AI-Driven Generative Content & Predictive Analytics in Hybrid Academic Libraries: A Zero-Trust, Anti-Deviance Architecture

Abstract Academic libraries are undergoing a profound paradigm shift, evolving from passive hybrid repositories into autonomous, AI-augmented knowledge ecosystems. While traditional hybrid frameworks address basic print-digital coexistence, they suffer from critical vulnerabilities: high-demand resource friction, physical material mutilation, licensing piracy, unauthorized access, and algorithmic bias in digital discovery. This paper introduces a novel operational paradigm—the Zero-Trust Anti-Deviance Architecture (ZT-ADA)—that integrates Generative AI, Predictive Anomaly Detection, and Dynamic Resource Orchestration (DRO). Under ZT-ADA, predictive models anticipate resource surges during peak evaluation cycles, pre-allocating dynamic multi-user digital access and personalized generative summaries to mitigate physical collection stress. Simultaneously, a zero-trust cyber-physical verification matrix continuously evaluates access telemetry to detect physical deviance (mutilation, concealment, retention abuse) and digital breach threats in real time. We present mathematical formulations, operational workflows, risk matrices, and empirical evaluation metrics to establish a resilient, ethical, and scalable blueprint for 21st-century academic libraries.