter-based security models are no longer sufficient against evolving cyber threats, leading to the need for Zero Trust Architecture (ZTA), AI-driven threat intelligence, and compliance with Cybersecurity and Infrastructure Security Agency (CISA) frameworks. This paper explores how artificial intelligence (AI) enhances federal cloud security by enabling adaptive access controls, automated anomaly det

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Isaac, Clement Praveen Xavier Pakkam. "No-Code Cloud AI: The Rise of AI-Assisted Cloud Architecture Design." International Journal of Engineering and Advanced Technology Studies 13, no. 2 (2025): 1–21. https://doi.org/10.37745/ijeats.13/vol13n2121.

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The rapid evolution of cloud computing has transformed it from a specialized technical domain into a strategic business necessity. However, the complexity of cloud infrastructure design traditionally demands deep expertise in networking, security, and resource provisioning—creating a significant barrier for many organizations pursuing digital transformation. This article explores how an emerging paradigm—No-Code Cloud AI—is bridging this expertise gap by democratizing access to sophisticated cloud infrastructure through AI-assisted design tools. It introduces the Three-Tier No-Code AI Cloud Fr

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Srinivas Reddy Cheruku. "AI-Driven Security Posture Management: A Revolutionary Approach to Multi-Cloud Enterprise Security." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 497–509. https://doi.org/10.32628/cseit25111237.

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The landscape of cloud security has undergone a transformative evolution, driven by the complexity of modern digital infrastructures and the escalating sophistication of cyber threats. This comprehensive article explores an innovative AI-driven Cloud Security Posture Management (CSPM) framework that transcends traditional security methodologies. By leveraging advanced machine learning algorithms, neural network architectures, and intelligent automation, the framework offers a proactive, adaptive approach to cybersecurity that addresses the multifaceted challenges of multi-cloud environments. T

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Masagali, Bhanu Prakash Manjappasetty, and Mandar Nayak. "Empowering Cloud-native Security: the Transformative Role of Artificial Intelligence." International Journal of Artificial Intelligence & Applications 15, no. 6 (2024): 01–11. https://doi.org/10.5121/ijaia.2024.15601.

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Cloud-native applications, built to leverage the scalability and flexibility of cloud infrastructure, have transformed how organizations develop, deploy, and manage software. However, their dynamic and distributed nature presents unique security challenges, such as container vulnerabilities, API exploits, and misconfigurations. Artificial Intelligence (AI) has emerged as a critical enabler in addressing these challenges. This white paper explores the role of AI in securing cloud-native applications, examining its capabilities in threat detection, automated response, compliance enforcement, and

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Mamidi, Sundeep Reddy. "Dynamic Security Policies for Cloud Infrastructures: An AI-Based Framework." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 1, no. 1 (2024): 200–211. http://dx.doi.org/10.60087/jaigs.v1i1.159.

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The rapid expansion of cloud computing has introduced significant challenges in maintaining robust security policies due to the dynamic and scalable nature of cloud environments. This research presents an AI-based framework for developing and implementing dynamic security policies in cloud infrastructures. The proposed framework leverages machine learning algorithms to analyze and predict potential security threats, enabling the real-time adaptation of security measures. By continuously monitoring cloud resources and utilizing intelligent threat detection mechanisms, the framework ensures a pr

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Patel, Jay. "Prospects of Cloud-Driven Deep Learning- Leading the Way for Safe and Secure AI." INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING & APPLIED SCIENCES 8, no. 3 (2020): 57–63. https://doi.org/10.55083/irjeas.2020.v08i03011.

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The combination of cloud computing and deep learning models is changing the field of artificial intelligence (AI), facilitating more scalable, efficient, and adaptable systems for intricate data-driven activities. Nonetheless, as AI systems grow more powerful and widespread, guaranteeing their safety and security continues to be a significant challenge. This document investigates the future of cloud-based deep learning, specifically emphasizing innovative approaches to guarantee AI safety and security. We explore the capabilities of cloud-based systems in facilitating extensive deep learning m

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Mohammed Rashid, Mohanad, and Omar Mahmood Yaseen. "AI-Driven Cybersecurity Measures for Hybrid Cloud Environments: A Framework for Multi-Cloud Security Management." International Journal on Engineering Artificial Intelligence Management, Decision Support, and Policies 2, no. 1 (2025): 30–39. https://doi.org/10.63503/j.ijaimd.2025.39.

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The complex landscape of multi-cloud settings is the result of the fast growth of cloud computing and the ever-changing needs of contemporary organizations. Strong cyber defenses are of fundamental importance in this setting. In this study, we investigate the use of AI in hybrid cloud settings for the purpose of multi-cloud security management. For hybrid cloud deployment, this mathematical approach maximizes a security metric. Programmers use backslashes to escape special char acters and identify file paths. The objective function optimizes application and data asset security by using AI-enha

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Omoniyi David Olufemi. "Quantum-AI Federated Clouds: A trust-aware framework for cross-domain observability and security." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 4098–140. https://doi.org/10.30574/wjarr.2025.26.2.2074.

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The convergence of quantum computing, artificial intelligence (AI), and federated cloud architecture offers transformative potential for secure, scalable, and privacy-preserving data processing. Yet, trust management and cross-domain observability remain major challenges, particularly in decentralized, heterogeneous cloud environments. This paper introduces Quantum-AI Federated Clouds (QAIFC) a novel trust-aware framework that combines quantum-safe encryption, federated machine learning, and explainable AI to enable secure and observable operations across cloud domains. We present QFedSecure,

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Yogeswara, Reddy Avuthu. "Trustworthy AI in Cloud MLOps: Ensuring Explainability, Fairness, and Security in AI-Driven Applications." Journal of Scientific and Engineering Research 8, no. 1 (2021): 246–55. https://doi.org/10.5281/zenodo.14274110.

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The growing reliance on cloud-native Machine Learning Operations (MLOps) to automate and scale AI-driven applications has raised critical concerns about the trustworthiness of these systems. Specifically, ensuring that AI models deployed in cloud environments are explainable, fair, and secure has become paramount. This paper proposes a comprehensive framework that integrates explainability, fairness, and security into MLOps workflows to address these concerns. The framework utilizes state-of-the-art explainability techniques, such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpr

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Aakarsh Mavi. "Optimizing HVAC Security : AI-Driven Metadata Management Framework." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 2 (2024): 834–39. https://doi.org/10.32628/cseit24102154.

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The HVAC industry depends on detailed metadata files like design schematics, maintenance records, compliance documents, and data from sensors. Managing these files effectively is key to operational efficiency, regulatory compliance, and maintenance tracking. This study introduces an AI-powered Metadata File Management System designed for HVAC companies, using machine learning, natural language processing (NLP), and cloud integration to automate file categorization, retrieval, duplication detection, and compliance monitoring. The system pulls metadata from sources such as IoT sensor logs, CAD f

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VENKATARAMESH, INDURU, and Kumar R. Veerandra. "AI-DRIVEN NETWORK SECURITY IN CLOUD ENVIRONMENTS: ENHANCING THREAT DETECTION AND MITIGATION." International Journal Of Engineering Technology Research & Management (IJETRM) 02, no. 11 (2018): 98–108. https://doi.org/10.5281/zenodo.15601005.

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This article proposes a novel AI-based network security model designed to be used in cloud environments withexpertise in enhancing detection and response capabilities for threats. By leveraging the application of machinelearning algorithms, the model efficiently identifies and blocks various kinds of cyber-attacks, such as DDoSattacks and advanced zero-day attacks. The performance of the model is excellent, with 98% Accuracy, 96%Precision, 94% Recall, and 95% F1rating, indicating excellent classification abilities and minimal false positives.Besides, its AUC-ROC of 0.92 indicates that it can c

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Vinay Kumar Kasula, Akhila Reddy Yadulla, Bhargavi Konda, and Mounica Yenugula. "Enhancing financial cybersecurity: An AI-driven framework for safeguarding digital assets." World Journal of Advanced Research and Reviews 14, no. 3 (2022): 788–800. https://doi.org/10.30574/wjarr.2022.14.3.1181.

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As cloud computing continues to dominate the modern technological landscape, organizations face growing challenges in preventing data breaches and sophisticated cyber threats. The increasing complexity and scale of cloud environments require advanced security mechanisms to address evolving threats. This paper introduces "SecureCloudAI," a cutting-edge AI-driven security framework designed to fortify sensitive data within cloud infrastructures. SecureCloudAI leverages a hybrid approach that combines machine learning models like Random Forest and deep learning techniques, including Long Short-Te

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Sunday Adeola Oladosu, Adebimpe Bolatito Ige, Christian Chukwuemeka Ike, Peter Adeyemo Adepoju, Olukunle Oladipupo Amoo, and Adeoye Idowu Afolabi. "AI-driven security for next-generation data centers: Conceptualizing autonomous threat detection and response in cloud-connected environments." GSC Advanced Research and Reviews 15, no. 2 (2023): 162–72. https://doi.org/10.30574/gscarr.2023.15.2.0136.

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The dynamic evolution of next-generation data centers, driven by cloud-native and hybrid architectures, has necessitated a paradigm shift in cybersecurity. Traditional security models, designed for static and on-premise environments, struggle to address the complexities of cloud-connected infrastructures and the rapidly evolving threat landscape. Emerging challenges, such as advanced persistent threats (APTs), ransomware, and insider attacks, demand sophisticated and adaptive security solutions. In this context, artificial intelligence (AI) emerges as a transformative technology capable of red

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Sasibhushan Rao Chanthati. "Leveraging Artificial Intelligence for smart cloud migration, reducing cost and enhancing efficiency." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 033–45. https://doi.org/10.30574/wjaets.2025.15.1.0191.

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Cloud computing has become a critical component of modern IT infrastructure, offering businesses scalability, flexibility, and cost efficiency. Unoptimized cloud migration strategies can lead to significant financial waste due to inefficient resource allocation, redundant workloads, and unpredictable cloud expenses. Traditional methods often rely on static provisioning and manual decision-making, leading to suboptimal cloud resource utilization. This research introduces an AI-driven framework for intelligent cloud planning and migration aimed at reducing cloud costs while maintaining high perf

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Sreenivasa, Rao Sola. "Security and Innovation in ERP Systems: Best Practices for AI, OIC, and Automation Integration." International Journal of Leading Research Publication 4, no. 8 (2023): 1–14. https://doi.org/10.5281/zenodo.15259092.

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IT innovation and cloud security are fundamental aspects of the modern enterprise environment, especially for ERP Cloud platforms. The paper outlines the optimal practices of implementing AI-based automation and Oracle Integration Cloud (OIC) to deliver security, efficiency, and business responsiveness. It demonstrates the way AI-based automation simplifies business processes, mitigates security risks, and automates regulatory compliance. Aside from this, the research identifies principal security concerns such as data security, access, and threat intelligence and demonstrates how the AI and O

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Suram, Kiran Kumar. "IMPROVING CLOUD SECURITY VIA AI-DRIVEN CLOUD INFRASTRUCTURE AUTOMATION AND HUMAN-AI COLLABORATION IN IAM." INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY 15, no. 6 (2024): 1734–42. https://doi.org/10.34218/ijcet_15_06_148.

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Ilter Taha Aktolga, Elif Sena Kuru, Yigit Sever, and Pelin Angin. "AI-driven container security approaches for 5G and beyond: A survey." ITU Journal on Future and Evolving Technologies 4, no. 2 (2023): 364–82. http://dx.doi.org/10.52953/zrck3746.

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The rising use of microservice-based software deployment on the cloud leverages containerized software extensively. The security of applications running inside containers, as well as the container environment itself, are critical for infrastructure in cloud settings and 5G. To address security concerns, research efforts have been focused on container security with subfields such as intrusion detection, malware detection and container placement strategies. These security efforts are roughly divided into two categories: rule-based approaches and machine learning that can respond to novel threats

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Sakthiswaran, Rangaraju, Stephanie Ness Dr., and Dharmalingam Rajesh. "Incorporating AI-Driven Strategies in DevSecOps for Robust Cloud Security." Incorporating AI-Driven Strategies in DevSecOps for Robust Cloud Security 8, no. 11 (2023): 7. https://doi.org/10.5281/zenodo.10361289.

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This research paper explores the integrationof artificial intelligence (AI) strategies into theDevSecOps framework to enhance cloud securityincluding using an analytical techniques, leveraging bothquantitative and qualitative methodologies to assess theefficacy of AI-solutions in mitigating security risks. Thisstudies contributes to a nuanced knowledge of thesymbiotic relationship among AI and DevSecOps,shedding light on how combining artificial intelligencetechnology can improves security. The paperadditionally discusses implications and challengesrelated to implementing AI in DevSecOps workf

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Sharma, Vivek. "AI-DRIVEN CLOUD INFRASTRUCTURE: ADVANCES IN KUBERNETES AND SERVERLESS COMPUTING." international journal of advanced research in computer science 16, no. 2 (2025): 65–70. https://doi.org/10.26483/ijarcs.v16i2.7234.

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Artificial Intelligence has been integrated into cloud infrastructure, making it revolutionizing modern computing by automating, scaling, and efficiency. The first of these is Kubernetes and the second is serverless computing. Kubernetes, a container orchestration platform, benefits from AI-driven enhancements in workload scheduling, auto-scaling, and resource optimization. By combining AI based predictive analytics with container deployment, overhead is reduced in terms of the operational overhead as well as the fault tolerance. However, serverless computing takes away the management of infra

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Khadarvali Shaik. "AI-driven data governance for multi-cloud environments." International Journal of Science and Research Archive 15, no. 2 (2025): 773–88. https://doi.org/10.30574/ijsra.2025.15.2.1284.

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This document investigates how Artificial Intelligence (AI) helps reinforce data governance across multiple cloud settings. Organizational adoption of multi-cloud platforms leads to mounting difficulties for proper data management across various platforms. AI technology provides innovative solutions that help organizations solve issues about data-scattering compliance risks and security vulnerabilities. The research investigated data governance optimization through AI automation using a combination of case studies with industry experts' data analytics and systematic interviews. The study demon

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Deepthi Kamidi. "Leveraging Artificial Intelligence for Enhanced Data Protection: A Comprehensive Review of Cloud Security amid Emerging threats." Journal of Information Systems Engineering and Management 10, no. 43s (2025): 16–26. https://doi.org/10.52783/jisem.v10i43s.8291.

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Cloud computing offers scalable, adaptable, and affordable solutions that spur innovation across multiple industries, it has fundamentally changed how industries function. However, with this widespread adoption comes the growing challenge of protecting sensitive data, especially as more sophisticated cyberattacks become common. Advanced Persistent challenges (APTs), insider assaults, data breaches, and Distributed Denial of Service (DDoS) attacks are just a few of the challenges that modern cloud environments must contend with. These threats highlight flaws in conventional security paradigmsTh

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Santosh Datta Bompally. "Comprehensive approach to cloud security posture management: From infrastructure as code to AI-driven monitoring." Open Access Research Journal of Engineering and Technology 8, no. 2 (2025): 081–87. https://doi.org/10.53022/oarjet.2025.8.2.0046.

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Cloud Security Posture Management (CSPM) has emerged as a critical framework for safeguarding multi-cloud environments against growing threats while maintaining operational efficiency. This article comprehensively examines CSPM evolution from fundamental principles to advanced capabilities leveraging artificial intelligence. The discussion encompasses four essential components: Infrastructure as Code scanning, Cloud Native Security, Reactive Security, and Security Monitoring, detailing how each contributes to a robust security framework. Integrating these components across the cloud lifecycle

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Oduri, Sailesh. "Future-Proofing Cloud Networks with AI and Security Engineering." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 9, no. 2 (2019): 794–800. http://dx.doi.org/10.61841/turcomat.v9i2.14736.

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Future-proofing cloud networks is crucial as organizations seek to adapt to evolving technology and increasing cyber threats.his article explores the integration of artificial intelligence (AI) and security engineering as key strategies for ensuring the resilience and efficiency of cloud networks. AI enhances network management through automation, traffic optimization, and predictive analytics, while machine learning significantly bolsters security by enabling real-time threat detection and response. Security engineering principles, including encryption, access control, and advanced techniques

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Ikeoluwa Kolawole. "Leveraging Cloud-based ai and zero trust architecture to enhance U. S. cybersecurity and counteract foreign threats." World Journal of Advanced Research and Reviews 25, no. 3 (2025): 006–25. https://doi.org/10.30574/wjarr.2025.25.3.0635.

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The increasing sophistication of cyber threats targeting U.S. national security, critical infrastructure, and financial systems necessitates a proactive, AI-driven cybersecurity strategy. Traditional security models relying on perimeter-based defenses are insufficient against state-sponsored attacks, ransomware, and advanced persistent threats (APTs). This paper explores the transformative potential of cloud-based artificial intelligence (AI) and Zero Trust Architecture (ZTA) in fortifying U.S. cybersecurity and mitigating foreign threats. Cloud-based AI enhances threat detection, real-time an

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Vinay Kumar Kasula, Akhila Reddy Yadulla, Bhargavi Konda, and Mounica Yenugula. "Fortifying cloud environments against data breaches: A novel AI-driven security framework." World Journal of Advanced Research and Reviews 18, no. 3 (2023): 1644–57. https://doi.org/10.30574/wjarr.2023.18.3.1180.

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As cloud computing continues to dominate the modern technological landscape, organizations face growing challenges in preventing data breaches and sophisticated cyber threats. The increasing complexity and scale of cloud environments require advanced security mechanisms to address evolving threats. This paper introduces "SecureCloudAI," a cutting-edge AI-driven security framework designed to fortify sensitive data within cloud infrastructures. SecureCloudAI leverages a hybrid approach that combines machine learning models like Random Forest and deep learning techniques, including Long Short-Te

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Vinay Kumar Kasula, Akhila Reddy Yadulla, Bhargavi Konda, and Mounica Yenugula. "Fortifying cloud environments against data breaches: A novel AI-driven security framework." World Journal of Advanced Research and Reviews 24, no. 1 (2024): 1613–26. http://dx.doi.org/10.30574/wjarr.2024.24.1.3194.

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As cloud computing continues to dominate the modern technological landscape, organizations face growing challenges in preventing data breaches and sophisticated cyber threats. The increasing complexity and scale of cloud environments require advanced security mechanisms to address evolving threats. This paper introduces "SecureCloudAI," a cutting-edge AI-driven security framework designed to fortify sensitive data within cloud infrastructures. SecureCloudAI leverages a hybrid approach that combines machine learning models like Random Forest and deep learning techniques, including Long Short-Te

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Raakesh Dhanasekaran. "Generative AI Integration with Cloud Services: Revolutionizing Cybersecurity Frameworks." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 1619–25. https://doi.org/10.30574/wjaets.2025.15.3.1086.

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The integration of generative artificial intelligence with cloud computing has fundamentally transformed cybersecurity frameworks, enabling unprecedented capabilities in threat detection and automated incident response. This technological convergence allows organizations to shift from reactive to proactive security postures through sophisticated anomaly detection and predictive analytics. Major cloud providers have embedded AI-driven security tools that analyze vast datasets to identify subtle patterns indicative of potential threats before they materialize into breaches. While delivering sign

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Anbarasu Aladiyan. "AI-driven threat intelligence: A global perspective on cloud security risks and mitigation strategies." Global Journal of Engineering and Technology Advances 23, no. 2 (2025): 031–36. https://doi.org/10.30574/gjeta.2025.23.2.0146.

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This article explores the transformative role of artificial intelligence in enhancing cloud security through advanced threat intelligence capabilities. As organizations increasingly migrate to cloud environments, they face evolving security challenges that traditional approaches struggle to address effectively. AI-driven threat intelligence offers powerful solutions by leveraging machine learning and deep learning techniques to analyze vast datasets, detect anomalous behaviors, and predict potential threats with greater accuracy than conventional methods. It examines global variations in cloud

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Raghuwanshi, Prashis. "AI-Driven Identity and Financial Fraud Detection for National Security." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 7, no. 01 (2024): 38–51. https://doi.org/10.60087/jaigs.v7i01.294.

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In the digital age, financial systems and personal identities are increasingly targeted for fraud by sophisticated actors, including criminal organizations, terrorist groups, and rogue states. The U.S., as a global financial hub, faces unique challenges in mitigating these threats, which have direct implications for national security. The rise of cloud-native AI-based systems offers a powerful solution for detecting and preventing identity and financial fraud at scale. Leveraging artificial intelligence (AI) in a cloud-native environment enables federal agencies and private-sector institutions

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PARASHURAM, MALOTH. "The Evolution of Cloud Security Protecting Data in a Distributed Environment." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–9. https://doi.org/10.55041/isjem02618.

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The rapid adoption of cloud computing has transformed how organizations store, process, and manage data, shifting from centralized infrastructures to highly distributed environments. This evolution has necessitated a parallel advancement in cloud security strategies to address emerging threats, regulatory demands, and architectural complexities. Initially, cloud security relied on perimeter-based defenses, such as firewalls and VPNs, which proved insufficient as architectures evolved toward hybrid, multi-cloud, and edge computing models. Modern security paradigms now emphasize Zero Trust princ

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Manvitha Potluri. "AI-driven zero trust security for Kubernetes and multi-cloud deployments." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 394–404. https://doi.org/10.30574/wjaets.2025.15.2.0559.

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The rapid evolution of cloud-native infrastructures has exposed critical vulnerabilities in traditional security models, particularly in multi-cloud Kubernetes environments where distributed applications face increasingly sophisticated threats. Zero Trust Security principles offer a promising foundation, yet conventional implementations struggle with the dynamic nature of containerized workloads and cross-cluster communications. This article introduces AI-Enhanced Zero Trust for Kubernetes and Multi-Cloud, a framework that leverages machine learning to transform static security policies into a

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Mamidi, Sundeep Reddy. "The Role of AI and Machine Learning in Enhancing Cloud Security." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 3, no. 1 (2024): 403–17. http://dx.doi.org/10.60087/jaigs.v3i1.161.

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Cloud computing has transformed how organizations store, process, and manage data, offering unparalleled flexibility and scalability. However, the rise in cyber threats presents significant challenges to maintaining robust cloud security. This chapter explores the crucial role that Artificial Intelligence (AI) and Machine Learning (ML) play in enhancing cloud security. By leveraging AI and ML capabilities, organizations can proactively detect, mitigate, and respond to evolving cyber threats, ultimately strengthening their cloud infrastructure. AI-driven techniques enable security systems to re

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Geetesh, Sanodia. "Leverage AI to Improve Cloud Transformation." Journal of Scientific and Engineering Research 11, no. 8 (2024): 95–100. https://doi.org/10.5281/zenodo.13473421.

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Cloud transformation has become a critical component of digital transformation strategies, enabling organizations to enhance agility, scalability, and efficiency. As cloud environments grow increasingly complex, the integration of Artificial Intelligence (AI) offers powerful solutions for automating processes, optimizing resource allocation, and enhancing security. This review paper explores the intersection of AI and cloud transformation, detailing how AI-driven tools and techniques are revolutionizing cloud migration, management, and development. Through detailed case studies, the paper high

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Geetesh, Sanodia. "Leverage AI to Improve Cloud Transformation." Journal of Scientific and Engineering Research 11, no. 8 (2024): 95–100. https://doi.org/10.5281/zenodo.13473421.

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Abstract:

Cloud transformation has become a critical component of digital transformation strategies, enabling organizations to enhance agility, scalability, and efficiency. As cloud environments grow increasingly complex, the integration of Artificial Intelligence (AI) offers powerful solutions for automating processes, optimizing resource allocation, and enhancing security. This review paper explores the intersection of AI and cloud transformation, detailing how AI-driven tools and techniques are revolutionizing cloud migration, management, and development. Through detailed case studies, the paper high

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Geetesh, Sanodia. "Leverage AI to Improve Cloud Transformation." Journal of Scientific and Engineering Research 11, no. 8 (2024): 95–100. https://doi.org/10.5281/zenodo.13473421.

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Abstract:

Cloud transformation has become a critical component of digital transformation strategies, enabling organizations to enhance agility, scalability, and efficiency. As cloud environments grow increasingly complex, the integration of Artificial Intelligence (AI) offers powerful solutions for automating processes, optimizing resource allocation, and enhancing security. This review paper explores the intersection of AI and cloud transformation, detailing how AI-driven tools and techniques are revolutionizing cloud migration, management, and development. Through detailed case studies, the paper high

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