The rapid transformation of the banking industry through digital technologies has significantly improved financial accessibility, operational efficiency, and customer engagement. However, the growing dependence on cloud computing, artificial intelligence (AI), FinTech services, and digital payment infrastructures has simultaneously increased exposure to sophisticated cyber threats. Traditional security mechanisms, which primarily rely on predefined rules and periodic monitoring, are often inadequate for detecting advanced persistent threats, insider attacks, and zero-day vulnerabilities. Consequently, modern digital banking platforms require intelligent safeguarding frameworks capable of continuous threat surveillance, adaptive learning, and real-time response.

This research proposes an Automated Safeguarding Framework for Ongoing Threat Surveillance in Digital Banking Platforms, integrating artificial intelligence, behavioral analytics, risk assessment, and automated incident response into a unified security architecture. The proposed framework emphasizes continuous monitoring of user activities, transaction behaviors, authentication patterns, and network communications to identify malicious activities before they compromise financial assets or customer information. The architecture incorporates data acquisition, intelligent feature engineering, anomaly detection, threat prioritization, automated mitigation, and continuous feedback learning, enabling proactive rather than reactive cybersecurity management.

The study synthesizes recent developments in AI-enabled banking, digital transformation, FinTech adoption, customer experience, workforce agility, and enterprise security. Special attention is given to the AI-driven security model proposed by Kubam et al. (2025), which demonstrates the effectiveness of optimal feature analysis for continuous enterprise cloud finance threat detection. The present framework extends these concepts toward comprehensive digital banking environments by integrating adaptive intelligence with operational risk management (Kubam et al., 2025).

Hypothetical performance evaluation indicates that continuous surveillance significantly reduces intrusion detection latency, improves fraud identification accuracy, minimizes false alarms, and enhances overall cyber resilience. Automated response mechanisms further reduce operational workload while ensuring regulatory compliance and uninterrupted banking services.

The research contributes a scalable and intelligent cybersecurity framework suitable for commercial banks, digital-only banks, FinTech institutions, and cloud-based financial ecosystems. The findings suggest that integrating AI-driven monitoring with adaptive decision-making can substantially improve digital banking security while maintaining customer trust and operational continuity.

Digital Banking, Artificial Intelligence, Cybersecurity, Threat Surveillance

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