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 of 147,922 sessions demonstrate a threat detection macro-average F1 of 0.966, compared to 0.156 for a rule-based baseline and 0.836 for a sequence-only (LSTM) baseline, with end-to-end critical-tier automated response latency under 1 ms on a single-node prototype. The integrated recovery chatbot achieves 97.1 percent identity verification accuracy and an 87.3 percent mass-reset attack detection rate with zero false positives on legitimate high volume recovery periods.

翻译：高校学术管理信息系统（ACMIS）面临广泛安全威胁的高价值目标，包括暴力登录攻击、支付欺诈、权限提升、内部数据窃取及学术诚信违规行为。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。本文提出一种基于AI的ACMIS安全代理，结合监督异常检测、行为分析及用于安全密码恢复的自然语言处理聊天机器人。该代理监测五个操作层面：身份认证、授权管理、金融交易、用户行为及系统健康状态，并通过四级风险升级框架进行响应。模块化架构使核心引擎可扩展至其他机构系统。在包含147,922个会话的模拟ACMIS事件日志数据集上进行的实验表明，该代理的威胁检测宏平均F1值为0.966，而基于规则基线的值为0.156、纯序列（LSTM）基线的值为0.836，且端到端关键级自动响应延迟在单节点原型上低于1毫秒。集成的恢复聊天机器人身份验证准确率达到97.1%，大规模重设攻击检测率达87.3%，且在合法高容量恢复期间零误报。