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Traditional rule-based intrusion detection systems are inadequate because many malicious activities are structurally indistinguishable from normal operations.\n  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.\n  A modular architecture allows the core engine to be extended to other institutional systems. 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The agent monitors five operational layers: authentication, authorisation, financial transactions, user behaviour, and system health, and responds through a four-tier risk escalation framework.\n  A modular architecture allows the core engine to be extended to other institutional systems. 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