Machine Identity Security in Cloud & AI: Ensuring Lifecycle Management, Ownership, and Accountability for Non-Human Identities
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© 2025 by IJCTT Journal
Volume-73 Issue-2
Year of Publication : 2025
Authors : Anant Wairagade
DOI :  10.14445/22312803/IJCTT-V73I2P110

How to Cite?

Anant Wairagade, "Machine Identity Security in Cloud & AI: Ensuring Lifecycle Management, Ownership, and Accountability for Non-Human Identities," International Journal of Computer Trends and Technology, vol. 73, no. 2, pp. 80-89, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I2P110

Abstract
Machine Identity Security is critical to protecting modern digital ecosystems. The expansion of Cloud and AI technologies across organizations has dramatically expanded the number of machine identities, representing everything from APIs to IoT devices and software services. These identities are essential for authentication, encryption, and communication between interlinked systems. However, managing machine identities is now a critical challenge because dynamic workloads, ephemeral containers, and automated processes have added unprecedented complexity [1]. As continuous data flows through the cloud environment and infrastructure evolves rapidly, breaches resulting from vulnerabilities associated with machine identities can be devastating. Example: API Key or Certificate — A compromised API key or an expired certificate can allow an attacker access to sensitive data or disrupt services. An evolving security framework focused on Cloud and AI ecosystems will be needed to address these risks. According to a research paper, recent strides in Machine Learning (ML) provide cloud security applications with threat detection, credential management, and other aspects of resilience that increasingly rely on algorithms [2]. This research aims to connect the concepts from theoretical frameworks to executable scenarios that can be implemented in the form of Machine Identity Security solutions. In particular, this will cover machine identity lifecycle management, accountability mechanisms, and miscellaneous problems raised by Non-Human Identities [3].

Keywords
Machine Identity Security, Non-Human Identity, Life cycle Management, Cloud and AI, Automated Governance.

Reference

[1] Ali Bou Nassif et al., “Machine Learning for Cloud Security: A Systematic Review,” IEEE Access, vol. 9, pp. 20717-20739, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Jana Glöckler et al., “A Systematic Review of Identity and Access Management Requirements in Enterprises and Potential Contributions of Self-Sovereign Identity,” Business & Information Systems Engineering, vol. 66, no. 4, pp. 421-440, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Varun Chandrasekaran et al., “SoK: Machine Learning Governance,” arXiv, pp. 1-19, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Alea Fairchild, and Piet Ribbers, Privacy-Enhancing Identity Management in Business, Digital Privacy, Springer, pp. 107-129, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Simon Feulner et al., “Exploring the Use of Self-Sovereign Identity for Event Ticketing Systems,” Electronic Markets, vol. 32, pp. 1759 1777, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Ant Allan, “Hype Cycle for Identity and Access Management Technologies,” Gartner Information Technology, 2020.
[Google Scholar] [Publisher Link]
[7] Audun Josang, and Simon Pope, “User-Centric Identity Management,” Proceedings of the AUSCERT Asia Pacific Information Technology Security Conference, pp. 1-13, 2005.
[Google Scholar]
[8] Kenneth Holstein et al., “Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need?,” Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, Glasgow Scotland Uk, pp. 1-16, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Cynthia Dwork, and Aaron Roth, “The Algorithmic Foundations of Differential Privacy,” Foundations and Trends in Theoretical Computer Science, vol. 9, no. 3-4, pp. 211-407, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Zulfiqar Ali Khan et al., “A Review on Task Scheduling Techniques in Cloud and Fog Computing: Taxonomy, Tools, Open Issues, Challenges, and Future Directions,” IEEE Access, vol. 11, pp. 143417-143445, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Shreshth Tuli, Giuliano Casale, and Nicholas R. Jennings, “GOSH: Task Scheduling Using Deep Surrogate Models in Fog Computing Environments,” IEEE Transactions on Parallel and Distributed Systems, vol. 33, no. 11, pp. 2821-2833, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Steffen Schwalm, Daria Albrecht, and Ignacio Alamillo, “eIDAS 2.0: Challenges, Perspectives and Proposals to Avoid Contradictions between eIDAS 2.0 and SSI,” Copenhagen, Denmark, Open Identity Summit, pp. 63-74, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Craig Gentry, “Fully Homomorphic Encryption Using Ideal Lattices,” Proceedings of the 41st Annual ACM Symposium on Theory of Computing, Bethesda MD USA, pp. 169-178, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Vaishali Singh, and S.K. Pandey, “Revisiting Cloud Security Attacks: Credential Attack,” Proceedings of FICR-TEAS 2020: Rising Threats in Expert Applications and Solutions: Proceedings of FICR-TEAS 2020, Jaipur, Rajasthan, India, pp. 339-350, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Yassine Maleh, Enhancing E-Learning Security in Cloud Environments: Risk Assessment and Penetration Testing, Cybersecurity Management in Education Technologies, 1st ed., CRC Press, pp. 1-40, 2023.
[Google Scholar] [Publisher Link]
[16] A. Sabitha Banu, and G. Padmavathi, “A Survey of Computational Intelligence Methods Used in Handling Man in the Middle Attacks in Machine to Machine Communications,” International Journal of Engineering Research and Technology, vol. 8, no. 8, pp. 218-226, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Umme Habiba et al., “Cloud Identity Management Security Issues & Solutions: A Taxonomy,” Complex Adaptive Systems Modeling, vol. 2, pp. 1-37, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Kokthay Poeng, and Laurent Schumacher, “Lateral Movement Identification in Cross-Cloud Deployment,” 2024 20th International Conference on Network and Service Management, Prague, Czech Republic, pp. 1-4, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Keith Bonawitz et al., “Practical Secure Aggregation for Federated Learning on User-Held Data,” arXiv, pp. 1-5, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Jacob Devlin et al., “BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding,” Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, Minnesota, vol. 1, pp. 4171-4186, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Nicolas Papernot et al., “SoK: Security and Privacy in Machine Learning,” 2018 IEEE European Symposium on Security and Privacy (EuroS&P), London, UK, pp. 399-414, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Ayoobkhan Mohamed Uvaze Ahamed et al., “Deep Learning and Optimization-Based Task Scheduling Algorithms for Fog-Cloud Computing Environment,” Journal of Intelligent and Fuzzy Systems, pp. 1-14, 2023.
[Google Scholar] [Publisher Link]