Cybersecurity Measures in Financial Institutions Protecting Sensitive Data from Emerging Threats and Vulnerabilities

¹ Assistant Professor, Department of Computer Science and Engineering (AI&ML), Neil Gogte Institute of Technology, Peerzadiguda Road Uppal, Kachawanisingaram Village, Hyderabad, Telangana, India
² Assistant Professor, Department of Computer Science Engineering, Tontadarya College of Engineering, Gadag-Betigeri, Karnataka, India
³ AP/MBA, J.J.College of Engineering and Technology, Tiruchirappalli, Tamil Nadu, India
⁴ Assistant Professor, Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India
⁵ Professor, Department of Computer Science and Engineering, K.S.R. College of Engineering, Tiruchengode, Namakkal, Tamil Nadu, India
⁶ Professor, Department of EEE, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India

kirankumarb.18@gmail.com
kulkarniak644@gmail.com
amalasuzana@jjcet.ac.in
diwakaranm@skcet.ac.in
drsivakumar.p@gmail.com
senthilkumar@newprinceshribhavani.com

Abstract

As financial institutions increasingly digitized, they are up against a growing suite of cybersecurity threats such as ransomware, cryptojacking, AI-enabled phishing, and quantum computing attacks. Other research work and government reports reinforce general cybersecurity concerns but are not technical or applied. They do not cater specifically to financial institutions. To fill these gaps, this study introduces an AI-based cybersecurity framework focusing on effective protection of financial data, compliance with regulations, and minimizing time for detecting threats.Building upon the existing research and drawing on best practices from both the financial and technology sectors, this paper presents a promising new framework that combines machine learning-driven methods for real-time fraud detection, the use of blockchain technology to ensure transaction integrity, and quantum-resistant and decentralized encryption methods to protect sensitive financial information from cyber threats. Whereas other research focuses on broad, high-level strategies, this research offers a step-by-step technical roadmap to zero-trust security, anomaly detection and automated cybersecurity responses. It also analyzes actual cyberattacks on financial institutions and develops predictive models to proactively reduce risks.To tackle on email-based financial scams, the research presents a deep learning-embedded BERT paradigm integrated with NLP to enhance phishing identification. It also introduces a biometric security mechanism that ensures that sensitive user data is unalterable, and accessible only to authorized parties. Cybersecurity measures for integrated financial IoT are proposed contribution to the NIST Cybersecurity Framework, including the prevention of cyber threats for automatic teller machines, mobile banking, and electronic payment systems. Challenges of compliance with GDPR, PCI-DSS and ISO 27001 are also addressed.Based on empirically-testing real-time financial datasets, the framework shows improved robustness to cyberattacks. These findings lay the groundwork for the future of cybersecurity, helping financial institutions stay secure and adaptive in the face of evolving cyber threats.