Quantitative Exploration of AI's Impact on Financial Cybersecurity: Trends, Data Privacy, and Human Expertise
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Authors
Stewart, Tanya
Issue Date
2026-03
Type
Dissertation
Language
en
Keywords
Business, Engineering, Science, & Technological Innovation , Financial , Cybersecurity , Artificial Intelligence
Alternative Title
Abstract
The integration of artificial intelligence (AI) into financial cybersecurity has introduced both significant opportunities and complex challenges, particularly in relation to regulatory compliance, data privacy, and human–AI collaboration. This quantitative, non-experimental correlational study examined the extent to which key innovation attributes, compatibility, complexity, and relative advantage, impact the adoption and effectiveness of AI-driven cybersecurity technologies within U.S. financial institutions. Grounded in the Technology Acceptance Model (TAM) and Diffusion of Innovations (DOI) theory, the study explored relationships between these independent variables and institutional outcomes, including system performance, adaptability, human–AI collaboration, and regulatory compliance. Data were collected from 90 cybersecurity professionals, IT managers, and compliance officers using a structured Likert-scale survey administered via Qualtrics. Statistical analysis was conducted using exploratory factor analysis (EFA) and multivariate analysis of variance (MANOVA), with Pillai’s Trace employed due to assumption violations. Results indicated that relative advantage had a statistically significant impact on institutional outcomes, while compatibility and complexity did not demonstrate significant independent effects. However, the combined model of all three variables produced a significant multivariate effect, highlighting the importance of integrated adoption strategies. The findings contribute to existing literature by emphasizing the critical role of perceived value and holistic implementation approaches in AI adoption within highly regulated financial environments. Practical implications include the need for financial institutions to prioritize strategic alignment, workforce integration, and governance frameworks when deploying AI-driven cybersecurity solutions.