An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response

The paper introduces an AI-based security agent designed for University Academic Management Information Systems (ACMIS), which integrates supervised anomaly detection, behavioral analytics, and a natural language processing chatbot for secure password recovery. The agent monitors five operational layers and employs a four-tier risk escalation framework, achieving a macro-average F1 score of 0.966 on a simulated dataset, significantly outperforming traditional rule-based systems and LSTM baselines. This modular architecture enables adaptability to other institutional systems, offering a promising solution for enhancing security against diverse threats in academic environments.