Integrating Artificial Intelligence and Machine Learning in Cybersecurity for Financial Institutions

Financial institutions are increasingly adopting Artificial Intelligence (AI) and Machine Learning (ML) to bolster cybersecurity defenses amid growing threats in a rapidly digitized financial landscape. This integration leverages the predictive and adaptive capabilities of AI/ML to enhance threat detection, automate incident response, and mitigate risks in real-time. Traditional security measures often struggle to keep pace with evolving cyber threats, such as ransomware, phishing, and insider attacks. AI/ML models, trained on vast datasets, can identify anomalous behaviors, detect zero-day vulnerabilities, and proactively counter sophisticated attacks.In addition to strengthening operational security, these technologies enable financial institutions to comply with regulatory standards and reduce operational costs through automation. However, the integration also poses challenges, including data privacy concerns, adversarial attacks on ML systems, and the need for skilled personnel to manage and interpret AI/ML tools effectively.This paper explores the current state of AI/ML in cybersecurity for financial institutions, highlights real-world applications, and discusses future opportunities and challenges. By adopting a robust framework that combines AI/ML with traditional cybersecurity practices, financial institutions can achieve resilient, adaptive, and scalable security postures to safeguard sensitive data and maintain trust in a volatile threat environment.