Responsible AI for Cybersecurity: Assessing the Barriers, Biases and Governance Gaps in Implementation with E-commerce Systems

Building on the findings of Obioha-Val (2024), which reveal both the transformative potential of AI and the risks associated with unregulated or opaque implementation, this study investigates the responsible deployment of AI-driven cybersecurity systems in e-commerce by examining structural, ethical, and governance challenges. Using four open-source datasets, including the IEEE-CIS Fraud Detection Dataset, OECD AI Readiness indicators, the Stanford AI Governance Index, and the Global AI Ethics Guidelines Dataset, the study applies Principal Component Analysis, Logistic Regression, Weighted Scoring Models, and K-means clustering to evaluate adoption barriers and framework adaptability. Results show that only 40% of e-commerce firms are AI integration-ready, with SMEs particularly hindered by outdated infrastructure and limited workforce capacity. Algorithmic fairness testing revealed zero transaction flags under the applied threshold, raising concerns of underfitting and potential hidden biases. Ethical risks such as privacy violations, consent ambiguity, and algorithmic discrimination, particularly in pricing and service delivery, are highlighted as critical threats. Governance analysis ranked the UK highest (8.00/10), while 95% of firms lacked formal AI oversight structures. Cluster analysis indicated that only 30% of international AI frameworks sufficiently incorporate operational principles like security and human oversight. This study adapts the Obioha-Val framework originally applied in U.S. public school systems to the commercial e-commerce context, offering a recalibrated, sector-specific model of responsible AI governance. Recommendations include developing AI-specific cybersecurity protocols, integrating fairness auditing tools, harmonizing global standards, and investing in infrastructure and AI literacy for SMEs.