Auditability and Change Approval in Automated Storage Provisioning: A Multiple-Case Study of IT Governance Controls

Authors

  • Sara Khan Department of Computer Science and Cloud Systems, University of Oxford Author
  • Karim Amin Department of Computer Science and Cloud Systems, Tecnológico de Monterrey Author

Abstract

Automation can accelerate infrastructure delivery while simultaneously weakening governance if approvals, evidence, and accountability are not embedded in the workflow. We conducted an embedded multiple-case study across six large organisations that had introduced automated storage provisioning at different levels of maturity. Data sources included 147 change records, 38 semi-structured interviews, pipeline logs, approval artefacts, and internal audit findings. Cross-case analysis examined traceability, segregation of duties, approval latency, exception handling, and post-change evidence. Organisations using machine-readable approval gates and immutable execution logs achieved substantially higher traceability than those relying on ticket comments and retrospective documentation. Median approval-to-deployment time was 41% shorter in mature implementations despite stronger control coverage. The most common weaknesses were undocumented emergency overrides, unclear ownership of reusable automation modules, and inconsistent evidence retention. Audit exceptions were concentrated in workflows where technical execution and approval authority were controlled by the same role. The cases show that automation does not inherently conflict with governance; well-designed pipelines can improve both speed and accountability. Governance controls were most effective when implemented as executable workflow requirements rather than external manual checks.

References

1. Nazir M. Automating enterprise storage provisioning with PowerShell and Python: effects on deployment time and configuration errors. Journal of Multidisciplinary Research and Integrated Sciences. 2021;1(1). Available from: https://jmris.com/index.php/jmris/article/view/2021P1V6

2. Haque GMM, Ansari I, Bhujel K, Jahid A, Azam MA. Digital transformation strategies and IT governance: aligning business value with technology investments. The American Journal of Management and Economics Innovations. 2026;8(3):24-48. doi:10.37547/tajmei/Volume08Issue03-02.

3. Bharadwaj A, El Sawy OA, Pavlou PA, Venkatraman N. Digital business strategy: toward a next generation of insights. MIS Q. 2013;37(2):471-482. doi:10.25300/MISQ/2013/37:2.3.

4. Bharadwaj AS. A resource-based perspective on information technology capability and firm performance: an empirical investigation. MIS Q. 2000;24(1):169-196. doi:10.2307/3250983.

5. Chen H, Chiang RHL, Storey VC. Business intelligence and analytics: from big data to big impact. MIS Q. 2012;36(4):1165-1188. doi:10.2307/41703503.

6. Gupta M, George JF. Toward the development of a big data analytics capability. Inf Manag. 2016;53(8):1049-1064. doi:10.1016/j.im.2016.07.004.

7. Mikalef P, Gupta M. Artificial intelligence capability: conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Inf Manag. 2021;58(3):103434. doi:10.1016/j.im.2021.103434.

8. Melville N, Kraemer K, Gurbaxani V. Review: information technology and organizational performance: an integrative model of IT business value. MIS Q. 2004;28(2):283-322. doi:10.2307/25148636.

9. Vial G. Understanding digital transformation: a review and a research agenda. J Strateg Inf Syst. 2019;28(2):118-144. doi:10.1016/j.jsis.2019.01.003.

10. Podsakoff PM, MacKenzie SB, Lee JY, Podsakoff NP. Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol. 2003;88(5):879-903. doi:10.1037/0021-9010.88.5.879.

11. Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res. 1981;18(1):39-50. doi:10.1177/002224378101800104.

12. Henseler J, Ringle CM, Sarstedt M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Mark Sci. 2015;43:115-135. doi:10.1007/s11747-014-0403-8.

13. Lakens D. Sample size justification. Collabra Psychol. 2022;8(1):33267. doi:10.1525/collabra.33267.

14. Nosek BA, Ebersole CR, DeHaven AC, Mellor DT. The preregistration revolution. Proc Natl Acad Sci U S A. 2018;115(11):2600-2606. doi:10.1073/pnas.1708274114.

15. Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016;3:160018. doi:10.1038/sdata.2016.18.

16. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77-101. doi:10.1191/1478088706qp063oa.

17. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. PLoS Med. 2007;4(10):e296. doi:10.1371/journal.pmed.0040296.

18. Efron B. Bootstrap methods: another look at the jackknife. Ann Stat. 1979;7(1):1-26. doi:10.1214/aos/1176344552.

Published

2026-06-01