AI and Multi-Cloud Compliance: Safeguarding Data Sovereignty

References

[1] Almagro Armenteros, J. J., Sønderby, C. K., Sønderby, S. K., Nielsen, H., & Winther, O. (2017). DeepLoc: prediction of protein subcellular localization using deep learning. Bioinformatics, 33(21), 3387-3395.https://doi.org/10.1093/bioinformatics/btx431

[2] Alam, M. K., Ahmad, A. U., & Muneeza, A. (2022). External sharī ‘ah audit and review committee vis‐a‐vis sharī ‘ah compliance quality and accountability: A case of islamic banks in Bangladesh. Journal of Public Affairs, 22(1), e2364.https://doi.org/10.1002/pa.2364

[3] Arner, D. W., Barberis, J., & Buckey, R. P. (2016). FinTech, RegTech, and the reconceptualization of financial regulation. Nw. J. Int'l L. & Bus., 37, 371.

[4] Alqahtani, H. S., & Sant, P. (2016, July). A multi-cloud approach for secure data storage on smart device. In 2016 sixth international conference on digital information and communication technology and its applications (dictap) (pp. 63-69). IEEE. https://doi.org/10.1109/DICTAP.2016.7544002

[5] Alyas, T., Alissa, K., Alqahtani, M., Faiz, T., Alsaif, S. A., Tabassum, N., & Naqvi, H. H. (2022). Multi‐cloud integration security framework using honeypots. Mobile Information Systems, 2022(1), 2600712.https://doi.org/10.1155/2022/2600712

[6] AlZain, M. A., Pardede, E., Soh, B., & Thom, J. A. (2012, January). Cloud computing security: from single to multi-clouds. In 2012 45th Hawaii International Conference on System Sciences (pp. 5490-5499). IEEE. https://doi.org/10.1109/HICSS.2012.153

[7] Amoore, L. (2018). Cloud geographies: Computing, data, sovereignty. Progress in human geography, 42(1), 4-24.https://doi.org/10.1177/0309132516662147

[8] Bayer, J. C., Norton, G. W., & Falck-Zepeda, J. B. (2010). Cost of compliance with biotechnology regulation in the Philippines: Implications for developing countries.

[9] Butler, T. (2011). Compliance with institutional imperatives on environmental sustainability: Building theory on the role of Green IS. The Journal of Strategic Information Systems, 20(1), 6-26.https://doi.org/10.1016/j.jsis.2010.09.006

[10] Ernstberger, J., Lauinger, J., Elsheimy, F., Zhou, L., Steinhorst, S., Canetti, R., ... & Song, D. (2023, July). Sok: data sovereignty. In 2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P) (pp. 122-143). IEEE.https://doi.org/10.1109/EuroSP57164.2023.00017

[11] Chinamanagonda, S. (2019). Security in Multi-cloud Environments-Heightened focus on securing multi-cloud deployments. Journal of Innovative Technologies, 2(1).

[12] Calzada, I. (2021). Data co-operatives through data sovereignty. Smart Cities, 4(3), 1158-1172.https://doi.org/10.3390/smartcities4030062

[13] Chintalapudi, K., Padmanabha Iyer, A., & Padmanabhan, V. N. (2010, September). Indoor localization without the pain. In Proceedings of the sixteenth annual international conference on Mobile computing and networking (pp. 173-184).https://doi.org/10.1145/1859995.1860016

[14] Cath, C. (2018). Governing artificial intelligence: ethical, legal and technical opportunities and challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180080.https://doi.org/10.1098/rsta.2018.0080

[15] Dafoe, A. (2018). AI governance: a research agenda. Governance of AI Program, Future of Humanity Institute, University of Oxford: Oxford, UK, 1442, 1443.

[16] Gasser, U., & Almeida, V. A. (2017). A layered model for AI governance. IEEE Internet Computing, 21(6), 58-62.https://doi.org/10.1109/MIC.2017.4180835

[17] Gupta, A., Vedaldi, A., & Zisserman, A. (2016). Synthetic data for text localisation in natural images. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2315-2324).

[18] Graupner, H., Torkura, K., Berger, P., Meinel, C., & Schnjakin, M. (2015, October). Secure access control for multi-cloud resources. In 2015 IEEE 40th Local Computer Networks Conference Workshops (LCN Workshops) (pp. 722-729). IEEE.https://doi.org/10.1109/LCNW.2015.7365920

[19] Hellmeier, M., Pampus, J., Qarawlus, H., & Howar, F. (2023, August). Implementing data sovereignty: Requirements & challenges from practice. In Proceedings of the 18th international conference on availability, reliability and security (pp. 1-9).https://doi.org/10.1145/3600160.3604995

[20] Hossain, M. E., Kabir, M. F., Al Noman, A., Akter, N., & Hossain, Z. (2022). Enhancing Data Privacy And Security In Multi Cloud Environments. BULLET: Jurnal Multidisiplin Ilmu, 1(05), 967-975.

[21] Hummel, P., Braun, M., Tretter, M., & Dabrock, P. (2021). Data sovereignty: A review. Big Data & Society, 8(1), 2053951720982012.https://doi.org/10.1177/2053951720982012

[22] Israel, M. (2014). Research ethics and integrity for social scientists: Beyond regulatory compliance.

[23] Jarke, M., Otto, B., & Ram, S. (2019). Data sovereignty and data space ecosystems. Business & Information Systems Engineering, 61, 549-550.https://doi.org/10.1007/s12599-019-00614-2

[24] Junghanns, P., Fabian, B., & Ermakova, T. (2016). Engineering of secure multi-cloud storage. Computers in Industry, 83, 108-120.https://doi.org/10.1016/j.compind.2016.09.001

[25] Janssen, M., Brous, P., Estevez, E., Barbosa, L. S., & Janowski, T. (2020). Data governance: Organizing data for trustworthy Artificial Intelligence. Government information quarterly, 37(3), 101493.https://doi.org/10.1016/j.giq.2020.101493

[26] Kukutai, T., & Taylor, J. (2016). Indigenous data sovereignty: Toward an agenda. ANU press.

[27] Kotaru, M., Joshi, K., Bharadia, D., & Katti, S. (2015, August). Spotfi: Decimeter level localization using wifi. In Proceedings of the 2015 ACM conference on special interest group on data communication (pp. 269-282).https://doi.org/10.1145/2785956.2787487

[28] Kuziemski, M., & Misuraca, G. (2020). AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings. Telecommunications policy, 44(6), 101976.https://doi.org/10.1016/j.telpol.2020.101976

[29] Lovett, R., Lee, V., Kukutai, T., Cormack, D., Rainie, S. C., & Walker, J. (2019). Good data practices for Indigenous data sovereignty and governance. Good data, 2019, 26-36.

[30] Li, W., Mahadevan, V., & Vasconcelos, N. (2013). Anomaly detection and localization in crowded scenes. IEEE transactions on pattern analysis and machine intelligence, 36(1), 18-32.https://doi.org/10.1109/TPAMI.2013.111

[31] Levi-Faur, D. (2011). Regulation and regulatory governance. Handbook on the Politics of Regulation, 1(1), 1-25.https://doi.org/10.4337/9780857936110

[32] Li, C. L., Sohn, K., Yoon, J., & Pfister, T. (2021). Cutpaste: Self-supervised learning for anomaly detection and localization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 9664-9674).

[33] Mortensen, K. I., Churchman, L. S., Spudich, J. A., & Flyvbjerg, H. (2010). Optimized localization analysis for single-molecule tracking and super-resolution microscopy. Nature methods, 7(5), 377-381.

[34] Pawar, P. S., Sajjad, A., Dimitrakos, T., & Chadwick, D. W. (2015). Security-as-a-service in multi-cloud and federated cloud environments. In Trust Management IX: 9th IFIP WG 11.11 International Conference, IFIPTM 2015, Hamburg, Germany, May 26-28, 2015, Proceedings 9 (pp. 251-261). Springer International Publishing.https://doi.org/10.1007/978-3-319-18491-3_21

[35] Patel, S. (2021). Challenges and technological advances in high-density data center infrastructure and environmental matching for cloud computing. International Journal of Advanced Research in Science, Communication and Technology (IJARSCT), 12(1), 1-7.

[36] Patel, S. (2024). Performance analysis of routing protocols in mobile ad-hoc networks (MANETs) using NS2: A comparative study of AODV, DSR, and DSDV. INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT, 8(09).

[37] Patel, S. (2024). Cloud computing: Revolutionizing IT infrastructure with on-demand services and addressing security challenges. International Journal of Advanced Research in Science, Communication and Technology

[38] Suraj, P. (2022). Optimizing energy efficiency in wireless sensor networks: A review of cluster head selection techniques. International Journal of Trend in Scientific Research and Development, 6(2), 1584-1589.

[39] Suraj, P. (2024). SYNERGIZING ROBOTICS AND ARTIFICIAL INTELLIGENCE: TRANSFORMING MANUFACTURING AND AUTOMATION FOR INDUSTRY 5.0. Synergy: Cross-Disciplinary Journal of Digital Investigation, 2(11), 69-75.

[40] Suraj, P. (2024). An Overview of Cloud Computing Impact on Smart City Development and Management. International Journal of Trend in Scientific Research and Development, 8(6), 715-722.

[41] Reddy, S., Allan, S., Coghlan, S., & Cooper, P. (2020). A governance model for the application of AI in health care. Journal of the American Medical Informatics Association, 27(3), 491-497.https://doi.org/10.1093/jamia/ocz192

[42] Singh, Y., Kandah, F., & Zhang, W. (2011, April). A secured cost-effective multi-cloud storage in cloud computing. In 2011 IEEE conference on computer communications workshops (INFOCOM WKSHPS) (pp. 619-624). IEEE.https://doi.org/10.1109/INFCOMW.2011.5928887

[43] Sparrow, M. K. (2011). The regulatory craft: controlling risks, solving problems, and managing compliance. Rowman & Littlefield.

[44] Taeihagh, A. (2021). Governance of artificial intelligence. Policy and society, 40(2), 137-157.https://doi.org/10.1080/14494035.2021.1928377

[45] Tabassum, N., Naeem, H., & Batool, A. (2023). The Data Security and multi-cloud Privacy concerns. International Journal for Electronic Crime Investigation, 7(1), 49-58.https://doi.org/10.54692/ijeci.2023.0701128

[46] Ulnicane, I., Eke, D. O., Knight, W., Ogoh, G., & Stahl, B. C. (2021). Good governance as a response to discontents? Déjà vu, or lessons for AI from other emerging technologies. Interdisciplinary Science Reviews, 46(1-2), 71-93.https://doi.org/10.1080/03080188.2020.1840220

[47] Walter, M., Kukutai, T., Carroll, S. R., & Rodriguez-Lonebear, D. (2021). Indigenous data sovereignty and policy (p. 244). Taylor & Francis.

[48] Winfield, A. F., & Jirotka, M. (2018). Ethical governance is essential to building trust in robotics and artificial intelligence systems. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180085.https://doi.org/10.1098/rsta.2018.0085

[49] Walter, M., Lovett, R., Maher, B., Williamson, B., Prehn, J., Bodkin‐Andrews, G., & Lee, V. (2021). Indigenous data sovereignty in the era of big data and open data. Australian Journal of Social Issues, 56(2), 143-156.https://doi.org/10.1002/ajs4.141

[50] Zuiderwijk, A., Chen, Y. C., & Salem, F. (2021). Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda. Government information quarterly, 38(3), 101577.https://doi.org/10.1016/j.giq.2021.101577

[51] Zhang, B., & Dafoe, A. (2019). Artificial intelligence: American attitudes and trends. Available at SSRN 3312874.

[52] Yu, N. Y., Wagner, J. R., Laird, M. R., Melli, G., Rey, S., Lo, R., ... & Brinkman, F. S. (2010). PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics, 26(13), 1608-1615.https://doi.org/10.1093/bioinformatics/btq249

[53] Yeoh, P. (2017). Regulatory issues in blockchain technology. Journal of Financial Regulation and Compliance, 25(2), 196-208.https://doi.org/10.1108/JFRC-08-2016-0068

[54] Zhang, J., & El-Gohary, N. M. (2016). Semantic NLP-based information extraction from construction regulatory documents for automated compliance checking. Journal of computing in civil engineering, 30(2), 04015014.https://doi.org/10.1061/(ASCE)CP.1943-5487.0000346

[55] Zafari, F., Gkelias, A., & Leung, K. K. (2019). A survey of indoor localization systems and technologies. IEEE Communications Surveys & Tutorials, 21(3), 2568-2599.https://doi.org/10.1109/COMST.2019.2911558

@article{1705421,
      author = {Shishir Tewari, Ashitosh Chitnis},
      title = {AI and Multi-Cloud Compliance: Safeguarding Data Sovereignty},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {7},
      pages = {571-587},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705421.pdf},
      abstract = {Multi-cloud enterprise adoption of AI solutions now faces fresh obstacles to safeguard data sovereignty since data should operate under national laws of its collection locale. Multinational companies that depend on distributed cloud methodologies for scalability and efficiency seek to perform AI-powered analytics while managing diverse regulations that control where and how they handle data and transfer it between borders. Organizations now need to follow three categories of strict data compliance policies: GDPR, CCPA, and EU AI Act that make them implement secure data handling systems to match local data protection standards.

The enforcement of data sovereignty becomes complex in multi-cloud systems because of substantial challenges and security risks they produce. Data distribution across multiple storage systems together with system compatibility problems and security holes and inconsistent regulatory standards result in the risk of data breaches and regulatory noncompliance and information control loss for business-critical data. Sovereignty requirements throughout multiple cloud providers become essential with AI-powered applications that process large volumes of sensitive data because data protection measures and encryption and access control need to be established at once. When enterprises lack proper governance systems they face problems that include regulatory penalties as well as regulatory conflicts with data residency requirements and impaired AI decision systems.

A detailed examination exists within this paper about how AI-powered multi-cloud enterprises should tackle legal, technical and operational challenges to guarantee data sovereignty. The paper delivers thorough examinations of data compliance standards while defining proper AI governance methods together with secure procedures for protecting data distributed across multiple cloud platforms. The research investigates data localization approaches as well as secure AI processing practices and encryption-based sovereignty methods which businesses can employ to decrease their vulnerabilities. The research uses financial sector and healthcare sector and public sector case studies to present effective methods which lead to AI performance enhancement alongside regulatory adherence.

Additionally this document reviews current sovereign cloud trends together with AI-based regulatory enforcement methods alongside automated compliance tracking methods which assist businesses in managing AI security together with cloud governance developments. Researchers offer specific solutions which businesses need to reach their maximum AI potential while sustaining regulatory compliance along with high security standards and upholding ethical responsibilities.},
      keywords = {Data Sovereignty, Multi-Cloud Security, AI Governance, Regulatory Compliance, Data Localization},
      month = {January},
  }