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Relevant bibliographies by topics / AI-Driven Cloud Security

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Academic literature on the topic 'AI-Driven Cloud Security'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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

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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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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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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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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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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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Book chapters on the topic "AI-Driven Cloud Security"

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Sharma, Mansi, David Raymond, Induni Weerarathna, Praveen Kumar, and Abeny Ramadan Chadar. "Cloud Security and Artificial Intelligence." In Handbook of AI-Driven Threat Detection and Prevention. CRC Press, 2025. https://doi.org/10.1201/9781003521020-9.

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Al-rubaye, Maitham, and Atakan Aral. "Performance Analysis of AI-Driven Security Models in the Cloud-Edge Continuum for Monitoring Critical Infrastructures." In Lecture Notes on Data Engineering and Communications Technologies. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87778-0_27.

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Haque, Kazi Nymul, Johirul Islam, Ijaz Ahmad, and Erkki Harjula. "Decentralized Pub/Sub Architecture for Real-Time Remote Patient Monitoring: A Feasibility Study." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59080-1_4.

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AbstractThe confluence of the Internet of Things (IoT) within the healthcare sector, called Internet of Medical Things (IoMT), has ushered in a transformative approach to real-time patient monitoring. Traditional methods that typically involve the direct transmission of medical sensor data to the cloud, falter under the constraints of medical IoT devices. In response, Multi-access Edge Computing (MEC), as defined by the European Telecommunications Standards Institute (ETSI), brings forth an innovative solution by relocating computing resources closer to the origin of data. However, MEC alone d

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Sharma, Garima, Jaspreet Singh, and Priyanka Maan. "AI-driven cloud storage service for securing IoT data." In Artificial Intelligence for Blockchain and Cybersecurity Powered IoT Applications. CRC Press, 2024. http://dx.doi.org/10.1201/9781003497585-8.

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Lampathaki, Fenareti, Evmorfia Biliri, Tasos Tsitsanis, Kostas Tsatsakis, Dimitris Miltiadou, and Konstantinos Perakis. "Toward an Energy Data Platform Design: Challenges and Perspectives from the SYNERGY Big Data Platform and AI Analytics Marketplace." In Data Spaces. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98636-0_14.

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AbstractToday, the need for “end-to-end” coordination between the electricity sector stakeholders, not only in business terms but also in securely exchanging real-time data, is becoming a necessity to increase electricity networks’ stability and resilience while satisfying individual operational optimization objectives and business case targets of all stakeholders. To this end, the SYNERGY energy data platform builds on state-of-the-art data management, sharing, and analytics technologies, driven by the actual needs of the electricity data value chain. This paper will describe the layered SYNE

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Vashishth, Tarun Kumar, Vikas Sharma, Kewal Krishan Sharma, Bhupendra Kumar, Sachin Chaudhary, and Rajneesh Panwar. "Enhancing Cloud Security." In Improving Security, Privacy, and Trust in Cloud Computing. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1431-9.ch004.

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Cloud computing has revolutionized the way organizations store, process, and manage data, offering flexibility and scalability. However, the rise in cyber threats poses significant challenges to maintaining robust cloud security. This chapter delves into the pivotal role that Artificial Intelligence (AI) and Machine Learning (ML) play in enhancing cloud security. By harnessing the capabilities of AI and ML, organizations can proactively detect, mitigate, and respond to evolving cyber threats, ultimately fortifying their cloud infrastructure.AI-driven techniques empower security systems to reco

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Jyothsna, V., E. Sandhya, Khaja Baseer Kamalapuram, and P. Bhasha. "AI-Driven Threat Detection in Cloud Environments." In Advances in Information Security, Privacy, and Ethics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-6859-6.ch012.

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In cloud computing, integrating artificial intelligence (AI) is crucial for enhancing cybersecurity. As organizations move to the cloud, they encounter advanced threats that traditional security measures often miss. AI, through machine learning and deep learning, significantly improves threat detection and response times. This chapter discusses AI-based systems like anomaly detection, predictive analytics, and automated responses, highlighting their effectiveness in real-world scenarios. It also addresses challenges such as data privacy, quality training data, and adversarial attacks. Future t

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Goel, Pawan Kumar, Avinash Kumar Sharma, Km Komal, and Lakshay Singh Mahur. "Security and Privacy Mechanisms in AI-Driven Cloud Platforms." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-9694-0.ch007.

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The rise of AI-driven cloud platforms has highlighted the need for robust security and privacy mechanisms. These platforms handle sensitive data and complex AI workloads, making them targets for cyber threats. A new solution combines multi-layered encryption with AI-enhanced anomaly detection for secure data processing and storage. This is the first of its kind to integrate advanced encryption with real-time AI-based threat detection for cloud environments. Preliminary tests show significant improvements in data security, reducing vulnerability by over 40%. This breakthrough strengthens the se

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Sangeetha, G., and Ajit Khosla. "Securing AI-Driven Haptic Healthcare Systems in Cloud Environments." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-2307-7.ch012.

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A new computational paradigm is known as “cloud computing” provides internet-based, highly scalable distributed computing systems where computational resources are made available as a service. One of the essential elements of the cloud model is virtualization. Despite the potential benefits of cloud computing, there are still concerns about data security. Data cache-based attacks are one of the main threats. The cloud model is thought to be seriously threatened by cache attacks. Due to the existence of LLC, the attacks operate across cores in a cross-VM environment and are successful in recove

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Antwi, Noble Worlanyo. "Threat Detection in Multi-Cloud Environments." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-4252-8.ch004.

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This chapter investigates the critical role of threat detection in securing multi-cloud environments, a rapidly evolving area as organizations adopt platforms such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). It analyzes traditional security mechanisms, including firewalls and intrusion detection systems, highlighting their limitations in cloud-native infrastructures. The chapter explores advanced practices such as Artificial Intelligence (AI)-driven analytics, Machine Learning (ML), User and Entity Behavior Analytics (UEBA), Zero Trust Architecture (ZTA), an

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Conference papers on the topic "AI-Driven Cloud Security"

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Jewanth, Sirigiri, Chittela Tarun Reddy, Ravi Rastogi, Billipalli Hemanth Reddy, and Nakka Bhanu Prakash Reddy. "AI-Driven Anomaly Detection in Cloud Network Security." In 2024 International Conference on Advances in Computing, Communication and Materials (ICACCM). IEEE, 2024. https://doi.org/10.1109/icaccm61117.2024.11059043.

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Seth, Dhruv Kumar, Karan Kumar Ratra, and Aneeshkumar P. Sundareswaran. "AI and Generative AI-Driven Automation for Multi-Cloud and Hybrid Cloud Architectures: Enhancing Security, Performance, and Operational Efficiency." In 2025 IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, 2025. https://doi.org/10.1109/ccwc62904.2025.10903928.

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Surya, S., D. Santhakumar, Anuj Tyagi, Kiran Onapakala, V. B. Thurai Raaj, and N. Krishna Kumar. "AI-Driven Threat Detection: Implementing Multi-Layer Security Networks in Cloud Environments." In 2025 International Conference on Pervasive Computational Technologies (ICPCT). IEEE, 2025. https://doi.org/10.1109/icpct64145.2025.10941038.

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Prabu, K., Vanitha A, R. Radhakrishnan, Rajakumar P, E. Thenmozhi, and S. P. Ramesh. "Enhancing Cloud Security with AI-Driven Anomaly Detection for Zero-Day Threats." In 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N). IEEE, 2024. https://doi.org/10.1109/icac2n63387.2024.10895696.

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Raman, Ramakrishnan, Vikram Kumar, Biju G. Pillai, Dhaval Rabadiya, Smruti Patre, and R. Meenakshi. "AI-Driven Remote Parkinson’s Diagnosis with BPNN Framework and Cloud-Based Data Security." In 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS). IEEE, 2024. http://dx.doi.org/10.1109/ickecs61492.2024.10616436.

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Yasani, Rajashekar Reddy, Putalpattu Muni Prasad, Pattlola Srinivas, N. V. Raja Sekhar Reddy, Parag Jawarkar, and Vedaprada Raghunath. "AI-Driven Solutions for Cloud Security Implementing Intelligent Threat Detection and Mitigation Strategies." In 2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC). IEEE, 2024. https://doi.org/10.1109/icec59683.2024.10837032.

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V, Prakash, and J. Anitha Gnanaselvi. "An AI-Driven Intelligent Threat-Responsive Shard Management System ITRSMS for Adaptive Cloud Security." In 2025 International Conference on Frontier Technologies and Solutions (ICFTS). IEEE, 2025. https://doi.org/10.1109/icfts62006.2025.11031848.

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Krishna Tungala, H. N. V. Sai Murali, Ganapathi Yeleswarapu, Mahesh Shivnatri, and Sridhar Kumar Irujolla. "A Zero Trust Framework with AI-Driven Identity and Intrusion Detection for Multi-Cloud MLOps." In 2025 13th International Symposium on Digital Forensics and Security (ISDFS). IEEE, 2025. https://doi.org/10.1109/isdfs65363.2025.11012077.

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Kumar, M. Lenin, M. Amanullah, Revatthy Krishnamurthy, N. Mohana Suganthi, and S. A. Kalaiselvan. "Revolutionizing Financial Cloud Services: AI and Blockchain-driven Resource Allocation for Maximized Transaction Speed and Security." In 2024 Third International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT). IEEE, 2024. http://dx.doi.org/10.1109/iceeict61591.2024.10718396.

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Viharika, Sadargari, and NAlangudi Balaji. "AI-Driven Intrusion Detection Systems in Cloud Infrastructures: A Comprehensive Review of Hybrid Security Models and Future Directions." In 2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS). IEEE, 2024. https://doi.org/10.1109/icuis64676.2024.10866856.

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Reports on the topic "AI-Driven Cloud Security"

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Pasupuleti, Murali Krishna. Securing AI-driven Infrastructure: Advanced Cybersecurity Frameworks for Cloud and Edge Computing Environments. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv225.

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Abstract: The rapid adoption of artificial intelligence (AI) in cloud and edge computing environments has transformed industries by enabling large-scale automation, real-time analytics, and intelligent decision-making. However, the increasing reliance on AI-powered infrastructures introduces significant cybersecurity challenges, including adversarial attacks, data privacy risks, and vulnerabilities in AI model supply chains. This research explores advanced cybersecurity frameworks tailored to protect AI-driven cloud and edge computing environments. It investigates AI-specific security threats,

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Pasupuleti, Murali Krishna. Next-Generation Extended Reality (XR): A Unified Framework for Integrating AR, VR, and AI-driven Immersive Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv325.

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Abstract: Extended Reality (XR), encompassing Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), is evolving into a transformative technology with applications in healthcare, education, industrial training, smart cities, and entertainment. This research presents a unified framework integrating AI-driven XR technologies with computer vision, deep learning, cloud computing, and 5G connectivity to enhance immersion, interactivity, and scalability. AI-powered neural rendering, real-time physics simulation, spatial computing, and gesture recognition enable more realistic and adap

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