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Abstract

Purpose: This study aims to evaluate the impact of analytic technology on the audit process in the digital age, with a particular focus on emerging security risks. The research investigates how technologies such as Big Data Analytics (BDA), Audit Data Analytics (ADA), and Artificial Intelligence (AI) enhance audit efficiency while addressing associated security vulnerabilities.


Research Design and Methodology: The study employs a systematic literature review (SLR) methodology to synthesize existing empirical and theoretical insights related to digital audit technologies and security risks. By reviewing academic articles, case studies, and industry reports, the research provides a comprehensive understanding of the current landscape of digital audits.


Findings and Discussion: The findings reveal that analytic technologies significantly enhance the accuracy and efficiency of audits by enabling real-time data analysis and predictive insights. However, adopting these technologies introduces new security risks, such as cyberattacks, data breaches, and algorithmic biases. The research also highlights the need for robust data governance, auditor training, and regulatory adaptation to ensure these technologies contribute to secure and transparent audit processes.


Implications: The study provides valuable implications for both practice and policy. It emphasizes the need for organizations to integrate advanced technologies while safeguarding audit integrity and security. The study calls for enhanced data governance, continuous auditor training, and the revision of audit standards to accommodate the evolving digital landscape, ensuring that technology adoption does not compromise audit quality.

Keywords

Analytic Technology Digital Audit Security Risks Big Data Analytics Data Governance

Article Details

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How to Cite
Sutisna, E. (2025). Evaluating Security Risks and the Impact of Analytic Technology on the Audit Process. Advances in Managerial Auditing Research, 3(1), 30–43. https://doi.org/10.60079/amar.v3i1.419
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References

  1. Abdelwahed, A. S., Abu-Musa, A. A. E. S., Badawy, H. A. E. S., & Moubarak, H. (2024). Investigating the impact of adopting big data and data analytics on enhancing audit quality. Journal of Financial Reporting and Accounting, ahead-of-print(ahead-of-print). https://doi.org/10.1108/JFRA-12-2023-0724
  2. Alles, M. G., & Gray, G. L. (2020). Will the medium become the message? A framework for understanding the coming automation of the audit process. Journal of Information Systems, 34(2), 109–130. https://doi.org/10.2308/isys-52633
  3. Almanasra, S. (2024). Applications of integrating artificial intelligence and big data: A comprehensive analysis. 33(1). https://doi.org/doi:10.1515/jisys-2024-0237
  4. Ansar, N., Parveen, S., Alankar, B., & Khan, I. R. (2024). Cost-Effective Cybersecurity Framework for Small and Medium-Sized Enterprises BT - Deep Learning and Visual Artificial Intelligence (V. Goar, A. Sharma, J. Shin, & M. F. Mridha (eds.); pp. 133–155). Springer Nature Singapore. https://doi.org/10.1007/978-981-97-4533-3_11
  5. Appelbaum, D., Kogan, A., & Vasarhelyi, M. A. (2017). Big Data and analytics in the modern audit engagement: Research needs. Auditing: A Journal of Practice & Theory, 36(4), 1–27.
  6. Bakri, A. A., Yusni, Y., & Botutihe, N. (2023). Analisis Efektivitas Penggunaan Teknologi Big Data dalam Proses Audit: Studi Kasus pada Kantor Akuntan Publik di Indonesia. Jurnal Akuntansi Dan Keuangan West Science, 2(03), 179–186. https://doi.org/10.58812/jakws.v2i03.641
  7. Bierstaker, J., Janvrin, D., & Lowe, D. J. (2014). What factors influence auditors’ use of computer-assisted audit techniques? Advances in Accounting, 30(1), 67–74. https://doi.org/https://doi.org/10.1016/j.adiac.2013.12.005
  8. Bleier, A., De Keyser, A., & Verleye, K. (2018). Customer Engagement Through Personalization and Customization BT - Customer Engagement Marketing (R. W. Palmatier, V. Kumar, & C. M. Harmeling (eds.); pp. 75–94). Springer International Publishing. https://doi.org/10.1007/978-3-319-61985-9_4
  9. Bozkus Kahyaoglu, S., & Caliyurt, K. (2018). Cyber security assurance process from the internal audit perspective. Managerial Auditing Journal, 33(4), 360–376. https://doi.org/10.1108/MAJ-02-2018-1804
  10. De Santis, F. (2024). Artificial Intelligence in Auditing BT - Artificial Intelligence in Accounting and Auditing: Accessing the Corporate Implications (M. Pierotti, A. Monreale, & F. De Santis (eds.); pp. 193–208). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-71371-2_9
  11. Ditkaew, K., & Suttipun, M. (2023). The impact of audit data analytics on audit quality and audit review continuity in Thailand. Asian Journal of Accounting Research, 8(3), 269–278. https://doi.org/10.1108/AJAR-04-2022-0114
  12. Efunniyi, C. P., Abhulimen, A. O., Obiki-Osafiele, A. N., Osundare, O. S., Agu, E. E., & Adeniran, I. A. (2024). Strengthening corporate governance and financial compliance: Enhancing accountability and transparency. Finance & Accounting Research Journal, 6(8), 1597–1616. https://doi.org/10.51594/farj.v6i8.1509
  13. Fedyk, A., Hodson, J., Khimich, N., & Fedyk, T. (2022). Is artificial intelligence improving the audit process? Review of Accounting Studies, 27(3), 938–985. https://doi.org/10.1007/s11142-022-09697-x
  14. Gauthier, M. P., & Brender, N. (2021). How do the current auditing standards fit the emergent use of blockchain? Managerial Auditing Journal, 36(3), 365–385. https://doi.org/10.1108/MAJ-12-2019-2513
  15. Haapamäki, E., & Sihvonen, J. (2019). Cybersecurity in accounting research. Managerial Auditing Journal, 34(7), 808–834. https://doi.org/10.1108/MAJ-09-2018-2004
  16. Haber, M. J., Chappell, B., & Hills, C. (2022). Mitigation Strategies BT - Cloud Attack Vectors: Building Effective Cyber-Defense Strategies to Protect Cloud Resources (M. J. Haber, B. Chappell, & C. Hills (eds.); pp. 221–296). Apress. https://doi.org/10.1007/978-1-4842-8236-6_7
  17. Hamdan, S. A. R., & Al Habashneh, A. K. (2024). The Advantages and Difficulties of Using AI and BT in the Auditing Procedures: A Literature Review BT - Artificial Intelligence-Augmented Digital Twins: Transforming Industrial Operations for Innovation and Sustainability (A. M. A. Musleh Al-Sartawi, A. A. Al-Qudah, & F. Shihadeh (eds.); pp. 111–126). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-43490-7_9
  18. Hanfy, F., Alakkas, A. A., & Alhumoudi, H. (2024). Analyzing the role of digitalization and its impact on auditing. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-024-19729-0
  19. Huang, C., Mirza, S. S., Zhang, C., & Miao, Y. (2024). Corporate digital transformation and audit signals: building trust in the digital age. Meditari Accountancy Research, ahead-of-print(ahead-of-print). https://doi.org/10.1108/MEDAR-10-2023-2187
  20. Jensen, M. C., & Meckling, W. H. (1976). Also published in Foundations of Organizational Strategy. Journal of Financial Economics, 4, 305–360. http://ssrn.com/abstract=94043Electroniccopyavailableat:http://ssrn.com/abstract=94043http://hupress.harvard.edu/catalog/JENTHF.html
  21. Jensen, M. C., & Meckling, W. H. (1979). Theory of the Firm: Managerial Behavior, Agency Costs, and Ownership Structure BT - Economics Social Institutions: Insights from the Conferences on Analysis & Ideology (K. Brunner (ed.); pp. 163–231). Springer Netherlands. https://doi.org/10.1007/978-94-009-9257-3_8
  22. Karimallah, K., & Drissi, H. (2024). Assessing the Impact of Digitalization on Internal Auditing Function. International Journal of Advanced Computer Science & Applications, 15(6). https://doi.org/10.14569/ijacsa.2024.0150687
  23. Leocádio, D., Malheiro, L., & Reis, J. C. G. dos. (2024). Auditors in the digital age: a systematic literature review. Digital Transformation and Society, ahead-of-print(ahead-of-print). https://doi.org/10.1108/DTS-02-2024-0014
  24. Lois, P., Drogalas, G., Karagiorgos, A., & Tsikalakis, K. (2020). Internal audits in the digital era: opportunities risks and challenges. EuroMed Journal of Business, 15(2), 205–217. https://doi.org/10.1108/EMJB-07-2019-0097
  25. Lokanan, M., Tran, V., & Vuong, N. H. (2019). Detecting anomalies in financial statements using machine learning algorithm. Asian Journal of Accounting Research, 4(2), 181–201. https://doi.org/10.1108/AJAR-09-2018-0032
  26. Majumdar, S., Madi, T., Jarraya, Y., Pourzandi, M., Wang, L., & Debbabi, M. (2019). Cloud Security Auditing: Major Approaches and Existing Challenges BT - Foundations and Practice of Security (N. Zincir-Heywood, G. Bonfante, M. Debbabi, & J. Garcia-Alfaro (eds.); pp. 61–77). Springer International Publishing. https://doi.org/10.1007/978-3-030-18419-3_5
  27. Manita, R., Elommal, N., Baudier, P., & Hikkerova, L. (2020). The digital transformation of external audit and its impact on corporate governance. Technological Forecasting and Social Change, 150, 119751. https://doi.org/https://doi.org/10.1016/j.techfore.2019.119751
  28. Muir, A., Brown, K., & Girma, A. (2024). Reviewing the Effectiveness of Multi-factor Authentication (MFA) Methods in Preventing Phishing Attacks BT - Proceedings of the Future Technologies Conference (FTC) 2024, Volume 4 (K. Arai (ed.); pp. 597–607). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-73128-0_40
  29. Murdock, H. (2021). Operational auditing: Principles and techniques for a changing world. CRC Press.
  30. O’Leary, D. E., Richardson, V. J., & Weidenmier Watson, M. (2024). Data-Driven Audits: Audit Analytic Platforms and General Ledger Analytic Tools. Current Issues in Auditing, 1–9. https://doi.org/10.2308/ciia-2023-027
  31. Özbaltan, N. (2024). Applying Machine Learning To Audit Data: Enhancing Fraud Detection, Risk Assessment And Audit Efficiency. EDPACS, 69(9), 70–86. https://doi.org/10.1080/07366981.2024.2376793
  32. Pratama, M. N. S., Nahong, M. S., Nggi, S. A., & Bhebhe, M. C. (2023). Pengaruh Kecerdasan Buatan Dalam Proses Audit Keuangan: Tantangan Dan Peluang Di Era Digital. Jurnal Locus Penelitian Dan Pengabdian, 2(12), 1181–1190. https://doi.org/10.58344/locus.v2i12.2333
  33. Putra, N. S., Ritchi, H., & Alfian, A. (2023). Hubungan Big Data Analytics terhadap Kualitas Audit: Penerapan pada Instansi Pemerintah. Jurnal Riset Akuntansi Dan Keuangan, 11(1), 57–72. https://doi.org/10.17509/jrak.v11i1.55139
  34. Putri, D. A., & Nizarudin, A. (2024). Peran Big Data Analytics dan Kualitas Audit dalam Memperkuat Kemampuan Auditor Mendeteksi Kecurangan Laporan Keuangan. JEMSI (Jurnal Ekonomi, Manajemen, Dan Akuntansi), 10(3), 1551–1561. https://doi.org/10.35870/jemsi.v10i3.2350
  35. Rahman, A., Subramanian, U., & Dao, M.-S. (2024). The impact of textual and automated analysis in auditing and accounting. International Journal of Business Innovation and Research, 33(2), 192–214. https://doi.org/10.1504/ijbir.2024.136407
  36. Rajesh, Y. S., Kumar, V. G. K., & Poojari, A. (2024). A Unified Approach Toward Security Audit and Compliance in Cloud Computing. Journal of The Institution of Engineers (India): Series B, 105(3), 733–750. https://doi.org/10.1007/s40031-024-01034-x
  37. Richins, G., Stapleton, A., Stratopoulos, T. C., & Wong, C. (2017). Big data analytics: opportunity or threat for the accounting profession? Journal of Information Systems, 31(3), 63–79. https://doi.org/10.2308/isys-51805
  38. Rizvi, S., Ryoo, J., Kissell, J., Aiken, W., & Liu, Y. (2018). A security evaluation framework for cloud security auditing. The Journal of Supercomputing, 74(11), 5774–5796. https://doi.org/10.1007/s11227-017-2055-1
  39. Sanoran, K., & Ruangprapun, J. (2023). Initial implementation of data analytics and audit process management. Sustainability, 15(3), 1766. https://doi.org/10.3390/su15031766
  40. Sargiotis, D. (2024). Data Governance Policies and Standards: Development and Implementation BT - Data Governance: A Guide (D. Sargiotis (ed.); pp. 247–277). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-67268-2_7
  41. Shabani, N., Munir, A., & Mohanty, S. P. (2022). A Study of Big Data Analytics in Internal Auditing BT - Intelligent Systems and Applications (K. Arai (ed.); pp. 362–374). Springer International Publishing.
  42. Skorikov, V., Podlegaev, A., Ponasyan, G., Mikula, A., & Selifanov, V. (2024). Simulation Modeling of Information Security Audit Procedures BT - AISMA-2024: International Workshop on Advanced Information Security Management and Applications (M. Lapina, Z. Raza, A. Tchernykh, M. Sajid, V. Zolotarev, & M. Babenko (eds.); pp. 286–292). Springer Nature Switzerland.
  43. Stein Smith, S. (2024). Audit Implications of AI and Blockchain BT - Blockchain, Artificial Intelligence, and Financial Services: Applications for Finance and Accounting Professionals, including the Cryptoasset Ecosystem (S. Stein Smith (ed.); pp. 179–192). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-74403-7_13
  44. Stepanyan, S. (2023). THE EVOLUTION OF INTERNAL AUDIT IN A DIGITAL ENVIRONMENT. ALTERNATIVE. https://doi.org/10.55528/18292828-2023.2-01
  45. Talha, M., Faisal, S. M., Khan, A. K., & Ngo, N.-Q.-N. (2024). Impact of Artificial Intelligence and Big Data on Auditing: A Study on the Perception of Auditors BT - Achieving Sustainable Business Through AI, Technology Education and Computer Science: Volume 2: Teaching Technology and Business Sustainability (A. Hamdan (ed.); pp. 97–110). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-71213-5_9
  46. Teichmann, F., Boticiu, S. R., & Sergi, B. S. (2023). The evolution of ransomware attacks in light of recent cyber threats. How can geopolitical conflicts influence the cyber climate? International Cybersecurity Law Review, 4(3), 259–280. https://doi.org/10.1365/s43439-023-00095-w