Examining the Influence of AI-Driven Cybersecurity in Financial Sector Management

Abstract

As financial institutions increasingly rely on AI for cybersecurity, they face complex regulatory landscapes requiring robust security measures to ensure transparency, accountability, and fairness. This research aims to develop a comprehensive AI-based cybersecurity model for the financial sector, enhancing the capacity to recognize, stop, and react to cyberattacks while ensuring data integrity and customer trust. The proposed CS-FSM model utilizes AI techniques, including KNN for predicting and identifying unauthorized access and EES for encrypting and decrypting financial data. The model's performance was evaluated using attack avoidance, risk reduction, scalability, and data privacy parameters. Experimental data were collected and analyzed on a system with a 2.84 GHz Intel Core i7 processor, utilizing Python 3.8.10 and Matlab 2018a for data processing and visualization. The CS-FSM model demonstrated significant improvements in key cybersecurity metrics compared to traditional methods. There was a rise of 18.3% in data privacy, 17.2% in scalability, 13.2% in risk reduction, 16.2% in data protection, and 11.2% in attack avoidance. These results indicate that the proposed model effectively enhances cybersecurity measures in the financial sector. The study confirms that integrating AI algorithms such as KNN and EES into financial sector cybersecurity frameworks can provide robust protection against cyber threats. In addition, the CS-FSM model ensures the secure handling of sensitive financial data, thereby maintaining customer trust and compliance with regulatory standards.