Implementing Secure Cloud Storage policies with Blockchain and AI
DOI:
https://doi.org/10.56472/ICCSAIML25-118Keywords:
Cloud computing, Blockchain, Artificial Intelligence, Data integrity, Regulatory ComplianceAbstract
Cloud computing has become a vital infrastructure for storing and accessing data remotely. However, this centralized model introduces significant security and privacy concerns, including data breaches, insider threats, and regulatory non-compliance. To address these issues, this paper presents a hybrid framework that integrates Blockchain and Artificial Intelligence (AI) to implement a secure cloud storage policy. Blockchain provides immutability, transparency, and decentralized data access management, reducing risks associated with tampering and unauthorized modifications. Concurrently, AI enhances threat detection through real-time anomaly identification and predictive analytics, allowing proactive response mechanisms to potential security breaches. The proposed architecture combines smart contracts for automated policy enforcement with AI modules that analyse access behaviours and assign risk scores to cloud storage activities. Case studies in healthcare and finance demonstrate the effectiveness of the framework, highlighting increased data integrity, regulatory compliance, and reduced incidents of insider threats and fraudulent activities. The paper also explores technical challenges such as Blockchain scalability, data privacy in public ledgers, and AI model biases. It proposes future advancements, including federated learning, edge AI deployment, and the development of quantum-resistant Blockchain algorithms to further enhance system robustness. This interdisciplinary approach illustrates that the synergy between Blockchain and AI can address modern cloud security needs by creating intelligent, adaptive, and secure data environments. Through continuous learning and decentralized control, such a framework enables organizations to meet growing cybersecurity demands while maintaining operational agility and user trust
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References
[1] S. Subashini and V. Kavitha, "A survey on security issues in service delivery models of cloud computing," Journal of Network and Computer Applications, vol. 34, no. 1, pp. 1–11, Jan. 2013.
[2] R. Zhang, R. Xue, and L. Liu, "Security and Privacy on Blockchain," ACM Computing Surveys, vol. 52, no. 3, pp. 1–34, Jun. 2019.
[3] P. Sharma, R. Singh, and H. Sharma, "AI-Driven Intrusion Detection Systems for Cloud Computing," IEEE Access, vol. 8, pp. 123456–123467, 2020.
[4] X. Chen, J. Li, J. Ma, Q. Tang, and W. Lou, "New Algorithms for Secure Outsourcing of Large-Scale Systems of Linear Equations," IEEE Transactions on Information Forensics and Security, vol. 11, no. 1, pp. 44–57, Jan. 2016.
[5] M. Conti, E. S. Kumar, C. Lal, and S. Ruj, "A Survey on Security and Privacy Issues of Blockchain Technology," IEEE Communications Surveys & Tutorials, vol. 21, no. 2, pp. 1102–1134, 2019.
[6] A. Dehghantanha and K.-K. R. Choo, "A Survey of Blockchain Security Issues and Challenges," Future Generation Computer Systems, vol. 107, pp. 841–853, 2020.
[7] N. Kshetri, "1 The Emerging Role of Big Data in Key Development Issues: Opportunities, Challenges, and Concerns," Big Data for Development, pp. 1–21, 2020.
[8] H. Haddad Pajouh et al., "A Comprehensive Cybersecurity Framework for Smart Healthcare Using AI and Blockchain," IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 2, pp. 543–556, 2022.
[9] P. J. Taylor and B. McKay, "Blockchain for Cybersecurity and Privacy: Architectures, Challenges, and Applications," IEEE Internet of Things Journal, vol. 8, no. 7, pp. 5564–5580, Apr. 2021.
[10] L. T. Yang et al., "Federated Learning with Blockchain for Autonomous Vehicles: Concepts, Challenges, and Future Directions," IEEE Network, vol. 34, no. 5, pp. 198–205, 2020.
[11] Thirunagalingam, A., Addanki, S., Vemula, V. R., & Selvakumar, P. (2025). AI in Performance Management: Data-Driven Approaches. In F. Özsungur (Ed.), Navigating Organizational Behavior in the Digital Age With AI (pp. 101-126). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-8442-8.ch005.
[12] Praveen Kumar Maroju, “Conversational AI for Personalized Financial Advice in the BFSI Sector”, vol.8, no.1, pp. 156-177, 2022.
[13] Swathi Chundru, “Beyond Rules-Based Systems: AI-Powered Solutions for Ensuring Data Trustworthiness”, vol.7 no. 7, pp. 17, 2023.
[14] Muniraju Hullurappa, “Intelligent Data Masking: Using GANs to Generate Synthetic Data for Privacy-Preserving Analytics”, ijiest, vol.9, no. 1, pp.9, 2023.
[15] Sudheer Panyaram, “Connected Cars, Connected Customers: The Role of AI and ML in Automotive Engagement”, International Transactions in Artificial Intelligence, vol.7, pp. 7, 2023.
[16] Venu Madhav Aragani, “New Era of Efficiency and Excellence Revolutionizing Quality Assurance Through AI”, ResearchGate, vol. 4, no. 4, pp.1-26, 2023.
[17] Lakshmi Narasimha Raju Mudunuri, “AI-Driven Inventory Management: Never Run Out, Never Overstock” , International Journal of Advances in Engineering Research, vol .26, no. 6, pp. 26-35, 2023.
[18] Mohanarajesh Kommineni, “Study High-Performance Computing Techniques for Optimizing and Accelerating AI Algorithms Using Quantum Computing and Specialized Hardware”. International Journal of Innovations in Applied Sciences & Engineering. Vol-9, pp48-59, 2023.
[19] Oku Krishnamurthy, “Enhancing Cyber Security Enhancement Through Generative AI”, Ijuse, vol.9, pp.35-50, 2023.
[20] Padmaja Pulivarthy, “Enhancing Database Query Efficiency: AI-Driven NLP Integration in Oracle”, researchgate.net, 2023.
[21] Swathi Chundru, “Beyond Rules-Based Systems: AI-Powered Solutions for Ensuring Data Trustworthiness”, vol.7 no. 7, pp. 17, 2023.
[22] Muniraju Hullurappa, “Intelligent Data Masking: Using GANs to Generate Synthetic Data for Privacy-Preserving Analytics”, ijiest, vol.9, no. 1, pp.9, 2023.
[23] Sudheer Panyaram, “Connected Cars, Connected Customers: The Role of AI and ML in Automotive Engagement”, International Transactions in Artificial Intelligence, vol.7, pp. 7, 2023.
[24] Venu Madhav Aragani, “New Era of Efficiency and Excellence Revolutionizing Quality Assurance Through AI”, ResearchGate, vol. 4, no. 4, pp.1-26, 2023.
[25] Lakshmi Narasimha Raju Mudunuri, “AI-Driven Inventory Management: Never Run Out, Never Overstock” , International Journal of Advances in Engineering Research, vol .26, no. 6, pp. 26-35, 2023.
[26] Mohanarajesh Kommineni, “Study High-Performance Computing Techniques for Optimizing and Accelerating AI Algorithms Using Quantum Computing and Specialized Hardware”. International Journal of Innovations in Applied Sciences & Engineering. Vol-9, pp48-59, 2023.
[27] Oku Krishnamurthy, “Enhancing Cyber Security Enhancement Through Generative AI”, Ijuse, vol.9, pp.35-50, 2023.
[28] Padmaja Pulivarthy, “Enhancing Database Query Efficiency: AI-Driven NLP Integration in Oracle”, researchgate.net, 2023.
[29] Venu Madhav Aragani, “Unveiling the Magic Of AI and Data Analytics: Revolutionizing Risk Assessment and Underwriting in the Insurance Industry”, International Journal of Advances in Engineering Research, vol 24(6), Pp.1-13, 2022.
[30] Vamshidhar Reddy Vemula, “Adaptive Threat Detection in DevOps: Leveraging Machine Learning for Real-Time Security Monitoring”, 5(5), 2022, 1-17.
[31] Muniraju Hullurappa, “The Role of Explainable AI in Building Public Trust: A Study of AI-Driven Public Policy Decisions”, vol-6, 2022.
[32] R. Daruvuri, “Dynamic load balancing in AI-enabled cloud infrastructures using reinforcement learning and algorithmic optimization,” World Journal of Advanced Research and Reviews, vol. 20, no. 1, pp. 1327–1335, Oct. 2023, doi: 10.30574/wjarr.2023.20.1.2045.
