Artificial Intelligence in Breach Detection : Need of the Hour for Password Management
DOI:
https://doi.org/10.17010/ijf/2026/v20i5/175258Keywords:
artificial intelligence, password management, Generations Y and Z, password cracking, automatic threat detection.JEL Classification Codes :M0, M1, O3
Publication Chronology: Paper Submission Date : August 20, 2025 ; Paper sent back for Revision : February 2, 2026 ; Paper Acceptance Date : March 20, 2026 ; Paper Published Online : May 15, 2026
Abstract
Purpose : The purpose of this study was to examine the role of artificial intelligence (AI) in strengthening the breach detection mechanisms and addressing emerging challenges in password management (PM).
Design/Methodology/Approach : The study adopted a quantitative research design and used primary data from 413 respondents from the National Capital Region, India. The partial least squares-structural equation modeling (Smart PLS-SEM) version 4.1.1.2 method was used to examine the moderating effect of AI on PM.
Findings : The study found that AI impacted password generation, automated threat detection, password reuse detection, user experience, password cracking detection, and password recovery, which led to PM.
Practical Implications : The study highlighted the need for organizations to integrate AI-enabled security solutions to proactively mitigate password-related cyber threats and data breaches.
Originality/Value : This paper contributed to the cybersecurity literature by emphasizing AI as a timely and essential solution for modern breach detection and effective PM in an evolving digital environment.
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1) Ab Hamid, M. R., Sami, W., & Sidek, M. H. (2017). Discriminant validity assessment: Use of Fornell & Larcker criterion versus HTMT criterion. In 1st International Conference on Applied & Industrial Mathematics and Statistics 2017 (ICoAIMS 2017) (Vol. 890, Article ID 012163). IOP Publishing. https://doi.org/10.1088/1742-6596/890/1/012163
2) Abdrabou, Y., Schütte, J., Shams, A., Pfeuffer, K., Buschek, D., Khamis, M., & Alt, F. (2022). “Your eyes tell you have used this password before”: Identifying password reuse from Gaze and Keystroke dynamics. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (Article no. 400, pp. 1–16). Association for Computing Machinery. https://doi.org/10.1145/3491102.3517531
3) Ali, A. K., & Subramanian, R. (2024). Determinants of continuance intention to use mobile payments: A meta-analysis. Indian Journal of Marketing, 54(6), 29–46. https://doi.org/10.17010/ijom/2024/v54/i6/173945
4) Andrea, B., Gabriella, H.-C., & Tímea, J. (2016). Y and Z generations at workplaces. Journal of Competitiveness, 8(3), 90–106. https://doi.org/10.7441/joc.2016.03.06
5) Aramide, O. O. (2023). AI-driven identity verification and authentication in networks: Enhancing accuracy, speed, and security through biometrics and behavioral analytics. Adhyayan: A Journal of Management Sciences, 13(02), 60–69. https://doi.org/10.21567/adhyayan.v13i2.10
6) Asaduzzaman, A., D'Souza, D., Uddin, M. R., & Woldeyes, Y. (2024). Increase security by analyzing password strength using machine learning. In 2024 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON) (pp. 32–37). IEEE. https://10.1109/ECTIDAMTNCON60518.2024.10479995
7) Avkiran, N. K., & Ringle, C. M. (2018). Partial least squares structural equation modeling: Recent advances in banking and finance (1st ed.). Springer Cham. https://doi.org/10.1007/978-3-319-71691-6
8) Bansal, N. (2020a). Awareness of password management and adoption of digital banking services in rural India. Finance India, 34(2), 861–874. https://www.researchgate.net/publication/372344657
9) Bansal, N. (2020b). The impact of training programme of digital banking services to employees of unorganised sector and their acceptability in India. International Journal of Electronic Customer Relationship Management, 12(3), 246–272. https://doi.org/10.1504/IJECRM.2020.110042
10) Bansal, N. (2026a). Artificial Intelligence in password-less authentication: Bridging the gap between security and transparency. Journal of Operational Risk, 21(1), 1–27. http://doi.org/10.21314/JOP.2026.001
11) Bansal, N. (2026b). Artificial intelligence and digital financial inclusion: A need of Indian economy. Context Aware and Personalised Educational Techniques [Press Release].
12) Bansal, N., Pareek, N., & Nigam, A. (2022). The impact of customer's training of digital banking services on its acceptability by customers' in India. International Journal of Business and Globalisation, 30(2), 207–231. https://doi.org/10.1504/IJBG.2022.122665
13) Bansal, N., & Gupta, M. (2026). Password management strategies and adoption of digital services in India. International Journal of Business Performance Management, 27(5). https://doi.org/10.1504/IJBPM.2026.10066337
14) Bansal, N., & Pandey, K. N. (2023). Impact of password management strategies on the trust enhancement in the digital era. International Journal of Business Performance Management, 24(3–4), 484–503. https://doi.org/10.1504/IJBPM.2023.132333
15) Bansal, P., & Ouda, A. (2024). Continuous authentication in the digital age: An analysis of reinforcement learning and behavioral biometrics. Computers, 13(4), Article no. 103. https://doi.org/10.3390/computers13040103
16) Bernard, H. R. (2002). Research methods in anthropology: Qualitative and quantitative approaches (3rd ed.). AltaMira Press, Walnut Creek.
17) Biesner, D., Cvejoski, K., Georgiev, B., Sifa, R., & Krupicka, E. (2021). Advances in password recovery using generative deep learning techniques. In 30th International Conference on Artificial Neural Networks (pp. 15–27). Springer International Publishing. https://doi.org/10.1007/978-3-030-86365-4_2
18) Bordian, M., Gil-Saura, I., & Šerić, M. (2023). The impact of value co-creation in sustainable services: Understanding generational differences. Journal of Services Marketing, 37(2), 155–167. https://doi.org/10.1108/JSM-06-2021-0234
19) Casalegno, C., Candelo, E., & Santoro, G. (2022). Exploring the antecedents of green and sustainable purchase behaviour: A comparison among different generations. Psychology & Marketing, 39(5), 1007–1021. https://doi.org/10.1002/mar.21637
20) Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In Modern methods for business research (1st ed.). Psychology Press.
21) Choudhury, R., & Gupta, S. (2025). Augmentation of financial inclusion: A structured review from the Indian perspective. Indian Journal of Finance, 19(5), 58–78. https://doi.org/10.17010//ijf/2025/v19i5/175044
22) Cialdini, R. B. (2009). Influence: Science and practice (Vol. 4, pp. 51–96). Pearson Education. https://www.scirp.org/reference/referencespapers?referenceid=3909498
23) Dani, J., McCulloh, B., & Saxena, N. (2024). When AI defeats password deception! A deep learning framework to distinguish passwords and honeywords. arXiv. https://doi.org/10.48550/arXiv.2407.16964
24) Delshadi, A. M., Aziz, M. M., Qayyum, M. U., Iqbal, M. W., Hamid, K., Abbas, F., Liaqat, H., & Ibrar, M. (2025). AI-based fake login attempt detection system using behavioral analytics. Spectrum of Engineering Sciences, 3(8), 1043–1056. https://doi.org/10.5281/zenodo.16991098
25) Edeh, E., Lo, W. J., & Khojasteh, J. (2023). [Review of the book Partial least squares structural equation modeling (PLS-SEM) using R: A workbook, by J. F. Hair Jr., G. T. M. Hult, C. M. Ringle, M. Sarstedt, N. P. Danks, & S. Ray]. Structural Equation Modeling: A Multidisciplinary Journal, 30(1), 165–167. https://doi.org/10.1080/10705511.2022.2108813
26) Falk, R. F., & Miller, N. B. (1992). A primer for soft modelling. University of Akron Press.
27) Farooq, U. (2020). Real time password strength analysis on a web application using multiple machine learning approaches. International Journal of Engineering Research & Technology, 9(12), 359–364. https://doi.org/10.17577/IJERTV9IS120146
28) Fogg, B. J. (2003). Persuasive technology: Using computers to change what we think and do. Elsevier Inc. https://doi.org/10.1016/B978-1-55860-643-2.X5000-8
29) Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
30) George, D., & Mallery, P. (2003). SPSS for Windows step by step: A simple guide and reference 11.0 update (4th ed.). Allyn & Bacon.
31) George, D., & Mallery, P. (2010). SPSS for Windows step by step: A simple guide and reference, 17.0 update (10th ed.). Pearson.
32) Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11- 2018-0203
33) Hair, J. F., Sarstedt, M., & Ringle, C. M. (2019). Rethinking some of the rethinking of partial least squares. European Journal of Marketing, 53(4), 566–584. https://doi.org/10.1108/EJM-10- 2018-0665
34) Hanif, A. A., & Ilyas, M. (2024). Enhance the detection of DoS and Brute force attacks within the MQTT environment through feature engineering and employing an ensemble technique. arXiv. https://doi.org/10.48550/arXiv.2408.00480
35) Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
36) Hitaj, B., Gasti, P., Ateniese, G., & Perez-Cruz, F. (2020). PassGAN: A deep learning approach for password guessing. arXiv. https://doi.org/10.48550/arXiv.1709.00440
37) Hubbard, J., Bendiab, G., & Shiaeles, S. (2022). IPASS: A novel open-source intelligence password scoring system. In 2022 IEEE International Conference on Cyber Security and Resilience (CSR) (pp. 90–95). IEEE. https://doi.org/10.1109/CSR54599.2022.9850311
38) Jananee, V., Krishnan, R., & Rakesh, V. S. (2024). AI-powered dynamic images: A new frontier in graphical password authentication. In 2024 International Conference on Emerging Research in Computational Science (ICERCS) (pp. 1–8). IEEE. https://doi.org/10.1109/ICERCS63125.2024.10894909
39) Jawed, H., Ziad, Z., Khan, M. M., & Asrar, M. (2018). Anomaly detection through keystroke and tap dynamics implemented via machine learning algorithms. Turkish Journal of Electrical Engineering and Computer Sciences, 26(4), 1698–1709. https://doi.org/10.3906/elk-1711-410
40) Joseph, D., Girish, S., & Suresh, G. (2023). FinTech and financial capability, what do we know and what we do not know: A scoping review. Indian Journal of Finance, 17(12), 40–55. https://doi.org/10.17010/ijf/2023/v17i12/170910
41) Kamol, M. M., Siddiky, M. S., Anwar, F., Khan, A.-M., & Salam, A. (2024). Credentials stuffing attack prevention using machine learning. In 2024 27th International Conference on Computer and Information Technology (ICCIT) (pp. 2899–2904). IEEE. https://doi.org/10.1109/ICCIT64611.2024.11022023
42) Karangara, R. (2025). Adaptive machine learning models for securing payment gateways: A resilient approach to mitigating evolving cyber threats in digital transactions. Artificial Intelligence Evolution, 6(1), 44–64. https://doi.org/10.37256/aie.6120256290
43) Karthick, R. (2025). A comprehensive survey on AI-enabled cloud security, DevSecOps, and scalable digital infrastructure. Preprints.org. https://doi.org/10.20944/preprints202507.1103.v1
44) Lakshmi, A., & Saldanha, A. T. (2025). Factors influencing behavioral intentions of senior citizens in adopting mobile financial services. Indian Journal of Marketing, 55(5), 26–44. https://doi.org/10.17010/ijom/2025/v55/i5/175018
45) Lee, K., Lee, J., Choi, C., & Yim, K. (2021). Offensive security of keyboard data using machine learning for password authentication in IoT. IEEE Access, 9, 10925–10939. https://doi.org/10.1109/ACCESS.2021.3050239
46) Malhotra, N. K. (2022). Marketing research: an applied orientation (7th ed.). Pearson.
47) Marzi, G., Balzano, M., Egidi, L., & Magrini, A. (2023). CLC estimator: A tool for latent construct estimation via congeneric approaches in survey research. Multivariate Behavioral Research, 58(6), 1160–1164. https://doi.org/10.1080/00273171.2023.2193718
48) Melancon, J. P., Forbes, L. P., & Fugate, D. (2015). Selected dimensions of service gender: A study of perceptions of Generation Y. Journal of Services Marketing, 29(4), 293–301. https://doi.org/10.1108/JSM-04-2014-0148
49) Mohapatra, A. K., Sahoo, A. P., Tripathy, P., Matta, R., & Saxena, A. (2026). Adoption of explainable artificial intelligence in retail investors' decision-making: Evidence from India. Indian Journal of Finance, 20(1), 24–37. https://doi.org/10.17010/ijf/2026/v20i1/175903
50) Mukherjee, S., Thapliyal, K., Paul, U., Bhandari, R. S., Sinha, A., & Kumar, Y. (2025). AI-powered threat intelligence: Enhancing real-time cyber threat detection and response. In 2025 International Conference on Engineering, Technology & Management (ICETM) (pp. 1–5). IEEE. https://doi.10.1109/ICETM63734.2025.11051359
51) Nallapareddy, V. S. (2024). Modified AI-based learning approach to secure data using deep learning algorithms. In 2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC) (pp. 1–7). IEEE. https://doi.org/10.1109/ICEC59683.2024.10837269
52) Nam, S., Jeon, S., & Moon, J. (2020). A new password cracking model with generative adversarial networks. In I. You (ed.), International Security Applications (Vol. 11897, pp. 247–258). Springer International Publishing. https://doi.org/10.1007/978-3-030-39303-8_19
53) Nizamani, S. Z., Hassan, S. R., Shaikh, R. A., Abozinadah, E. A., & Mehmood, R. (2021). A novel hybrid textual-graphical authentication scheme with better security, memorability, and usability. IEEE Access, 9, 51294–51312. https://doi.org/10.1109/ACCESS.2021.3069164
54) Obilor, E. I. (2023). Convenience and purposive sampling techniques: Are they the same? International Journal of Innovative Social & Science Education Research, 11(1), 1–7. https://www.researchgate.net/publication/398681103
55) Pahari, S., Manna, A., & Biswas, D. (2023). Pay with confidence: A thematic analysis of user intentions and perceptions on third-party and banking payment apps. Indian Journal of Finance, 17(5), 25–38. https://doi.org/10.17010/ijf/2023/v17i5/172735
56) Pandey, P. (2025). AI-enabled multi-factor authentication (MFA) systems for private and public cloud security. In 2025 International Conference on Electronics and Renewable Systems (ICEARS) (pp. 886–889). IEEE. https://doi.org/10.1109/ICEARS64219.2025.10941462
57) Pawale, A., Inchal, B., Bogur, S., Redekar, P., & Sonwalkar, P. K. (2025). Beyond passwords: The essence and impact of multi-factor authentication in cybersecurity. In 2025 6th International Conference for Emerging Technology (INCET) (pp. 1–5). IEEE. https://doi.org/10.1109/INCET64471.2025.11140909
58) Picard, C., & Pierre, S. (2023). RLAuth: A risk-based authentication system using reinforcement learning. IEEE Access, 11, 61129–61143. https://doi.org/10.1109/ACCESS.2023.3286376
59) Rahman, T., Rohan, R., Pal, D., & Kanthamanon, P. (2021). Human factors in cybersecurity: A scoping review. In Proceedings of the 12th International Conference on Advances in Information Technology (pp. 1–11). Association for Computing Machinery. https://doi.org/10.1145/3468784.3468789
60) Rengkung, B. J., Royan, N., & Roestam, R. (2024). Enhancing password security through K-means clustering and entropy-based classification using machine learning and deep learning. In 2024 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob) (pp. 56–62). IEEE. https://doi.org/10.1109/APWiMob64015.2024.10792955
61) Ringle, C. M., da Silva, D., & de Souza Bido, D. (2014). Modeling of structural equations with Smartpls use. ReMark - Revista Brasileira de Marketing, 13(2), 56–73. https://doi.org/10.5585/remark.v13i2.2717
62) Rooparaghunath, R. H., Harikrishnan, T. S., & Gupta, D. (2020). Trenchcoat: Human-computable hashing algorithms for password generation. arXiv. https://doi.org/10.48550/arXiv.2310.12706
63) Sadasivam, G. K., Sharma, A. D., Kumar, A., Aggarwal, A., & Kumar, A. (2026). A novel hybrid model that generates passwords. In J. Choudrie, P. N. Mahalle, T. Perumal & A. Joshi (eds.), ICT for intelligent systems. ICTIS 2025 (Vol. 1517, pp. 471–481). Springer Nature. https://doi.org/10.1007/978-981-96-8895-1_40
64) Sardana, V., Mohapatra, A. K., Singhania, S., & Chakrabarti, D. (2024). Changing dynamics of banking landscape: What do we know and what lies ahead? Prabandhan: Indian Journal of Management, 17(1), 8–23. https://doi.org/10.17010/pijom/2024/v17i1/173288
65) Sarnacchiaro, P., & Boccia, F. (2018). Some remarks on measurement models in the structural equation model: An application for socially responsible food consumption. Journal of Applied Statistics, 45(7), 1193–1208. https://doi.org/10.1080/02664763.2017.1363162
66) Sarower, A. H., Bhuiyan, T., Hasan, M. M., Arefin, M. S., & Hossain, G. (2025). SMFA: Strengthening multi-factor authentication with steganography for enhanced security. IEEE Access, 13, 43593–43606. https://doi.org/10.1109/ACCESS.2025.3545769
67) Shams, M. S., Mei, T. S., & Adnan, Z. (2021). Nexus between idiosyncratic deals and work engagement via psychological empowerment: A PLS-SEM approach. Management Science Letters, 11, 689–698. https://doi.org/10.5267/j.msl.2020.8.037
68) Sikder, A. K., Aksu, H., & Uluagac, A. S. (2020). A context-aware framework for detecting sensor-based threats on smart devices. IEEE Transactions on Mobile Computing, 19(2), 245–261. https://doi.org/10.1109/TMC.2019.2893253
69) Siow, J. Y., Chan, A., Østbye, T., Cheng, G. H., & Malhotra, R. (2017). Validity and reliability of the positive aspects of caregiving (PAC) scale and development of its shorter version (S-PAC) among family caregivers of older adults. The Gerontologist, 57(4), e75–e84. https://doi.org/10.1093/geront/gnw198
70) Tejani, A. S., Ng, Y. S., Xi, Y., & Rayan, J. C. (2024). Understanding and mitigating bias in imaging artificial intelligence. RadioGraphics, 44(5), Article ID e230067. https://doi.org/10.1148/rg.230067
71) Tiwari, S., & Mishra, R. (2023). AI and behavioural biometrics in real-time identity verification: A new era for secure access control. SSRN. https://doi.org/10.2139/ssrn.5259379
72) Xu, M., Qian, F., Mei, Q., Huang, K., & Liu, X. (2018). DeepType: On-device deep learning for input personalization service with minimal privacy concern. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2(4), 1–26. https://doi.org/10.1145/3287075
73) Zuur, A. F., Ieno, E. N., & Elphick, C. S. (2010). A protocol for data exploration to avoid common statistical problems. Methods in Ecology and Evolution, 1(1), 3–14. https://doi.org/10.1111/j.2041-210X.2009.00001.x
