From Compliance to Cost Optimization: AI’s Role in Modern Cloud Security Strategies
Keywords:
Cloud Security, Artificial Intelligence, Compliance, Cost Optimization, Machine Learning, Threat Detection, Predictive Analytics, Cybersecurity, AutomationAbstract
More so, as the adoption of cloud computing continues to grow, there are more compliance demands, higher expenses, and continued emergence of new threats. Previous concepts in cloud security strategies mostly included compliance with regulations, while with AI’s incorporation there is a shift that addresses the compliance aspect with equal consideration for cost containment. In the following paper, the author seeks to understand how AI can help advance current cloud security paradigms and show how compliance, resources, and security costs are not a hindrance to efficient and effective delivery of cloud services. Enthusiastically, the paper shows strong AI-based solutions that equal the peculiar ideas of various compliance monitoring, threat detection, automated response to an incurrence or surge in criminal incidents, and several predictive analytics. These results propose that AI can considerably decrease the personnel and systemic loads, costs, and dangers and empower organizations to be more proactive in cloud safety. This paper also explains the specific issues regarding the use of AI in cloud security and the privacy issues arising from this practice, modeling the current problems of AI in terms of further development of technologies for AI-based optimization of cloud security.
References
Abouelyazid, M., & Xiang, C. (2019). Architectures for AI Integration in Next-Generation Cloud Infrastructure, Development, Security, and Management. International Journal of Information and Cybersecurity, 3(1), 1-19.
Joshi, D., Sayed, F., Saraf, A., Sutaria, A., & Karamchandani, S. (2021). Elements of Nature Optimized into Smart Energy Grids using Machine Learning. Design Engineering, 1886-1892.
Preyaa Atri, "Design and Implementation of High-Throughput Data Streams using Apache Kafka for Real-Time Data Pipelines", International Journal of Science and Research (IJSR), Volume 7 Issue 11, November 2018, pp. 1988-1991, https://www.ijsr.net/getabstract.php?paperid=SR24422184316
Syed, F. M., & ES, F. K. (2023). Leveraging AI for HIPAA-Compliant Cloud Security in Healthcare. Revista de Inteligencia Artificial en Medicina, 14(1), 461-484.
Preyaa Atri, "Optimizing Financial Services Through Advanced Data Engineering: A Framework for Enhanced Efficiency and Customer Satisfaction", International Journal of Science and Research (IJSR), Volume 7 Issue 12, December 2018, pp. 1593-1596, https://www.ijsr.net/getabstract.php?paperid=SR24422184930
JOSHI, D., SAYED, F., BERI, J., & PAL, R. (2021). An efficient supervised machine learning model approach for forecasting of renewable energy to tackle climate change. Int J Comp Sci Eng Inform Technol Res, 11, 25-32.
Beeram, D., Alapati, N. K., & VISA, I. (2023). Multi-Cloud Strategies and AI-Driven Analytics: The Next Frontier in Cloud Data Management. Innovative Computer Sciences Journal, 9(1).
Preyaa Atri, "Enhancing Big Data Interoperability: Automating Schema Expansion from Parquet to BigQuery", International Journal of Science and Research (IJSR), Volume 8 Issue 4, April 2019, pp. 2000-2002, https://www.ijsr.net/getabstract.php?paperid=SR24522144712
Mircea, M., & Andreescu, A. I. (2011). Using cloud computing in higher education: A strategy to improve agility in the current financial crisis. Communications of the IBIMA.
Joshi, D., Parikh, A., Mangla, R., Sayed, F., & Karamchandani, S. H. (2021). AI Based Nose for Trace of Churn in Assessment of Captive Customers. Turkish Online Journal of Qualitative Inquiry, 12(6).
Preyaa Atri, "Unlocking Data Potential: The GCS XML CSV Transformer for Enhanced Accessibility in Google Cloud", International Journal of Science and Research (IJSR), Volume 8 Issue 10, October 2019, pp. 1870-1871, https://www.ijsr.net/getabstract.php?paperid=SR24608145221
Preyaa Atri, "Enhancing Data Engineering and AI Development with the 'Consolidate-csv-files-from-gcs' Python Library", International Journal of Science and Research (IJSR), Volume 9 Issue 5, May 2020, pp. 1863-1865, https://www.ijsr.net/getabstract.php?paperid=SR24522151121
Sekar, J. (2023). MULTI-CLOUD STRATEGIES FOR DISTRIBUTED AI WORKFLOWS AND APPLICATION. Journal of Emerging Technologies and Innovative Research, 10, P600-P610.
Khambaty, A., Joshi, D., Sayed, F., Pinto, K., & Karamchandani, S. (2022, January). Delve into the Realms with 3D Forms: Visualization System Aid Design in an IOT-Driven World. In Proceedings of International Conference on Wireless Communication: ICWiCom 2021 (pp. 335-343). Singapore: Springer Nature Singapore.
Preyaa Atri, "Advancing Financial Inclusion through Data Engineering: Strategies for Equitable Banking", International Journal of Science and Research (IJSR), Volume 11 Issue 8, August 2022, pp. 1504-1506, https://www.ijsr.net/getabstract.php?paperid=SR24422190134
Mungoli, N. (2023). Scalable, Distributed AI Frameworks: Leveraging Cloud Computing for Enhanced Deep Learning Performance and Efficiency. arXiv preprint arXiv:2304.13738.
Muhammad, T. (2022). A Comprehensive Study on Software-Defined Load Balancers: Architectural Flexibility & Application Service Delivery in On-Premises Ecosystems. International Journal of Computer Science and Technology, 6(1), 1-24.
Khambati, A., Pinto, K., Joshi, D., & Karamchandani, S. H. (2021). Innovative Smart Water Management System Using Artificial Intelligence. Turkish Journal of Computer and Mathematics Education, 12(3), 4726-4734.
George, J. (2022). Optimizing hybrid and multi-cloud architectures for real-time data streaming and analytics: Strategies for scalability and integration.
Preyaa Atri. (2021). Automated Object Deletion in Google Cloud Storage: Introducing the Clean-up-gcs-bucket Library. European Journal of Advances in Engineering and Technology, 8(7), 79–83. https://doi.org/10.5281/zenodo.11408114
Sathupadi, K. (2022). Ai-driven qos optimization in multi-cloud environments: Investigating the use of ai techniques to optimize qos parameters dynamically across multiple cloud providers. Applied Research in Artificial Intelligence and Cloud Computing, 5(1), 213-226.
Raman, P. K. (2022). Omnichannel Commerce in the Grocery Sector: A Comparative Study of India, UK, and US with Technological Insights on APIs and Headless Commerce. Journal of Science & Technology, 3(3), 136-200.
Preyaa Atri. (2021). Efficiently Handling Streaming JSON Data: A Novel Library for GCS-to-BigQuery Ingestion. European Journal of Advances in Engineering and Technology, 8(10), 96–99. https://doi.org/10.5281/zenodo.11408124
Yathiraju, N. (2022). Investigating the use of an artificial intelligence model in an ERP cloud-based system. International Journal of Electrical, Electronics and Computers, 7(2), 1-26.
Robertson, J., Fossaceca, J. M., & Bennett, K. W. (2021). A cloud-based computing framework for artificial intelligence innovation in support of multidomain operations. IEEE Transactions on Engineering Management, 69(6), 3913-3922.
Preyaa Atri. (2021). Efficient Data Transformation on Google Cloud Storage: A Python Library for Converting CSV to Parquet. European Journal of Advances in Engineering and Technology, 8(3), 59–62. https://doi.org/10.5281/zenodo.11408142
Velayutham, A. (2020). Architectural strategies for implementing and automating service function chaining (sfc) in multi-cloud environments. Applied Research in Artificial Intelligence and Cloud Computing, 3(1), 36-51.
Devan, M., Shanmugam, L., & Althati, C. (2021). Overcoming Data Migration Challenges to Cloud Using AI and Machine Learning: Techniques, Tools, and Best Practices. Australian Journal of Machine Learning Research & Applications, 1(2), 1-39.
Faccia, A., & Petratos, P. (2021). Blockchain, enterprise resource planning (ERP) and accounting information systems (AIS): Research on e-procurement and system integration. Applied Sciences, 11(15), 6792.
Gudimetla, S. R., & Kotha, N. R. (2018). Cloud security: Bridging the gap between cloud engineering and cybersecurity. Webology (ISSN: 1735-188X), 15(2).
Mishra, P., & Singh, G. (2023). Energy management systems in sustainable smart cities based on the internet of energy: A technical review. Energies, 16(19), 6903.
Karadsheh, L. (2012). Applying security policies and service level agreement to IaaS service model to enhance security and transition. computers & security, 31(3), 315-326.
Kanth, T. C. (2023). EFFICIENT STRATEGIES FOR SEAMLESS CLOUD MIGRATIONS USING ADVANCED DEPLOYMENT AUTOMATIONS.
Atri P. Enabling AI Work flows: A Python Library for Seamless Data Transfer between Elasticsearch and Google Cloud Storage. J Artif Intell Mach Learn & Data Sci 2022, 1(1), 489-491. DOI: doi.org/10.51219/JAIMLD/preyaa-atri/132
Parimi, S. S. R. (2018). Optimizing Financial Reporting and Compliance in SAP with Machine Learning Techniques. TIJER-TIJERINTERNATIONAL RESEARCH JOURNAL (www. TIJER. org), ISSN, 2349-9249.
El Kafhali, S., El Mir, I., & Hanini, M. (2022). Security threats, defense mechanisms, challenges, and future directions in cloud computing. Archives of Computational Methods in Engineering, 29(1), 223-246.
Shah, V., & Konda, S. R. (2022). Cloud Computing in Healthcare: Opportunities, Risks, and Compliance. Revista Espanola de Documentacion Cientifica, 16(3), 50-71.
Ahmad, W., Rasool, A., Javed, A. R., Baker, T., & Jalil, Z. (2021). Cyber security in iot-based cloud computing: A comprehensive survey. Electronics, 11(1), 16.
Dalal, A., Abdul, S., Mahjabeen, F., & Kothamali, P. R. (2019). Leveraging Artificial Intelligence and Machine Learning for Enhanced Application Security. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 10(1), 82-99.
Yaseen, A. (2022). ACCELERATING THE SOC: ACHIEVE GREATER EFFICIENCY WITH AI-DRIVEN AUTOMATION. International Journal of Responsible Artificial Intelligence, 12(1), 1-19.
Preyaa Atri (2022) Streamlined Data Extraction and Automated Email Distribution: The BigQuery Email Extractor Approach. Journal of Mathematical & Computer Applications. SRC/JMCA-201. DOI: doi.org/10.47363/JMCA/2022(1)166
Opala, O. J. (2012). An analysis of security, cost-effectiveness, and it compliance factors influencing cloud adoption by it managers (Doctoral dissertation, Capella University).
Kanth, T. C. (2023). EFFICIENT STRATEGIES FOR SEAMLESS CLOUD MIGRATIONS USING ADVANCED DEPLOYMENT AUTOMATIONS.
Gill, S. S., Xu, M., Ottaviani, C., Patros, P., Bahsoon, R., Shaghaghi, A., ... & Uhlig, S. (2022). AI for next generation computing: Emerging trends and future directions. Internet of Things, 19, 100514.
Gudimetla, S. R., & Kotha, N. R. (2018). Cloud security: Bridging the gap between cloud engineering and cybersecurity. Webology (ISSN: 1735-188X), 15(2).
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