AURA-X: AI-Powered Framework for Secure Cloud Access

Cloud services have gradually become part of routine work for individuals and organizations, yet their growing use has also opened several unnoticed weaknesses in access control and account-level security. Existing rule-based systems work reasonably well for predictable situations but often fall short when users behave differently or when an attacker takes advantage of valid credentials. In this work, We propose AURA-X, a conceptual model that observes routine access behavior of cloud users and identifies situations where the pattern appears unusual or risky. The framework brings together ideas from anomaly detection, behavior analysis, ensemble learning, and explainability so that alerts are not only timely but also understandable to administrators. Federated learning is considered in the design to ensure that sensitive information does not need to be centrally collected. The intention of the model is to offer a practical and transparent way to strengthen cloud login and access monitoring without depending on a single vendor or platform.