Student AI Phishing Detection System

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About This Architecture

Privacy-first phishing detection system processes screenshots, emails, SMS, PDFs, and social media content entirely on-device without transmitting user data. Input flows through OCR Engine and Direct Text Parser into unified Extracted Text Module, then to AI Phishing Detection using NLP and CNN analysis. Risk Scoring Engine assigns 0-100 scores with color-coded thresholds (Safe 0-30, Suspicious 31-69, Phishing 70-100) while Explainability Module surfaces suspicious phrases and reasoning. Fork this diagram on Diagrams.so to customize detection thresholds, add new input sources, or integrate with campus security systems.

People also ask

How do you build a phishing detection system that processes student data locally without sending information to external servers?

This architecture uses on-device OCR and NLP analysis to detect phishing in screenshots, emails, and messages. All processing occurs within a local boundary, with AI Phishing Detection Module scoring threats 0-100 and Explainability Module surfacing suspicious phrases without transmitting user data externally.

Student AI Phishing Detection System

Autointermediatesecurityphishing-detectionprivacyexplainable-aistudent-securityon-device-processing
Domain: SecurityAudience: cybersecurity educators and student IT administrators implementing phishing awareness programs
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Created by

February 23, 2026

Updated

February 25, 2026 at 10:05 AM

Type

architecture

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