Alam, Md Zahidul and Halder, Joshua Probal and Mohammad, Abdul Rahman and Khatun, Mst. Murshida (2024) Artificial Intelligence Governance in Business: Ensuring Data Privacy Compliance and Mitigating Cybersecurity Risks. American Journal of Technology Advancement, 1 (1). ISSN 2997-9382

Text AJTA_Artificial+Intelligence+Governance.pdf Download (768kB)

Abstract

The widespread integration of Artificial Intelligence (AI) into business enterprises has revolutionized decision-making, streamlined business operations and driven digital innovation, at the same time posing challenges to data privacy and cyber security. With the increasing adoption of AI-driven systems in handling sensitive data, it is crucial that enterprises have strong AI governance frameworks in place to ensure regulatory compliance, reduce security risks, and enhance organizational resilience. This study aims to explore how AI governance can help improve data privacy compliance and reduce cybersecurity threats using a data-driven business analytics approach. The dataset used for this research is the Data Breaches dataset, which contains organizational data breach incidents over 16 years (2004 through 2021) from a range of industries. Exploratory Data Analysis (EDA) is performed after Data Preprocessing and Feature Engineering to look for patterns in Breach Methods, Industry Sector and Severity of Breached Records. Most of the time, machine learning classification models such as Support Vector Machine (SVM), Random Forest, XGBoost, Decision Tree and Logistic Regression are used to predict the severity of the breach and to analyze the cybersecurity risk in the various organizational contexts. The accuracy, precision, recall, F1-score, receiver operating characteristic (ROC) curve and area under the curve (AUC) are used to assess model performance, as are the confusion matrix analysis. The empirical results support that there are notable relationships between the type of organization, breach methods and the level of data exposure, highlighting the power of AI-enabled predictive analytics in recognizing high-risk scenarios. This study recommends the following comprehensive AI governance framework to enhance the cybersecurity resilience of organizations: Intelligent risk assessment, continuous compliance monitoring, automated threat detection, privacy-by-design, and governance-driven decision support. The proposed framework offers practical assistance for business leaders, policymakers and information security professionals to help them adopt responsible AI governance strategies that safeguard sensitive business information and facilitate sustainable digital transformation. This study builds on the body of research on AI governance by connecting business analytics, compliance with data protection laws, and cybersecurity risk management in one analytical framework.

Item Type:	Article
Uncontrolled Keywords:	Artificial Intelligence Governance, Business Analytics, Data Privacy Compliance, Cybersecurity, Cybersecurity Risk Management, Information Governance and Regulatory Compliance
Subjects:	H Social Sciences
Depositing User:	admin eprints
Date Deposited:	07 Sep 2026 08:07
Last Modified:	07 Sep 2026 08:07
URI:	http://eprints.umsida.ac.id/id/eprint/17056

Actions (login required)

View Item