Design and Evaluation of Risk-Adaptive Authentication Systems under High-Traffic, Low-Latency Constraints

Risk-Adaptive Authentication (RAA) addresses the need to continuously analyse the environment, user behavior, and transaction context in order to determine an appropriate level of authentication, particularly in systems, such as IoT-based financial trading desks, that must respond to rapidly evolving contextual risks in under 100 ms. This review examines the design and deployment of RAA systems in high-traffic, low-latency environments, together with recent advances in contextual risk modelling, edge computing, and adaptive decision-making. It surveys context-aware frameworks, risk-aware access-control mechanisms, decentralized blockchain-based architectures, and machine-learning-based risk-assessment methods. The review also outlines architectural approaches that integrate artificial intelligence, edge intelligence, and federated learning into the authentication process to reduce latency while preserving security. Finally, it identifies promising directions for future research, including quantum-resistant cryptography, privacy-enhancing AI, and greater trust in autonomous systems, all of which may improve the resilience and adaptability of RAA systems. The literature reviewed indicates that modern authentication techniques increasingly aim to provide strong security guarantees in high-criticality digital environments, supported by substantial research investment and innovation. Overall, this review summarizes state-of-the-art authentication solutions that address a wide range of threats while meeting the demands of digital operations, namely low-latency performance, flexibility, and high security assurance.