CYBERALERT

CyberAlert is a prototype for detecting spam and phishing messages. It is built for users who receive suspicious links, offers, login warnings, or payment messages and need a quick technical explanation without reading a cybersecurity textbook first.

Most people can sense that a message feels wrong, but they may not know which signal matters: urgency, spoofed sender, credential request, bad link, or emotional pressure. A yes/no detector is too shallow because it does not teach the user what to look for next time.

The backend is built with Python and Flask. A submitted message moves through request validation, prompt construction, AI analysis, severity scoring, and a cleaned response payload. The prompt asks the model to classify the threat type, explain the indicators, and return a practical risk level.

The system is intentionally lightweight because the important part is the classification flow, not a giant backend. If the prompt is loose, the output gets vague. So the prompt structure carries a lot of the reliability work here.

CyberAlert is focused on explainable risk detection.

The biggest issue is consistency. AI can over-explain low-risk text or under-explain subtle phishing attempts if the prompt does not define a clear output shape. Security tools also need careful wording because a confident false safe result is worse than an honest uncertain one.

I chose Flask because the prototype needed a direct request-response backend with low overhead. The AI prompt is structured around threat category, severity, evidence, and recommendation so the result stays readable. The system favors cautious explanations over dramatic claims.

The prototype can analyze suspicious messages and produce a clear risk explanation. It is a practical foundation for a bigger security workflow with URL checks, sender validation, and stored reports added later.