Abstract: The proliferation of remote work across U.S. financial institutions has created an expanded cybersecurity threat surface, exposing sensitive financial data to sophisticated attack vectors. This study examines how AI-driven technologies enhance data security protocols within remote financial work environments, drawing on the lived experiences of cybersecurity specialists and IT managers. Using a qualitative phenomenological design, semi-structured interviews were conducted with 12 senior cybersecurity and IT professionals across U.S. financial institutions. Data were analyzed using Braun and Clarke's six-phase thematic analysis framework in NVivo. Five interconnected themes emerged: AI as a catalyst for data security; cybersecurity leaders' perceptions of AI; drivers and barriers to AI adoption; AI-enabled Zero Trust identity and access controls; and compliance and ethics in AI security. Findings demonstrate that AI-powered tools-including machine learning-based anomaly detection, behavioral analytics, and automated incident response-substantially strengthen threat detection and compliance monitoring in remote environments. Participants consistently characterized AI as an augmenter of human expertise rather than a replacement, expressing concern over algorithmic opacity, integration costs, and regulatory complexity. The study provides empirical evidence on the theory-practice intersection of AI adoption in financial cybersecurity, offering practical recommendations for governance, explainability, and workforce capability development.

Keywords: Artificial Intelligence; Cybersecurity; Remote Work; Financial Institutions; Zero Trust Architecture; Phenomenological Research; Machine Learning

Downloads: | DOI: 10.17148/IARJSET.2026.13301