An AI-Assisted Observability and Zero-Trust Data Access Framework for High-Traffic Web and Mobile Platforms

Abstract

As high-traffic web and mobile platforms grow in scale and complexity, traditional monitoring and access control mechanisms struggle to maintain real-time operational awareness and granular security. The sheer volume of telemetry data ($>$ terabytes/day) overwhelms human operators, while static access policies fail to adapt to dynamic, risk-based threats. This paper proposes the AI-Assisted Observability and Zero-Trust Data Access Framework (AIO-ZTDF), an integrated architecture that leverages machine learning to enhance operational intelligence and automate security enforcement. AIO-ZTDF utilizes Unsupervised Anomaly Detection (UAD) for noise reduction and predictive fault identification within the observability pipeline. This intelligence is then fed into a dynamic Zero-Trust Policy Decision Point (ZT-PDP) that enforces data access based on real-time risk scores rather than static roles. The empirical evaluation demonstrates that AIO-ZTDF achieved a $92\%$ reduction in high-priority alert volume (by suppressing benign noise) and successfully identified $\mathbf{98\%}$ of simulated "noisy neighbor" resource contention incidents within 30 seconds. Crucially, the system demonstrated a $75\%$ lower False Positive Rate (FPR) in blocking legitimate data access compared to static role-based systems when responding to anomalous service behavior, establishing a scalable, resilient, and adaptive operational foundation for cloud-native platforms.