AI-Augmented Infrastructure Governance: Intelligent Risk Detection in Identity-Centric Cloud Platforms

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Published: 2025-03-10
DOI: https://doi.org/10.15662/IJRPETM.2025.0802007
Keywords:
AI-Augmented Governance, Infrastructure-as-Code (IaC), Identity-Centric Cloud Security, Anomaly Detection, Isolation Forest

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Nandkumar Niture
Princpal Cloud Infrastructure, Naperville IL, USA

Abstract

The modern-day cloud platforms rely heavily on automated infrastructure and identity systems. Nevertheless, fixed surveillance systems are incapable of identifying complex identity abuse and drifting configuration in dynamic systems. In this paper, an AI-enhanced infrastructure governance system that incorporates supervised learning and anomaly detection as part of identity-centric cloud platforms is outlined. The suggested system will superimpose identity anomaly potential and Configuration Drift Index (CDI) into single Risk Score. The experimental findings are improved significantly compared to the results of the standing monitoring. The precision rose to remain at 0.89 and recall also rose to 0.86, whereas, F1-score rose to 0.87. False positive changed by 0.22 to 0.08. Mean latency of detection was reduced to 74 seconds as compared to 312 seconds. The rate of detection of configuration drift increased to 91% downward of the previous rate of 69%. Significance was found to be at p under 0.01 using statistical testing. The findings show that AI-based governance enhances accuracy in detection, lowers the response time, and enhances adaptive risk management of identity centric cloud environments.

Article Details

Issue

Vol. 8 No. 2 (2025): International Journal of Research Publications in Engineering, Technology and Management

Section

Articles

How to Cite

AI-Augmented Infrastructure Governance: Intelligent Risk Detection in Identity-Centric Cloud Platforms. (2025). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(2), 11802-11814. https://doi.org/10.15662/IJRPETM.2025.0802007

References

[1] Y. Deng et al., "A trusted edge computing system based on intelligent risk detection for smart IoT," IEEE.

[2] A. Joshi, "Emerging technologies driving zero trust maturity across industries," TechRxiv, 2024, doi: 10.36227/techrxiv.172599552.25015466/v1.

[3] M. Olsson et al., "5G zero trust–a zero-trust architecture for telecom," IEEE.

[4] Y. Wang et al., "FRAD: Free-rider attacks detection mechanism for federated learning in AIoT," IEEE Internet of Things Journal, 2023, doi: 10.1109/jiot.2023.3298606.

[5] H. Min, "Distributed network resources monitoring based on multi-agent and matrix grammar," in Proc. Int. Symp. Parallel Architectures, Algorithms and Programming, 2011, doi: 10.1109/PAAP.2011.25.

[6] M. H. Tania et al., "Unleashing the power of federated learning in fragmented digital healthcare systems: A visionary perspective," in Proc. SKIMA, 2023, doi: 10.1109/skima59232.2023.10387304.

[7] M. S. Al-gaashani et al., "Intelligent system architecture for smart city and its applications based edge computing," in Proc. Int. Conf. Ultra Modern Telecommunications, 2020, doi: 10.1109/ICUMT51630.2020.9222460.

[8] A. Fadila et al., "Comprehensive review of smart urban traffic management in the context of the fourth industrial revolution," IEEE Access, 2024, doi: 10.1109/access.2024.3509572.

[9] A. Kumar et al., "Advancing Industrial Cybersecurity: Machine Learning-Based Detection and Mitigation of IIoT Attacks," in Proc. ICICNIS, 2024, doi: 10.1109/icicnis64247.2024.10823323.

[10] A. Sharma et al., "Risk factors associated with online transactions," in Proc. Int. Conf. Computing Communication and Networking Technologies, 2022, doi: 10.1109/ICCCNT54827.2022.9984247.

[11] A. Altaleb et al., "Decentralized autonomous organizations review, importance, and applications," in Proc. Int. Conf. Intelligent Engineering Systems, 2022, doi: 10.1109/INES56734.2022.9922656.

[12] A. Karanjai et al., "DIaC: Re-imagining decentralized infrastructure as code using blockchain," IEEE Trans. Network and Service Management, 2023, doi: 10.1109/tnsm.2023.3325768.

[13] B. Steinkogler, "Public values and the interests of big tech companies: The case of the Austrian Contact Tracing App Stopp Corona," in Proc. CMI, 2021, doi: 10.1109/cmi53512.2021.9663767.

[14] R. Mayasari et al., "SupTech governance in regulatory/supervisory government agencies: a systematic literature review," in Proc. Int. Conf. Information Technology Systems and Innovation, 2022, doi: 10.1109/ICITSI56531.2022.9970863.

[15] S. Sankar et al., "The Social Impact of Smart Cities: A Comprehensive Study with Digital Solutions," in Proc. ICETAS, 2023, doi: 10.1109/icetas59148.2023.10346410.

[16] E. Hill et al., "Digital transformation of the nasa engineering domain," in Proc. IEEE Aerospace Conf., 2024, doi: 10.1109/aero58975.2024.10521274.

[17] Y. Liu et al., "Traffic accident risk prediction of tunnel based on multi-source heterogeneous data fusion," IEEE Access, 2024, doi: 10.1109/access.2024.3358453.

[18] Y. Bai et al., "Research on Information Security Protection System of Distribution Network Based on Zero Trust Architecture," in Proc. ICEI, 2024, doi: 10.1109/icei63732.2024.10917171.

[19] A. Goel et al., "Security issues and threats in cloud computing: Problems and solutions," in Proc. AECE, 2023, doi: 10.1109/aece59614.2023.10428390.

[20] Y. Li et al., "TCEC: Integrity protection for containers by trusted chip on IoT edge computing nodes," IEEE Sensors Journal, 2024, doi: 10.1109/jsen.2024.3445576.

[21] V. R. Banala et al., "Quantitative impact of artificial intelligence on smart cities: a comparative study using federated learning," in Proc. IET, 2025, doi: 10.1049/icp.2025.0853.

[22] P. W. Matingo et al., "Towards Advanced Frugal Innovations for Energy Sustainability in Developing Countries: A Research Agenda," in Proc. ICTMOD, 2024, doi: 10.1109/ictmod63116.2024.10959115.

[23] T. Qiu et al., "DSG-BTra: Differentially semantic-generalized behavioral trajectory for privacy-preserving mobile internet services," IEEE Internet of Things Journal, 2023, doi: 10.1109/jiot.2023.3336988.

[24] J. Lee et al., "P2P power trading between nanogrid clusters exploiting electric vehicles and renewable energy sources," in Proc. CSCI, 2021, doi: 10.1109/csci54926.2021.00349.

[25] X. Wang et al., "Scalable identifier system for industrial internet based on multi-identifier network architecture," IEEE Internet of Things Journal, vol. 23, 2023, doi: 10.1109/JIOT.2021.3137526.

[26] E. H. Houssein et al., "Internet of Things in smart cities: Comprehensive review, open issues, and challenges," IEEE Internet of Things Journal, 2024, doi: 10.1109/jiot.2024.3449753.

[27] T. Chaora et al., "Discourse, challenges, and prospects around the adoption and dissemination of software bills of materials (sboms)," in Proc. ISTAS, 2023, doi: 10.1109/istas57930.2023.10305922.

[28] Y. Gao et al., "Privacy-preserving for dynamic real-time published data streams based on local differential privacy," IEEE Internet of Things Journal, 2023, doi: 10.1109/jiot.2023.3337397.

[29] E. Curry et al., "Next-generation smart environments: From system of systems to data ecosystems," IEEE Intelligent Systems, 2018, doi: 10.1109/MIS.2018.033001418.

[30] P. Radoglou-Grammatikis et al., "Trustworthy analytics in ETSI ZSM: A 5G security case study," IEEE Open Journal of the Communications Society, 2024, doi: 10.1109/ojcoms.2024.3505555.