AI Enabled Extraction of Secure Authentication and Data Access Flows from HAR Traces

Abstract

To safely access protected data modern online and mobile applications rely on sophisticated authentication and authorization techniques like OAuth, OpenID Connect, API keys, multi factor authentication and proprietary token exchanges. Platform integrators, security teams, QA engineers and financial data aggregators must comprehend these methods. Nevertheless, documentation is sometimes sparse, outdated or unavailable. By recording headers, payloads, cookies, redirection and time data, HTTP Archive (HAR) traces offer a comprehensive, low level record of network interactions between clients and servers. In this paper, an AI enabled method for automatically extracting data access procedures and secure authentication from HAR traces is presented. The suggested method reconstructs end-to-end authentication procedures, determines token lifecycles and maps data access patterns by fusing machine learning, sequence modeling and rule based security heuristics. With an emphasis on fintech and regulated contexts, the paper addresses system design, feature engineering, model selection, security issues and real world implementations.