Cybersecurity Risks in Identity and Access Management Using an Adaptive trust Authenticate Protocol

Identity and Access Management (IAM) systems are critical for safeguarding organizational infrastructure by ensuring that only authorized users access sensitive information and resources. However, traditional IAM protocols often struggle to detect advanced threats such as identity spoofing, privilege escalation, and unauthorized access through stolen credentials. This paper proposes an adaptive trust authentication protocol that addresses these challenges by integrating deep learning-based anomaly detection, user behavior analytics (UBA), and multi-factor authentication (MFA) into the access control process. The protocol utilizes behavioral biometrics and dynamic access control to continuously monitor user actions in real-time, detecting deviations from typical usage patterns indicative of potential threats. A user trust score is dynamically generated based on real-time behavior analysis and MFA results, while behavior patterns are further evaluated using a deep control convolutional network. By combining the trust score with behavioral analytics, the system initiates secure and context-aware authentication of sensitive financial data. Extensive testing of the proposed protocol demonstrates its effectiveness in mitigating internal and external cybersecurity risks, significantly improving detection accuracy and reducing false positives. The novelty of this approach lies in its seamless integration of advanced behavioral analytics, deep learning, and adaptive authentication strategies, offering a robust, scalable, and resilient solution for modern IAM systems.