The Influence of Artificial Intelligence on Data System Security
DOI:
https://doi.org/10.22399/ijcesen.3476Keywords:
Artificial intelligence, Cyber security, , Machine Learning, Deep Learning, Data System Security, Intrusion Detection, Anomaly DetectionAbstract
Data system security has emerged as a top concern for businesses throughout the globe in today's fast-paced digital environment. The increasing sophistication and frequency of cyber-attacks necessitate more sophisticated and flexible forms of defense measures to deal with them. Machine learning, deep learning, and behavioral analytics are all forms of Artificial Intelligence (AI), which has become a revolutionary area of cybersecurity. This paper looks at how AI can enhance the security of a data system in general, and how it can be applied in threat detection, intrusion prevention, malware analysis, and predictive security, in particular. The intertwining of AI technology allows automated responses to potential threats, real-time anomaly detection, and enormous amounts of data processing. This paper gives an informative review of the extent to which AI is transforming the cybersecurity space and enhancing cyber infrastructures through investigating the latest progress and deployment plans.
References
[1] K. Arockiasamy, “The Role of Artificial Intelligence in Cyber Security,” in AI Tools for Protecting and Preventing Sophisticated Cyber Attacks, vol. 8, no. 10, 2023, pp. 1–24. doi: 10.4018/978-1-6684-7110-4.ch001. DOI: https://doi.org/10.4018/978-1-6684-7110-4.ch001
[2] S. S. S. Neeli, “Critical Cybersecurity Strategies for Database Protection Against Cyber Attacks,” J. Artif. Intell. Mach. Learn. Data Sci., vol. 1, no. 1, pp. 2102–2106, Nov. 2022, doi: 10.51219/JAIMLD/sethu-sesha-synam-neeli/461. DOI: https://doi.org/10.51219/JAIMLD/sethu-sesha-synam-neeli/461
[3] S. Okdem and S. Okdem, “Artificial Intelligence in Cybersecurity: A Review and a Case Study,” Appl. Sci., vol. 14, no. 22, Nov. 2024, doi: 10.3390/app142210487. DOI: https://doi.org/10.3390/app142210487
[4] N. Malali and S. R. P. Madugula, “Robustness and Adversarial Resilience of Actuarial AI/ML Models in the Face of Evolving Threats,” Int. J. Innov. Sci. Res. Technol., vol. 10, no. 3, pp. 910–916, Mar. 2025, doi: 10.38124/ijisrt/25mar1287. DOI: https://doi.org/10.38124/ijisrt/25mar1287
[5] M. Mikic and J. Malala, “The impact of artificial intelligence on the future of work,” in The Home in the Digital Age, Milton Park, Abingdon, Oxon ; New York : Routledge, 2021. | Series: Routledge advances in sociology: Routledge, 2021, pp. 143–159. doi: 10.4324/9781003080114-8. DOI: https://doi.org/10.4324/9781003080114-8
[6] P. Piyush, A. A. Waoo, M. P. Singh, P. K. Pareek, S. Kamal, and S. V. Pandit, “Strategizing IoT Network Layer Security Through Advanced Intrusion Detection Systems and AI-Driven Threat Analysis,” J. Intell. Syst. Internet Things, vol. 24, no. 2, pp. 195–207, 2024, doi: 10.54216/JISIoT.120215. DOI: https://doi.org/10.54216/JISIoT.120215
[7] M. Zulfadhilah, “The Importance Of Securing Digital Data,” Proc. 2nd Sari Mulia Int. Conf. Heal. Sci. 2017 (SMICHS 2017), vol. 6, pp. 431–435, 2017, doi: 10.2991/smichs-17.2017.53. DOI: https://doi.org/10.2991/smichs-17.2017.53
[8] V. Thangaraju, “Enhancing Web Application Performance and Security Using AI-Driven Anomaly Detection and Optimization Techniques,” Int. Res. J. Innov. Eng. Technol., vol. 09, no. 03, pp. 205–212, 2025, doi: 10.47001/IRJIET/2025.903027. DOI: https://doi.org/10.47001/IRJIET/2025.903027
[9] P. Goyal, P. Sharma, M. Sharma, and A. Pareek, “The Importance of Data Encryption in Data Security,” J. Nonlinear Anal. Optim., vol. 13, no. 1, pp. 1–11, 2023, doi: 10.36893/jnao.2022.v13i02.001-011. DOI: https://doi.org/10.36893/JNAO.2022.V13I02.001-011
[10] W. Febriyani, T. F. Kusumasari, and M. Lubis, “Data Security: A Systematic Literature Review and Critical Analysis,” in 2023 International Conference on Advancement in Data Science, E-learning and Information System (ICADEIS), IEEE, Aug. 2023, pp. 1–6. doi: 10.1109/ICADEIS58666.2023.10270832. DOI: https://doi.org/10.1109/ICADEIS58666.2023.10270832
[11] A. Mishra, “AI-Powered Cybersecurity Framework for Secure Data Transmission in Iot Network,” Int. J. Adv. Eng. Manag., vol. 7, no. 3, pp. 05–13, Mar. 2025, doi: 10.35629/5252-07030513. DOI: https://doi.org/10.35629/5252-07030513
[12] R. A. Teimoor, “A Review of Database Security Concepts, Risks, and Problems,” UHD J. Sci. Technol., vol. 5, no. 2, pp. 38–46, 2021, doi: 10.21928/uhdjst.v5n2y2021.pp38-46. DOI: https://doi.org/10.21928/uhdjst.v5n2y2021.pp38-46
[13] Y. Wang, J. Xi, and T. Cheng, “The Overview of Database Security Threats’ Solutions: Traditional and Machine Learning,” J. Inf. Secur., vol. 12, no. 01, pp. 34–55, 2021, doi: 10.4236/jis.2021.121002. DOI: https://doi.org/10.4236/jis.2021.121002
[14] A. A. Wells, K. Ajeigbe, and M. Stern, “Security Trends in Networking : From Traditional Approaches to Zero Trust Architectures,” 2025.
[15] V. Kolluri, “A Detailed Analysis of AI as a Double-Edged Sword: AI-Enhanced Cyber Threats Understanding and Mitigation,” Int. J. Creat. Res. Thoughts, vol. 8, no. 7, pp. 2320–2882, 2020.
[16] Adish K and Venkatesh, “A Review Paper on Cyber Security,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 11, no. 10, pp. 528–531, Mar. 2022, doi: 10.48175/IJARSCT-2920. DOI: https://doi.org/10.48175/IJARSCT-2920
[17] S. Chatterjee, “Risk Management in Advanced Persistent Threats (APTs) for Critical Infrastructure in the Utility Industry,” Int. J. Multidiscip. Res., vol. 3, no. 4, pp. 1–10, Aug. 2021, doi: 10.36948/ijfmr.2021.v03i04.34396. DOI: https://doi.org/10.36948/ijfmr.2021.v03i04.34396
[18] A. K. Polinati, “AI-Powered Anomaly Detection in Cybersecurity: Leveraging Deep Learning for Intrusion Prevention,” Int. J. Commun. Networks Inf. Secur., vol. 17, no. 3, pp. 301–323, 2025.
[19] A. Mishra, “AI-Powered Cyber Threat Intelligence System for Predicting and Preventing Cyber Attacks,” Int. J. Adv. Eng. Manag., vol. 7, no. 2, pp. 873–892, Feb. 2025, doi: 10.35629/5252-0702873892. DOI: https://doi.org/10.35629/5252-0702873892
[20] K. S. Kandala, D. V. Sai, N. Saketh, I. Neelima, and B. Alekhya, “Artificial Intelligence Techniques for Prevention of Cyber Attacks and Detection of Security Threats,” Int. J. Eng. Res. Appl. www.ijera.com, vol. 12, pp. 37–44, 2022, doi: 10.9790/9622-1206053744.
[21] S. S. S. Neeli, “A Hands-On Guide to Data Integrity and Privacy for Database Administrators,” Int. J. Sci. Res. Eng. Manag., vol. 09, no. 01, pp. 1–6, Jan. 2025, doi: 10.55041/IJSREM16443. DOI: https://doi.org/10.55041/IJSREM16443
[22] I. Hamid and M. M. H. Rahman, “AI, machine learning and deep learning in cyber risk management,” Discov. Sustain., vol. 6, no. 1, May 2025, doi: 10.1007/s43621-025-01012-3. DOI: https://doi.org/10.1007/s43621-025-01012-3
[23] R. Dattangire, R. Vaidya, D. Biradar, and A. Joon, “Exploring the Tangible Impact of Artificial Intelligence and Machine Learning: Bridging the Gap between Hype and Reality,” in 2024 1st International Conference on Advanced Computing and Emerging Technologies (ACET), IEEE, Aug. 2024, pp. 1–6. doi: 10.1109/ACET61898.2024.10730334. DOI: https://doi.org/10.1109/ACET61898.2024.10730334
[24] M. J. Correia and F. Matos, “The impact of artificial intelligence on innovation management: A literature review,” Proc. Eur. Conf. Innov. Entrep. ECIE, pp. 222–230, 2021, doi: 10.34190/EIE.21.225. DOI: https://doi.org/10.34190/EIE.21.225
[25] H. Kali, “The Future of HR Cybersecurity: AI-Enabled Anomaly Detection In Workday,” Int. J. Recent Technol. Sci. Manag., vol. 8, no. 6, 2023.
[26] D. D. Rao, S. Madasu, S. R. Gunturu, C. D’Britto, and J. Lopes, “Cybersecurity Threat Detection Using Machine Learning in Cloud-Based Environments: A Comprehensive Study,” Int. J. Recent Innov. Trends Comput. Commun., vol. 12, no. 1, pp. 285–290, 2024.
[27] S. Singamsetty, “Fuzzy-Optimized Lightweight Cyber-Attack Detection for Secure Edge-Based IoT,” J. Crit. Rev., vol. 6, no. 07, pp. 1028–1033, 2019, doi: 10.53555/jcr.v6:i7.13156.
[28] M. Aljanabi, M. A. Ismail, R. A. Hasan, and J. Sulaiman, “Intrusion Detection: A Review,” Mesopotamian J. CyberSecurity, pp. 1–4, Jan. 2021, doi: 10.58496/MJCS/2021/001. DOI: https://doi.org/10.58496/MJCS/2021/001
[29] V. Prajapati, “Enhancing Threat Intelligence and Cyber Defense through Big Data Analytics: A Review Study,” J. Glob. Res. Math. Arch., vol. 12, no. 4, pp. 1–6, 2025, doi: https://zenodo.org/records/15223174.
[30] S. Ness, V. Eswarakrishnan, H. Sridharan, V. Shinde, N. V. P. Janapareddy, and V. Dhanawat, “Anomaly Detection in Network Traffic using Advanced Machine Learning Techniques,” IEEE Access, vol. 10, no. 02, pp. 1063–1067, 2025, doi: 10.1109/ACCESS.2025.3526988. DOI: https://doi.org/10.1109/ACCESS.2025.3526988
[31] N. Prajapati, “Federated Learning for Privacy-Preserving Cybersecurity : A Review on Secure Threat Detection,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 5, no. 4, pp. 520–528, 2025, doi: 10.48175/IJARSCT-25168. DOI: https://doi.org/10.48175/IJARSCT-25168
[32] S. B. Shah, “Machine Learning for Cyber Threat Detection and Prevention in Critical Infrastructure,” Dep. Oper. Bus. Anal. Inf. Syst. (OBAIS, vol. 2, no. 2, pp. 1–7, 2025, doi: 10.5281/zenodo.14955016.
[33] O. Timilehin, “AI in Security Information and Event Management: Transforming User Experience and Decision-Making AI in Security Information and Event Management: Transforming User Experience and Decision-Making Author; Oladoja Timilehin,” 2022.
[34] M. Gorda, “On the Use of Artificial Intelligence in Cybersecurity Incident Investigations,” in 2025 International Russian Smart Industry Conference (SmartIndustryCon), 2025, pp. 747–752. doi: 10.1109/SmartIndustryCon65166.2025.10986177. DOI: https://doi.org/10.1109/SmartIndustryCon65166.2025.10986177
[35] B. Qiu, D. Liu, S. Cao, C. Mu, S. Yan, and Y. Liu, “Risk Analysis and Protection Suggestions for Artificial Intelligence Data Security,” in 2024 IEEE 9th International Conference on Data Science in Cyberspace (DSC), 2024, pp. 392–398. doi: 10.1109/DSC63484.2024.00059. DOI: https://doi.org/10.1109/DSC63484.2024.00059
[36] N. Mohamed, “Artificial Intelligence in Cybersecurity: A Review of Solutions for APT-Exploited Vulnerabilities,” in 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2024, pp. 1–7. doi: 10.1109/ICCCNT61001.2024.10724084. DOI: https://doi.org/10.1109/ICCCNT61001.2024.10724084
[37] P. Shetty, “AI and Security, From an Information Security and Risk Manager Standpoint,” IEEE Access, vol. 12, pp. 77468–77474, 2024, doi: 10.1109/ACCESS.2024.3408144. DOI: https://doi.org/10.1109/ACCESS.2024.3408144
[38] S. Sankar, R. Dutta, and S. Karmakar, “Cyber Threat Prediction and Assessment with Machine Learning Approaches,” in 2024 IEEE 21st India Council International Conference (INDICON), 2024, pp. 1–6. doi: 10.1109/INDICON63790.2024.10958346. DOI: https://doi.org/10.1109/INDICON63790.2024.10958346
[39] R. Barton, P. W. C. Prasad, I. Seher, and A. Elchouemi, “Artificial Intelligence (AI) in Cybersecurity and Inhibitors to AI Adoption,” in 2024 International Conference on Intelligent Education and Intelligent Research (IEIR), 2024, pp. 1–10. doi: 10.1109/IEIR62538.2024.10959777. DOI: https://doi.org/10.1109/IEIR62538.2024.10959777
[40] V. L. Priya, A. A, A. Chahar, and A. A, “Artificial Intelligence as a Tool for Enhanced Data Integrity and Data Security,” in 2023 International Conference on Artificial Intelligence and Smart Communication (AISC), IEEE, Jan. 2023, pp. 781–785. doi: 10.1109/AISC56616.2023.10085250. DOI: https://doi.org/10.1109/AISC56616.2023.10085250
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 International Journal of Computational and Experimental Science and Engineering

This work is licensed under a Creative Commons Attribution 4.0 International License.