The Development of Scalable Artificial Intelligence Algorithms for the Protection of Kubernetes Clusters in Multiple Clouds
Keywords:
Kubernetes, multi-cloud, AI algorithms, security, anomaly detection, machine learning, cloud infrastructure, predictive analytics, automation, blockchainAbstract
In multi-cloud environments, the increasing usage of Kubernetes has presented new issues in terms of assuring security. This is especially true as the scale of these systems continues to improve. It is difficult for traditional security methods to keep up with the ever-changing requirements of multi-cloud infrastructures, which is why scalable algorithms powered by artificial intelligence are required. In this research, we investigate the design and implementation of artificial intelligence-driven security algorithms that are tailor-made for Kubernetes clusters that span multiple clouds. Artificial intelligence has the potential to improve security by enabling real-time threat detection, resource optimization, and automated response. This is accomplished through the utilization of machine learning, anomaly detection, and predictive analytics. In this article, artificial intelligence algorithms are used to secure multi-cloud Kubernetes settings. The paper explores key methodologies, difficulties, and case studies that illustrate the usefulness of these algorithms. In conclusion, the paper offers some insights into future trends and the manner in which artificial intelligence might be integrated with developing technologies like blockchain in order to improve security.
Downloads
References
Nischay Reddy Mitta, “AI-Enhanced Real-Time Monitoring and Control Systems for Manufacturing Operations: Implementing Machine Learning for Dynamic Process Adjustment, Anomaly Detection, and Performance Optimization”, Edinburg J. of Nat. Lang. Proc. and AI, vol. 9, pp. 14–54, Apr. 2025
Sateesh Kumar Nallamala, “AI-Driven Computational Tools for Accelerated Biomarker Discovery: Developing Machine Learning Models for Identifying Disease-Specific Biomarkers and Enhancing Diagnostic Accuracy ”, Los Angeles J Intell Syst Pattern Rec, vol. 5, pp. 54–95, May 2025
VinayKumar Dunka, “AI-Enabled Decision Support Systems for Retail Merchandising: Combining Machine Learning and Optimization Algorithms for Product Placement, Inventory Allocation, and Space Utilization”, American J Cognit Comput AI Syst, vol. 9, pp. 14–53, Apr. 2025
Mohammed, Hameed Ul Hassan. "A Machine Learning-Driven Placement Optimization Framework for VLSI Physical Design." Journal of Computational Analysis and Applications 31.4 (2023).
Hameed, Shahul, Marcus Rodriguez, and Takudzwa Fadziso. "AI-Augmented Test Automation Pipelines for Continuous Delivery in Large-Scale Software Systems." American Journal of Cognitive Computing and AI Systems 4 (2020): 118-132.
Sake, Lekhya Sai, et al. "Deep Learning Architectures for Fraudulent Insurance Claim Detection in Health-Finance Ecosystems." Los Angeles Journal of Intelligent Systems and Pattern Recognition 1 (2021): 380-414.
Maruthi Rohit Ayyagari, Pavan Punukollu, Sreeharsha Burugu, Raghuveer Prasad Yerneni, Sowmya Gudekota, and Midhun Punukollu, “Developing AI-Driven Consumer Insights for Omni-Channel Retail Strategies: Leveraging Machine Learning and Advanced Analytics for Customer Journey Mapping, Behavior Prediction, and Experience Personalization”, Edinburg J. of Nat. Lang. Proc. and AI, vol. 9, pp. 55–97, May 2025
Pavan Punukollu, Raghuveer Prasad Yerneni, Sreeharsha Burugu, Midhun Punukollu, Sowmya Gudekota, Nischay Reddy Mitta, Sateesh Kumar Nallamala, & VinayKumar Dunka. (2022). AI-Powered Warehouse Automation in Retail Supply Chains: Developing Machine Learning Models for Robotic Process Automation, Inventory Management, and Order Fulfillment. Newark Journal of Human-Centric AI and Robotics Interaction, 2, 397-435.
Sowmya Gudekota et. al, “AI-Powered Supply Chain Risk Management in Manufacturing: Using Machine Learning to Identify, Assess, and Mitigate Risks Across Global Supply Chains”, Los Angeles J Intell Syst Pattern Rec, vol. 1, pp. 342–379, Feb. 2021
Midhun Punukollu, “AI-Driven Genomic Sequencing: Revolutionizing Personalized Medicine Through Predictive Analytics ”, Los Angeles J Intell Syst Pattern Rec, vol. 2, pp. 293–329, Jun. 2022
Sricharan Kodali, “Enhancing Customer Experience in Digital Banking with AI-Driven Personalization Engines: Combining Machine Learning and NLP for Real-Time User Interaction, Behavior Analysis, and Service Optimization”, American J Data Sci Artif Intell Innov, vol. 2, pp. 609–644, Feb. 2022
Aishwarya Selvam, “Federated Machine Learning for Collaborative Drug Discovery Without Data Sharing ”, The Distrib. Learning & Br. App. Sci. Res., vol. 6, pp. 1355–1398, Mar. 2020
Venkata Siva Prakash Nimmagadda, “Optimizing Assembly Line Layouts with AI for Increased Productivity and Cost Reduction ”, J. of Art. Int. Research and App., vol. 1, no. 1, pp. 798–836, Jan. 2021
Shubha Vakulabharanam, “Predictive Analytics for QA Metrics: Leveraging AI to Forecast Testing Outcomes and Resource Needs ”, J. of Art. Int. Research and App., vol. 1, no. 1, pp. 679–715, Jan. 2021
Nischay Reddy Mitta, “AI-Powered Decision Support Systems for Manufacturing Operations Management: Leveraging Machine Learning to Enhance Strategic Planning, Resource Allocation, and Production Scheduling”, Art. Intel. Mach. Learn. Auto. Sys., vol. 9, pp. 96–136, Mar. 2025
Sateesh Kumar Nallamala, “AI-Enhanced Systems Biology Approaches for Metabolic Network Reconstruction: Developing Machine Learning Models for Enzyme Activity Prediction, Pathway Analysis, and Metabolic Engineering”, European Journal of Quantum Computing and Intelligent Agents, vol. 8, pp. 44–81, Jun. 2024
Pavan Punukollu, Sreeharsha Burugu, Raghuveer Prasad Yerneni, Midhun Punukollu, & Sowmya Gudekota. (2022). Developing AI-Driven Predictive Models for Credit Risk Forecasting: Leveraging Machine Learning Techniques for Enhancing Decision-Making in Lending Practices. European Journal of Quantum Computing and Intelligent Agents, 6, 135-169.
Sowmya Gudekota et. al, “Application of AI in Drug Discovery for Complex Diseases: Leveraging Deep Learning to Identify Novel Drug Targets, Predict Drug Efficacy, and Uncover Hidden Mechanisms of Action ”, American J Cognit Comput AI Syst, vol. 6, pp. 109–145, Oct. 2022
Midhun Punukollu, “Deep Learning Techniques for Streamlining Drug Development Timelines in the Pharmaceutical Industry ”, Newark J. Hum. Centric AI Robot Inter., vol. 2, pp. 436–475, Apr. 2022
Sricharan Kodali, “Harnessing the Power of Artificial Intelligence and Advanced Data Science Techniques for Predictive Analytics in Healthcare: A Comprehensive Exploration for Improved Patient Outcomes, Operational Efficiency, and Data-Driven Decision-Making”, Essex Journal of AI Ethics and Responsible Innovation, vol. 3, pp. 566–593, Aug. 2023
Aishwarya Selvam, “Generative Adversarial Networks for Synthetic Data Creation in Manufacturing Process Simulations ”, J. of Art. Int. Research and App., vol. 1, no. 1, pp. 716–757, Feb. 2021
Venkata Siva Prakash Nimmagadda, “Personalizing In-Store Experiences Using Machine Learning and IoT Integration ”, American J Data Sci Artif Intell Innov, vol. 1, pp. 876–912, Mar. 2021
Pavan Punukollu, Sreeharsha Burugu, Sowmya Gudekota, Midhun Punukollu, & Raghuveer Prasad Yerneni. (2023). AI-Powered Robotics for Precision Manufacturing: Developing Machine Learning Models to Enhance Robotic Automation, Improve Accuracy, and Enable Complex Assembly Tasks. Essex Journal of AI Ethics and Responsible Innovation, 3, 355-394.
Shubha Vakulabharanam, “Sentiment Analysis of Customer Reviews to Enhance Insurance Product Development ”, American J Data Sci Artif Intell Innov, vol. 1, pp. 801–838, Jan. 2021
Sowmya Gudekota et. al, “Artificial Intelligence in Financial Compliance: Utilizing Machine Learning Models for Regulatory Reporting, Anti-Money Laundering (AML), and Know Your Customer (KYC) Procedures”, Art. Intel. Mach. Learn. Auto. Sys., vol. 6, pp. 78–115, Aug. 2022
Midhun Punukollu, “Machine Learning Models for Predicting Pharmacokinetics and Drug Safety Profiles ”, American J Data Sci Artif Intell Innov, vol. 3, pp. 389–429, Apr. 2023
Sricharan Kodali, “Utilizing AI for Real-Time Supply Chain Visibility: Developing Machine Learning Models for Predictive Analytics, Inventory Tracking, and Disruption Management”, J. Artif. Intell. Mach. Learn. Stud., vol. 4, pp. 178–218, Dec. 2020
Aishwarya Selvam, “Graph Neural Networks for Fraud Detection in Large-Scale Banking Networks ”, American J Data Sci Artif Intell Innov, vol. 1, pp. 839–875, Mar. 2021
Venkata Siva Prakash Nimmagadda, “Predictive Analytics for Antimicrobial Resistance Monitoring in Pharmaceutical Drug Development ”, Essex Journal of AI Ethics and Responsible Innovation, vol. 1, pp. 676–715, Apr. 2021
Shubha Vakulabharanam, “Sentiment-Aware Trading Algorithms: Using Machine Learning to Leverage Market Sentiment for Trading Strategies ”, Essex Journal of AI Ethics and Responsible Innovation, vol. 1, pp. 601–637, Jan. 2021
Pavan Punukollu, Sreeharsha Burugu, Raghuveer Prasad Yerneni, Midhun Punukollu, & Sowmya Gudekota. (2023). AI-Powered Virtual Screening Frameworks for Accelerating Drug Discovery: Utilizing Deep Learning for Molecular Docking, Compound Library Prioritization, and ADMET Prediction . Los Angeles Journal of Intelligent Systems and Pattern Recognition, 3, 447-484
Sowmya Gudekota, Midhun Punukollu, Raghuveer Prasad Yerneni, Pavan Punukollu, and Sreeharsha Burugu, “Developing AI-Based Real-Time Genomic Data Analysis Pipelines for Precision Medicine: Leveraging Machine Learning for Variant Calling, Functional Annotation, and Clinical Reporting ”, Edinburg J. of Nat. Lang. Proc. and AI, vol. 6, pp. 128–169, Feb. 2022
Midhun Punukollu, “Leveraging NLP for Biological Literature Mining and Protein Annotation in Life Sciences ”, Essex Journal of AI Ethics and Responsible Innovation, vol. 1, pp. 563–600, May 2021
Sricharan Kodali, “Leveraging AI for Real-Time Clinical Decision Support Systems in Oncology: Utilizing Machine Learning for Cancer Diagnosis, Prognosis, and Treatment Planning Based on Multi-Modal Patient Data”, Los Angeles J Intell Syst Pattern Rec, vol. 3, pp. 521–558, Aug. 2023
Aishwarya SelvamIndependent Researcher, USA, “Improving Anti-Money Laundering (AML) Processes with Advanced Machine Learning AlgorithmsBanking and regulatory AML use advanced ML. This study shows ML algorithms may detect suspicious transaction patterns, improve compliance, and reduce AML framework fa”, Essex Journal of AI Ethics and Responsible Innovation, vol. 1, pp. 638–675, Mar. 2021
Venkata Siva Prakash Nimmagadda, “Predictive Maintenance for Retail Supply Chain Equipment Using Machine Learning Models ”, Los Angeles J Intell Syst Pattern Rec, vol. 2, pp. 405–445, Jan. 2022
Shubha Vakulabharanam, “Sentiment-Driven Inventory Management Using NLP for Seasonal Demand in Retail ”, Los Angeles J Intell Syst Pattern Rec, vol. 2, pp. 330–367, Jan. 2022
Pavan Punukollu, “Development of AI-Based Precision Medicine Platforms for Oncology: Utilizing Machine Learning for Tumor Profiling, Personalized Drug Selection, and Treatment Outcome Prediction”, Los Angeles J Intell Syst Pattern Rec, vol. 3, pp. 485–520, Apr. 2024
Sowmya Gudekota, Pavan Punukollu, Sreeharsha Burugu, Raghuveer Prasad Yerneni, and Midhun Punukollu, “Developing AI-Enhanced Cybersecurity Frameworks for Connected and Autonomous Vehicles: Utilizing Machine Learning Models for Threat Detection, Intrusion Prevention, and Secure Communication Protocols”, J. Artif. Intell. Mach. Learn. Stud., vol. 6, pp. `104–148, Apr. 2022
Midhun Punukollu, “Leveraging Machine Learning for Real-Time Predictive Maintenance in Smart Manufacturing Ecosystems ”, Los Angeles J Intell Syst Pattern Rec, vol. 4, pp. 544–585, May 2024
Sricharan Kodali, “Exploring the Role of Generative Adversarial Networks (GANs) in Financial Data Augmentation: Enhancing Predictive Accuracy and Robustness in AI-Based Risk Modeling”, American J Data Sci Artif Intell Innov, vol. 3, pp. 305–339, Oct. 2023
Aishwarya Selvam, “Integration of AI with CRISPR-Cas9 Systems for Targeted Gene Editing Research ”, Los Angeles J Intell Syst Pattern Rec, vol. 2, pp. 368–404, Mar. 2022
Venkata Siva Prakash Nimmagadda, “Reinforcement Learning for Dynamic Test Execution Planning in Continuous Integration Pipelines”, Newark J. Hum. Centric AI Robot Inter., vol. 2, pp. 551–587, Apr. 2022
Shubha Vakulabharanam, “Simulating Biochemical Pathways with AI for Improved Drug Target Validation ”, Newark J. Hum. Centric AI Robot Inter., vol. 2, pp. 476–511, Jan. 2022
Pavan Punukollu, “Development of AI-Based Systems for Adaptive Manufacturing Processes: Using Machine Learning Algorithms to Optimize Production Parameters and Enhance Flexibility in Manufacturing Lines ”, Newark J. Hum. Centric AI Robot Inter., vol. 4, pp. 214–256, Apr. 2024
Sowmya Gudekota, Raghuveer Prasad Yerneni, Pavan Punukollu, Sreeharsha Burugu, and Midhun Punukollu, “AI-Powered Personalized Medicine Platforms for Cardiovascular Diseases: Utilizing Machine Learning for Risk Assessment, Treatment Optimization, and Predictive Modeling of Cardiovascular Events”, American J Data Sci Artif Intell Innov, vol. 3, pp. 266–304, Jun. 2023
Midhun Punukollu, “Deep Learning Algorithms for Optimizing Production Line Automation in Advanced Manufacturing Systems ”, Newark J. Hum. Centric AI Robot Inter., vol. 4, pp. 300–339, Jun. 2024
Aishwarya Selvam, “Knowledge Graphs for Integrating Multi-Omics Data in Pharmaceutical Research ”, Newark J. Hum. Centric AI Robot Inter., vol. 2, pp. 512–550, Jan. 2022
Venkata Siva Prakash Nimmagadda, “Sentiment Analysis for Automated Validation of User Feedback in QA Processes ”, The Distrib. Learning & Br. App. Sci. Res., vol. 9, pp. 824–864, Feb. 2023
Shubha Vakulabharanam, “Using Machine Learning for Real-Time Emission Monitoring in Smart Automobiles ”, The Distrib. Learning & Br. App. Sci. Res., vol. 9, pp. 741–780, Apr. 2023
Venkata Siva Prakash Nimmagadda, “Using AI to Improve Success Rates of Clinical Trials Through Advanced Patient Stratification Techniques ”, American J Data Sci Artif Intell Innov, vol. 4, pp. 342–379, Feb. 2024
Sowmya Gudekota, Pavan Punukollu, Sreeharsha Burugu, Midhun Punukollu, and Raghuveer Prasad Yerneni, “Application of AI for Optimizing Energy Consumption in Manufacturing Facilities: Leveraging Machine Learning to Analyze Energy Usage Patterns and Implement Energy Efficiency Strategies”, Newark J. Hum. Centric AI Robot Inter., vol. 3, pp. 461–495, Dec. 2023
Venkata Siva Prakash Nimmagadda, “Transfer Learning Techniques for Domain-Specific Quality Assurance Testing ”, J. of Art. Int. Research and App., vol. 3, no. 1, pp. 1170–1208, Feb. 2023
Downloads
Published
Versions
- 11-12-2025 (2)
- 06-05-2025 (1)
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Content published in the Journal of Artificial Intelligence for Data-Driven Discovery (JAIDDD) is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). This license allows users to share and adapt the material, provided they:
-
Give appropriate credit to the original author(s).
-
Use the material for non-commercial purposes only.
-
Distribute any adaptations under the same license.
For permissions beyond this license, contact editor@jaiddd.org. Authors retain copyright and grant JAIDDD the right to publish and distribute their work.
