Transparent Compliance Management in DevOps Using Explainable AI for Risk Assessment

Authors

  • Sandeep Belidhe   Independent Researcher, USA

Keywords:

DevOps, Compliance Management, Explainable AI, Risk Assessment, Transparency, Automation, Regulatory Adherence, Continuous Integration, Security, AI Interpretability.

Abstract

In this fast-evolving field of DevOps, the issue of compliance with the set and required regulatory and security standards is a vital but rather challenging endeavour. Typically, traditional methods of managing compliance prevent the organization and its employees from obtaining important information about risks, which hinders risk assessment mechanisms. To carry out this research, the main topic of this paper is: How to enable transparency in DevOps compliance management using Explainable Artificial Intelligence (XAI). Using XAI allows organizations to improve the ability of their automated risk assessment system and compliance processes to be both explainable and auditable. XAI models make it easy for DevOps teams to understand how decisions are made, address risks before they occur, and meet regulatory requirements. Also, this approach enhances stakeholder cooperation since different technical and non-technical teams can understand compliance information. Concerning risk management, DevOps automates the deployment of new AI models. At the same time, the information explained to users by XAI enhances existing procedures, fosters trust in AI systems, and develops a robust containment and ongoing compliance scheme for AI-enhanced services as technology and regulation evolve.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2022-03-17

Issue

Volume 8, Issue 2, March-April-2022

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

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

How to Cite

[1]
Sandeep Belidhe , " Transparent Compliance Management in DevOps Using Explainable AI for Risk Assessment" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 2, pp.547-552, March-April-2022.