AI-Based Email Security System

Email is a central channel for personal and organizational communication, but it is also exploited to distribute spam, phishing links,fraudulent requests, and malicious attachments. This paper proposes an AI-based email security system that analyzes message text,sender-related indicators, URLs, and attachment metadata to estimate whether an email is legitimate, spam, phishing, or potentiallymalicious. The proposed workflow combines preprocessing, feature extraction, machine-learning classification, risk scoring, andexplanations for flagged messages. A layered design is recommended because no single indicator is reliable in every case: languagepatterns, sender authentication signals, suspicious URL characteristics, and attachment properties should be considered together. Thesystem should issue a warning or quarantine recommendation rather than automatically treating every flagged message as malicious.Evaluation can measure precision, recall, F1-score, false-positive rate, detection latency, and robustness to changing attack patterns.As no implemented prototype or measured dataset results were provided, this paper presents a proposed design and evaluation planrather than claiming numerical performance