Enhancing Data Security in Artificial Intelligence Systems: A Cybersecurity and Information Governance Approach

Abstract

This study presents a comprehensive framework designed to enhance data security in artificial intelligence (AI) systems by integrating robust cybersecurity measures with information governance principles. Employing a convergent parallel mixed-methods design, the research combines a quantitative meta-analysis of 11,245 AI deployments, a qualitative synthesis of 89 governance studies, and technical validations across three open-source AI models (BERT, YOLOv7, and federated learning). Critical threats, including data poisoning (42% success rate), adversarial examples (23% higher vulnerability in vision models), and model inversion (31% more frequent in generative AI), were identified. The proposed framework demonstrated a Composite Security Score (CSS) of 0.87, outperforming existing models (CSS range: 0.78–0.82), and achieved 84–89% attack mitigation and 90–95% regulatory compliance under standards like GDPR and NIST RMF. The framework’s practical contribution lies in offering sector-specific, scalable solutions for strengthening AI system security, thereby enabling healthcare, finance, and public sector organizations to align innovation with ethical and legal obligations. Theoretically, the study advances knowledge by bridging cybersecurity engineering with governance structures, addressing a critical gap in AI risk management. Preliminary evaluations into quantum-ready encryption strategies were explored, though more extensive testing is suggested for future research. Key limitations include reliance on secondary datasets, potential model generalizability constraints, and emerging threats like quantum attacks that require ongoing adaptation. Recommendations advocate sector-specific adoption, quantum-ready encryption, and research into adaptive security and third-party risks to ensure scalable, ethical AI security.