AI-Enhanced Threat Detection and Response in Financial Cybersecurity: Current Practice and Emerging Trends

Abstract

Growing sophistication of cyberattacks calls for defense mechanisms beyond conventional approaches. Signature-based security mechanisms are ineffective against new threats such as zero-day exploits and advanced persistent threats. Artificial Intelligence (AI), through machine learning, provides the ability to sift through vast datasets, identify subtle patterns that are suggestive of an attack, and forecast vulnerabilities before exploitation, going beyond reactive defenses. Initial AI deployments, however, were plagued by adaptability and false positive problems. Despite its potential, integrating AI into cybersecurity frameworks is beset by significant challenges in terms of data paucity, model robustness against adversarial attacks, explainability, and ethical aspects such as bias and privacy. Here, we consolidate current research to present a holistic picture of AI's incorporation in cybersecurity, examining its applications, methodologies, ethical considerations, and prospects. Our review indicates that AI significantly strengthens threat detection (including zero-days), automates data analysis, and facilitates predictive security actions, going beyond classical limitations. It emphasizes the importance of explainable AI (XAI) for analyst trust as well as the changing role of human experts interacting with AI systems, while also pointing to lingering issues with data quality and adversarial robustness. An understanding of these dynamics is indispensable for crafting efficacious, robust, and ethical AI-powered cybersecurity approaches, enlightening practitioners and policymakers working in the challenging environment of cyber defense.