Explainable AI (XAI): Building Trust and Transparency in Security Systems
DOI:
https://doi.org/10.15680/IJCTECE.2025.0805012Keywords:
XAI, cybersecurity, explainability, trust, interpretability, threat detection, automated systems, machine learningAbstract
The paper examines the importance of Explainable AI (XAI) in improving trust and transparency in cybersecurity, and especially in automated threat detection systems. Since AI models will be used in detecting and reducing cyber threats, the unintelligibility of most black-box systems is the concern of cybersecurity experts. XAI will solve these issues by offering the transparent and comprehensible explanations of the AI-based decision making that will enable users to trust the system and its activities. The study uses case studies and data analysis of actual cybersecurity systems to determine the impact of XAI on the system performance and user confidence. The most important results are that XAI enhances decision making because it provides information about the way threat detection models arrive at their conclusions and, thus, enhances more transparency. The analysis makes the conclusion that the application of XAI to cybersecurity leads to better performance of the automated systems and the overall level of trust in the AI technologies in cyber threat protection.
References
[1] Azmi, S. K. (2025). Enhancing Java Virtual Machine Performance for Scalable Artificial Intelligence and Machine Learning Workloads. Well Testing Journal, 34(S3), 566-580.
[2] Syed Khundmir Azmi. (2025). Enhancing Java Virtual Machine Performance for Scalable Artificial Intelligence and Machine Learning Workloads. Well Testing Journal, 34(S3), 566–580. Retrieved from https://welltestingjournal.com/index.php/WT/article/view/221
[3] Azmi, S. K. (2025). Enhancing Java Virtual Machine performance for scalable artificial intelligence and machine learning workloads. Well Testing Journal, 34(S3), 566–580.
[4] Azmi, S. K. (2025). LLM-Aware Static Analysis: Adapting Program Analysis to Mixed Human/AI Codebases at Scale. Global Journal of Engineering and Technology Advances, 24(03), 260-269.
[5] Syed, Khundmir Azmi. (2025). LLM-Aware Static Analysis: Adapting Program Analysis to Mixed Human/AI Codebases at Scale. Global Journal of Engineering and Technology Advances. 24. 10.30574/gjeta.2025.24.3.0284.
[6] Azmi, S. K. (2025). LLM-aware static analysis: Adapting program analysis to mixed human/AI codebases at scale. Global Journal of Engineering and Technology Advances, 24(3), 260–269.
[7] Azmi, Syed Khundmir. “LLM-Aware Static Analysis: Adapting Program Analysis to Mixed Human/AI Codebases at Scale.” Global Journal of Engineering and Technology Advances, vol. 24, no. 3, 30 Sept. 2025, pp. 260–269, https://doi.org/10.30574/gjeta.2025.24.3.0284. Accessed 7 Oct. 2025.
[8] Azmi, S. K. (2023). Trust but Verify: Benchmarks for Hallucination, Vulnerability, and Style Drift in AI-Generated Code Reviews. Well Testing Journal, 32(1), 76-90.
[9] Syed Khundmir Azmi. (2023). Trust but Verify: Benchmarks for Hallucination, Vulnerability, and Style Drift in AI-Generated Code Reviews. Well Testing Journal, 32(1), 76–90. Retrieved from https://welltestingjournal.com/index.php/WT/article/view/229
[10] Azmi, S. K. (2023, February 6). Trust but verify: Benchmarks for hallucination, vulnerability, and style drift in AI-generated code reviews. Well Testing Journal, 32(1), 76–90.
[11] Syed, Khundmir Azmi. (2023). Secure DevOps with AI-Enhanced Monitoring. International Journal of Science and Research Archive. 9. 10.30574/ijsra.2023.9.2.0569.
[12] Syed, Khundmir Azmi. “Secure DevOps with AI-Enhanced Monitoring.” International Journal of Science and Research Archive, vol. 9, no. 2, 30 June 2023, pp. 1193–1200, https://doi.org/10.30574/ijsra.2023.9.2.0569. Accessed 13 Oct. 2025.
[13] Azmi, S. K. (2022). From Assistants to Agents: Evaluating Autonomous LLM Agents in Real-World DevOps Pipeline. Well Testing Journal, 31(2), 118-133.
[14] Azmi, S. K. (2022). From assistants to agents: Evaluating autonomous LLM agents in real-world DevOps pipeline. Well Testing Journal, 31(2), 118–133.
[15] Syed Khundmir Azmi. (2022). From Assistants to Agents: Evaluating Autonomous LLM Agents in Real-World DevOps Pipeline. Well Testing Journal, 31(2), 118–133. Retrieved from https://welltestingjournal.com/index.php/WT/article/view/230
[16] Azmi, S. K. (2022). Green CI/CD: Carbon-Aware Build & Test Scheduling for Large Monorepos. Well Testing Journal, 31(1), 199-213.
[17] Syed Khundmir Azmi. (2022). Green CI/CD: Carbon-Aware Build & Test Scheduling for Large Monorepos. Well Testing Journal, 31(1), 199–213. Retrieved from https://welltestingjournal.com/index.php/WT/article/view/231
[18] Azmi, S. K. (2022). Green CI/CD: Carbon-aware build & test scheduling for large monorepos. Well Testing Journal, 31(1), 199–213.
[19] Azmi, S. K. (2021). Computational Yoshino-Ori Folding for Secure Code Isolation in Serverless It Architectures. Well Testing Journal, 30(2), 81-95.
[20] Azmi, S. K. (2021, October 28). Computational Yoshino-Ori folding for secure code isolation in serverless IT architectures. Well Testing Journal, 30(2), 81–95.
[21] Syed Khundmir Azmi. (2021). Computational Yoshino-Ori Folding for Secure Code Isolation in Serverless It Architectures. Well Testing Journal, 30(2), 81–95. Retrieved from https://welltestingjournal.com/index.php/WT/article/view/237
[22] Azmi, S. K. (2021). Riemannian Flow Analysis for Secure Software Dependency Resolution in Microservices Architectures. Well Testing Journal, 30(2), 66-80.
[23] Azmi, S. K. (2021). Riemannian flow analysis for secure software dependency resolution in microservices architectures. Well Testing Journal, 30(2), 66–80.
[24] Syed Khundmir Azmi. (2021). Riemannian Flow Analysis for Secure Software Dependency Resolution in Microservices Architectures. Well Testing Journal, 30(2), 66–80. Retrieved from https://welltestingjournal.com/index.php/WT/article/view/236
[25] Azmi, S. K. (2025). Voronoi partitioning for secure zone isolation in software-defined cyber perimeters. Global Journal of Engineering and Technology Advances, 24(03), 431-441.
[26] Azmi, S. K. (2025). Voronoi partitioning for secure zone isolation in software-defined cyber perimeters. Global Journal of Engineering and Technology Advances, 24(3), 431–441
[27] Syed, Khundmir Azmi. (2025). Voronoi partitioning for secure zone isolation in software-defined cyber perimeters. Global Journal of Engineering and Technology Advances. 24. 431-441. 10.30574/gjeta.2025.24.3.0294.
[28] Azmi, Syed Khundmir. “Voronoi Partitioning for Secure Zone Isolation in Software-Defined Cyber Perimeters.” Global Journal of Engineering and Technology Advances, vol. 24, no. 3, 30 Sept. 2025, pp. 431–441, https://doi.org/10.30574/gjeta.2025.24.3.0294. Accessed 13 Oct. 2025.
[29] Syed, Khundmir Azmi. (2025). Zero-Trust Architectures Integrated With Blockchain For Secure Multi-Party Computation In Decentralized Finance. INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTS. 13. 2320-2882
[30] Syed, Khundmir Azmi. (2025). Bott-Cher Cohomology For Modeling Secure Software Update Cascades In Iot Networks. INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTS. 13. g1-g12.
[31] Azmi, S. K. (2025). Bott-Cher Cohomology for Modeling Secure Software Update Cascades in IoT Networks. International Journal of Creative Research Thoughts (IJCRT), 13(9)
[32] Syed, Khundmir Azmi. (2025). Retrieval-Augmented Requirements: Using RAG To Elicit, Trace, And Validate Requirements From Enterprise Knowledge Bases.
[33] Azmi, S. K. (2025, September 9). Retrieval-Augmented Requirements: Using RAG to Elicit, Trace, and Validate Requirements from Enterprise Knowledge Bases. International Journal of Creative Research Thoughts (IJCRT), 13(9).
[34] Syed, Khundmir Azmi. (2025). Hypergraph-Based Data Sharding for Scalable Blockchain Storage in Enterprise IT Systems. Journal of Emerging Technologies and Innovative Research. 12. g475-g487.
[35] Azmi, S. K. (2025). Kirigami-Inspired Data Sharding for Secure Distributed Data Processing in Cloud Environments. JETIR, 12(4).
[36] Syed, Khundmir Azmi. (2025). Kirigami-Inspired Data Sharding for Secure Distributed Data Processing in Cloud Environments. Journal of Emerging Technologies and Innovative Research. 12. o78-o91.
[37] Syed, Khundmir Azmi. (2024). Human-in-the-Loop Pair Programming with AI: A Multi-Org Field Study across Seniority Levels. International Journal of Innovative Research in Science Engineering and Technology. 13. 20896-20905. 10.15680/IJIRSET.2024.1312210|.
[38] Azmi, S. K. (2024, October). Klein bottle-inspired network segmentation for untraceable data flows in secure IT systems. IRE Journals. https://www.irejournals.com/formatedpaper/1711014.pdf
[39] Syed, Khundmir Azmi & Azmi,. (2024). Klein Bottle-Inspired Network Segmentation for Untraceable Data Flows in Secure IT Systems. 8. 852-862.
[40] Syed, Khundmir Azmi & Azmi,. (2023). Quantum Zeno Effect for Secure Randomization in Software Cryptographic Primitives. 7. 2456-8880.
[41] Azmi, S. K. (2024, March). Quantum Zeno effect for secure randomization in software cryptographic primitives. IRE Journals. Retrieved from https://www.irejournals.com/paper-details/1711015
[42] Azmi, S. K. (2024). Cryptographic hashing beyond SHA: Designing collision-resistant, quantum-resilient hash functions. International Journal of Science and Research Archive, 12(2), 3119–3127.
[43] Syed, Khundmir Azmi. (2024). Cryptographic Hashing Beyond SHA: Designing collision-resistant, quantum-resilient hash functions. International Journal of Science and Research Archive. 13. 3119-3127. 10.30574/ijsra.2024.12.2.1238.
[44] Azmi, Syed Khundmir. “Cryptographic Hashing beyond SHA: Designing Collision-Resistant, Quantum-Resilient Hash Functions.” International Journal of Science and Research Archive, vol. 12, no. 2, 31 July 2024, pp. 3119–3127, https://doi.org/10.30574/ijsra.2024.12.2.1238. Accessed 9 Oct. 2025.
[45] Syed Khundmir Azmi. (2023). Photonic Reservior Computing or Real-Time Malware Detection in Encrypted Network Traffic. Well Testing Journal, 32(2), 207–223. Retrieved from https://welltestingjournal.com/index.php/WT/article/view/244
[46] Azmi, S. K. (2023, August 31). Photonic reservoir computing or real-time malware detection in encrypted network traffic. Well Testing Journal, 32(2), 207–223.
[47] Azmi, S. K. (2023). Photonic Reservior Computing or Real-Time Malware Detection in Encrypted Network Traffic. Well Testing Journal, 32(2), 207-223.
[48] Syed, Khundmir Azmi. (2025). Algebraic geometry in cryptography: Secure post-quantum schemes using isogenies and elliptic curves. International Journal of Science and Research Archive. 10. 1509-1517. 10.30574/ijsra.2023.10.2.0965.
[49] Azmi, Syed Khundmir. “Algebraic Geometry in Cryptography: Secure Post-Quantum Schemes Using Isogenies and Elliptic Curves.” International Journal of Science and Research Archive, vol. 10, no. 2, 31 Dec. 2023, pp. 1509–1517, https://doi.org/10.30574/ijsra.2023.10.2.0965. Accessed 15 Oct. 2025.
[50] Azmi, S. K. (2023). Algebraic geometry in cryptography: Secure post-quantum schemes using isogenies and elliptic curves. IJSRA. https://ijsra.net/sites/default/files/IJSRA-2023-0965.pdf
[51] Syed, Khundmir Azmi. (2022). Bayesian Nonparametrics in Computer Science: Scalable Inference for Dynamic, Unbounded, and Streaming Data. 5. 399-407.
[52] Azmi, S. K. (2022, April). Bayesian nonparametrics in computer science: Scalable inference for dynamic, unbounded, and streaming data. IRE Journals. https://www.irejournals.com/formatedpaper/1711044.pdf
[53] Syed Khundmir Azmi. (2022). Computational Knot Theory for Deadlock-Free Process Scheduling in Distributed IT Systems. Well Testing Journal, 31(1), 224–239. Retrieved from https://welltestingjournal.com/index.php/WT/article/view/243
[54] Azmi, S. K. (2022, March 30). Computational knot theory for deadlock-free process scheduling in distributed IT systems. Well Testing Journal, 31(1), 224–239.
[55] Azmi, S. K. (2021, September). Markov Decision Processes with Formal Verification: Mathematical Guarantees for Safe Reinforcement Learning. IRE Journals, 5(3) https://www.irejournals.com/formatedpaper/1711043.pdf
[56] Syed, Khundmir Azmi. (2021). Markov Decision Processes with Formal Verification: Mathematical Guarantees for Safe Reinforcement Learning. 5. 418-428.
[57] Conde Camillo da Silva, R., Oliveira Camargo, M. P., Sanches Quessada, M., Lopes, A. C., Diassala Monteiro Ernesto, J., & Pontara da Costa, K. A. (2022). An Intrusion Detection System for Web-Based Attacks Using IBM Watson. IEEE Latin America Transactions, 20(2), 191-197. https://doi.org/10.1109/TLA.2022.9661457
[58] Confalonieri, R., Coba, L., Wagner, B., & Besold, T. R. (2020). A historical perspective of explainable Artificial Intelligence. WIREs Data Mining and Knowledge Discovery, 11(1). https://doi.org/10.1002/widm.1391
[59] Dwivedi, R., Dave, D., Naik, H., Singhal, S., Rana, O., Patel, P., Qian, B., Wen, Z., Shah, T., Morgan, G., & Ranjan, R. (2022). Explainable AI (XAI): Core Ideas, Techniques and Solutions. ACM Computing Surveys, 55(9), 1–33. https://doi.org/10.1145/3561048
[60] Gunning, D., & Aha, D. (2019). DARPA’s Explainable Artificial Intelligence (XAI) Program. AI Magazine, 40(2), 44–58. https://doi.org/10.1609/aimag.v40i2.2850
[61] Mia, L. (2025). Evaluating the Trade-offs Between Explainability and Security in AI-Powered Cyber Defense. https://doi.org/10.2139/ssrn.5140427
[62] Moustafa, N., Koroniotis, N., Keshk, M., Zomaya, A. Y., & Tari, Z. (2023). Explainable Intrusion Detection for Cyber Defences in the Internet of Things: Opportunities and Solutions. IEEE Communications Surveys & Tutorials, 25(3), 1775-1807. https://doi.org/10.1109/COMST.2023.3280465
[63] Niteen, N., & Kurian, S. M. (2023). Exploring Explainable AI, Security and Beyond: A Comprehensive Review. International Journal on Emerging Research Areas, 3(2). https://ijera.in/index.php/IJERA/article/view/5
[64] Srivastava, G., Jhaveri, R. H., Bhattacharya, S., Pandya, S., Rajeswari, Maddikunta, P. K. R., Yenduri, G., Hall, J. G., Alazab, M., & Gadekallu, T. R. (2022). XAI for Cybersecurity: State of the Art, Challenges, Open Issues and Future Directions. ArXiv:2206.03585 [Cs]. https://arxiv.org/abs/2206.03585
[65] Jimmy, F. (2021). Emerging Threats: The Latest Cybersecurity Risks and the Role of Artificial Intelligence in Enhancing Cybersecurity Defenses. Valley International Journal Digital Library, 9(2), 564–574. https://doi.org/10.18535/ijsrm/v9i2.ec01
