A Comprehensive Framework for Cybersecurity in Identity and Access Management Systems

Abstract

In today’s increasingly complex digital landscape, financial organizations in both the public and private sectors are recognizing the essential role of Identity and Access Management (IAM) technology to fulfil mission-critical objectives. IAM systems are fundamental for securing access to sensitive resources across diverse, heterogeneous technology environments while adhering to stringent regulatory compliance requirements. A well designed identity management system is not only crucial for protecting user privacy and sensitive data but also enables seamless information sharing between different public and private sector entities, thus enhancing the efficiency and security of today’s public service delivery. This study introduces an advanced security framework incorporating a hash-based data security algorithm that provides a robust mechanism for securely storing financial data. The proposed framework integrates this algorithm with a cyber water swarm optimization-based IAM technique, with deep hill prophet learning strategy which dynamically manages user identities and access rights by calculating a unique trust score for each user. The trust score is determined by evaluating various behavioural and contextual factors, enabling the system to adaptively control access based on user reliability and risk level. Users with higher trust scores are granted access to sensitive financial information, which they can retrieve using a secret encryption key provided by the system, ensuring that data remains secure even in the event of unauthorized access attempts. To validate the proposed system, a real time dataset was utilized to simulate authentic financial transaction scenarios, serving as input for the suggested IAM mechanism. This dataset enabled testing under realistic conditions, highlighting the system's ability to handle live data flows while maintaining secure and efficient access management. The entire experimentation process was conducted within the MATLAB environment, which provided the computational resources needed for modelling, simulation, and analysis of the IAM framework's effectiveness. The findings from this study demonstrate that the integration of advanced hashing techniques with cyber water swarm optimization for IAM offers a scalable and secure approach for modern financial organizations, reinforcing the value of AI-driven identity management in protecting against evolving cyber threats.