Open Access

Intelligent Lifecycle Management and Security Governance of Non-Human Identities in Cloud Infrastructure

https://doi.org/10.55640/irjaet-v03i09-01

PDF

Erion Kodra ¹ , Elira Hoxha ¹ ,

⁴ Department of Computer Science, Albanian Institute of Technology, Albania

Abstract

The rapid adoption of cloud infrastructure has transformed identity management from a predominantly human-centric security function into a broader governance problem involving applications, services, workloads, automation pipelines, containers, machine accounts, and other non-human identities (NHIs). Unlike human identities, NHIs frequently operate continuously, interact programmatically, inherit permissions through infrastructure configurations, and may remain active beyond their intended operational lifetime. Consequently, ineffective lifecycle management can create excessive privileges, unmanaged credentials, stale service identities, and difficult-to-audit access relationships. This paper develops an intelligent lifecycle management and security governance framework for NHIs in cloud infrastructure by synthesizing concepts from the provided literature on time-series representation, dimensionality reduction, symbolic representation, segmentation, event reasoning, and predictive maintenance. The central proposition is that NHI governance can be improved by treating identity activity as a continuously evolving operational data stream rather than as a static inventory. The proposed framework combines identity discovery, behavioral representation, temporal segmentation, risk assessment, lifecycle state determination, policy enforcement, and continuous governance. The literature indicates that compact representations of temporal behavior can support scalable analysis, while event-driven and knowledge-oriented approaches can improve interpretation of evolving identity activity. The framework therefore integrates temporal analytics with policy-based security controls to support adaptive decisions concerning identity creation, authorization, rotation, suspension, and retirement. The paper concludes that intelligent NHI governance should move from periodic credential administration toward continuous, evidence-based lifecycle control.

Keywords

Non-Human Identities, Cloud Infrastructure, Identity Lifecycle Management, Security Governance

References

M. Dasoomi et al., “Predicting the Choice of Online or Offline Shopping Trips Using a Deep Neural Network Model and Time Series Data: A Case Study of Tehran, Iran,” Sustainability, vol. 15, no. 20, p. 14764, 2023.

Y. Hu et al., “A Novel Segmentation and Representation Approach for Streaming Time Series,” IEEE Access, vol. 7, pp. 184423–184437, 2019.

N. Q. V. Hung and D. T. Anh, “An Improvement of PAA for Dimensionality Reduction in Large Time Series Databases,” in Proceedings of the PRICAI 2008: Trends in Artificial Intelligence, Lecture Notes in Computer Science, 2008, pp. 698–707.

C. Ji et al., “A Piecewise Linear Representation Method Based on Importance Data Points for Time Series Data,” in Proceedings of the 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD), Nanchang, China, 2016, pp. 111–116.

E. Keogh, “Fast Similarity Search in the Presence of Longitudinal Scaling in Time Series Databases,” in Proceedings of the 9th IEEE International Conference on Tools with Artificial Intelligence, Newport Beach, CA, USA, 2002.

Y. Li et al., “Piecewise Linear Representation Based on Time Series Volatility,” Computer Systems & Applications, vol. 30, no. 6, pp. 300–305, 2021.

J. Lin et al., “A Symbolic Representation of Time Series, with Implications for Streaming Algorithms,” in Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, San Diego, CA, USA, 2003.

Y. Y. Liu et al., “Trend Feature Extraction Method for Time Series Based on Turning Point and Trend Segment,” Journal of Computer Applications, vol. 40, pp. 92–97, 2020.

B. Lkhagva et al., “New Time Series Data Representation ESAX for Financial Applications,” in Proceedings of the 22nd International Conference on Data Engineering Workshops (ICDEW’06), Atlanta, GA, USA, 2006.

J. Pengtao et al., “Error Restricted Piecewise Linear Representation of Time Series Based on Special Points,” in Proceedings of the 2008 7th World Congress on Intelligent Control and Automation, Chongqing, China, 2008, pp. 2059–2064.

Y. Sun et al., “An Improvement of Symbolic Aggregate Approximation Distance Measure for Time Series,” Neurocomputing, vol. 138, pp. 189–198, 2014.

X. Wang et al., “Data-Driven and Knowledge-Based Predictive Maintenance Method for Industrial Robots for the Production Stability of Intelligent Manufacturing,” Expert Systems with Applications, vol. 234, pp. 121136.1–121136.20, 2023.

H. Wu et al., “Online Event-Driven Subsequence Matching Over Financial Data Streams,” in Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, Paris, France, 2004, pp. 23–34.

B. Zhou et al., “Semantic-Aware Event Link Reasoning Over Industrial Knowledge Graph Embedding Time Series Data,” International Journal of Production Research, pp. 4117–4134, 2023.

Ganapathy, S. K. (2025). Automated Governance and Protection of Non-Human Identities in Cloud IAM. Frontiers in Emerging Computer Science and Information Technology, 2(02), 08–20. Retrieved from https://irjernet.com/index.php/fecsit/article/view/cloud-iam-non-human-identities

Most read articles by the same author(s)

<< < 2 3 4 5 6 7 8 > >>

International Research Journal of Advanced Engineering and Technology

Intelligent Lifecycle Management and Security Governance of Non-Human Identities in Cloud Infrastructure

Abstract

Keywords

References

Most read articles by the same author(s)

Similar Articles