The Integration of Trustworthy AI Values: A Comprehensive Model for Governance, Risk, and Compliance in Audit Architecture Framework context
Abstract
The Trustworthy AI-compliant Governance, Risk, Compliance Architecture (GRC Architecture) as outlined in this paper has the objective of analyzing the performance of a firm vis-àvis all the three facets. In other words, the system, analyzing the data from multiple sources monitor the GRC aspects of an organization in real time and report it to the stakeholders as to where the firm lacks performance/ risk/ compliance measures thereby enabling them to take corrective measures in time. The system is a supervised ML system which has sufficient human intervention and oversight. It is built on features like robustness and safety; the AI system has an in-built model control faculty that would take care of wrong actions by not letting them manifest, by classifying AI activity into multiple classes in which super-critical functions wherein a potential misstep can cause business disruptions; human approval is necessary there. Complying with all the laws and regulations governing the respective domains, the GRC Architecture is ISOcertified and adheres to all the privacy and data governance laws. It is transparent: all its actions are well accounted for. To ensure diversity, non-discrimination, and fairness, a special model is employed. It considers societal and environmental wellbeing in that paperless electronic digital system ensures a reduced carbon footprint contributing to infinite intangible benefits hidden prima facie, as the GRC
aspects of many a firm are taken care of. In this research paper, a Governance Risk and Control system architecture is shown to adhere to these parameters of Trustworthy AI
Keywords:
Artificial Intelligence, trust in autonomous systems, Business process systems, GRC architecture, Trustworthy AIPublished
Issue
Section
License
Copyright (c) 2023 International Journal on Emerging Research Areas

This work is licensed under a Creative Commons Attribution 4.0 International License.
All published work in this journal is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
How to Cite
Similar Articles
- Adithya Satheesh, Ashwin S Nair, Darren Padamittam Jacob, Athul Rajeev, Er. Maheshwary Sreenath, Intrusion Countermeasure System , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Melvin Tom Varghese, Joseph V S, Kevin Chacko, Johns Benny, Tintu Alphonsa Thomas, Crop Recommendation System using Machine Learning and IoT , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Dona S Plavelil, A Devanandha, Haritha H Kurupp, Jissin k Jose, DETECTION OF ALZHEIMER’S DISEASE AND ASSISTANCE , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Dr.Sinciya P.O, Aaron Varughese Bino, Anamin Fathima Anish, Aathira Krishna, Dona Maria Joseph, Unveiling Stress through Facial Expressions: A Literature Review on Detection Methods , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Sebin Thomas, John VG, Josin Chacko, Mariyam Shajahan, Sharon Sunny, PPT GENERATION FROM REPORT , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Aaron Samuel Mathew, Green Cloud Computing: A Literature Survey , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Lis Jose, Polarity Classification of Malayalam Document-A Rule Based Approach , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Akhil Mohan , E R Sreema, Leshma Mohandas , P U Prabath, Saeedh Mohammed , Virtual Air Canvas , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Honey Joseph, Aaron M Vinod, Abin Mathew varghese, Aby Alex, Aleena Sain, Crop Yield Prediction Using ML , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Linsa Mathew, Brain Tumor Detection , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
You may also start an advanced similarity search for this article.