University Academic Management Information Systems (ACMIS) are high-value targets for a wide spectrum of security threats including brute-force login attacks, payment fraud, privilege escalation, insider data theft, and academic integrity violations. Traditional rule-based intrusion detection systems are inadequate because many malicious activities are structurally indistinguishable from normal operations. This paper presents an AI-based security agent for ACMIS that combines supervised anomaly detection, behavioural analytics, and a natural language processing chatbot for secure password recovery. The agent monitors five operational layers: authentication, authorisation, financial transactions, user behaviour, and system health, and responds through a four-tier risk escalation framework. A modular architecture allows the core engine to be extended to other institutional systems. Experiments on a simulated ACMIS event log dataset demonstrate a threat detection macro-average F1 of 0.91, compared to 0.49 for a rule-based baseline, with critical-tier automated response latency under 300 ms at the 95th percentile.

翻译：高校教学管理信息系统（ACMIS）是包括暴力破解登录攻击、支付欺诈、权限提升、内部数据窃取及学术诚信违规等广泛安全威胁的高价值目标。传统基于规则的入侵检测系统存在不足，因为许多恶意活动在结构上与正常操作难以区分。本文提出一种面向ACMIS的AI安全代理，该代理融合了监督式异常检测、行为分析以及用于安全密码恢复的自然语言处理聊天机器人。该代理监控五个操作层级：认证、授权、财务交易、用户行为和系统健康，并通过四级风险升级框架进行响应。其模块化架构使核心引擎可扩展至其他机构系统。在模拟ACMIS事件日志数据集上的实验表明，与基于规则的基线系统（宏平均F1值为0.49）相比，本威胁检测方法的宏平均F1值达到0.91，且关键级自动响应延迟在第95百分位低于300毫秒。