The rapid integration of artificial intelligence (AI) into U.S. financial institutions has fundamentally transformed cybersecurity threat detection and response capabilities, while simultaneously introducing a new class of sophisticated risks. This study conducts a systematic literature review to analyze the impact of AI-driven cybersecurity tools on threat detection efficacy, incident response time, and overall cyber resilience within the U.S. financial sector. Drawing on peer-reviewed research, government reports, and institutional case studies published between 2019 and 2025, the study identifies key themes including the deployment of machine learning for anomaly detection, the emergence of adversarial AI threats, regulatory fragmentation, and the widening capability gap between large and small financial institutions. The findings reveal that while AI substantially improves detection accuracy and operational efficiency, its adoption introduces risks such as adversarial attacks, model drift, data poisoning, and algorithmic bias that existing frameworks are inadequate to address comprehensively. This paper proposes a scalable, unified AI-cybersecurity framework tailored for U.S. financial institutions, emphasizing governance, adaptive risk assessment, cross-institutional collaboration, and bias-aware AI design. The proposed framework is intended to serve as a foundation for regulators, financial institutions, and technology providers seeking to harness AI's capabilities while maintaining systemic stability and regulatory compliance.

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