NO-CODE AI SECURITY: AUTOMATED PHISHING RESPONSE

Phishing attacks are a major problem in cybersecurity, as they exploit people's trust through scam emails and other messages. Many of the traditional tools used to detect these attacks are not very flexible and often require a lot of technical knowledge, making them difficult for everyday users. This project tackles that issue by developing an automated phishing response system using no-code AI, which makes advanced security tools accessible to everyone. The project objectives were to design a single system to detect and manage phishing threats, to build it using Make.com to automate a workflow that monitors a Gmail inbox, extracts links, scans them for threats with VirusTotal, uses OpenAI's GPT-4o-mini for risk analysis, and sends alerts via Slack and finally to evaluate how well it worked, focusing on its accuracy, speed, and user-friendliness. The methodology involved a literature review covering phishing trends, the use of AI in cybersecurity, and no-code platforms. After the research phase, I designed the system in separate parts, built it piece by piece, and then tested the final product using sample emails that contained both malicious and safe links. The results were positive. The system could process an email in about 10 seconds and correctly identify every URL. Harmful links were automatically quarantined, and an alert was sent, while safe links were routed to the main inbox. The system ran without any errors, showing it is reliable and simple enough for non-experts to use. This work contributes to a more inclusive approach to cybersecurity and automated incident response. By blending no-code automation with AI, it strengthens our defense against phishing while meeting ethical and regulatory requirements. Future improvements could involve adding support for multiple channels and introducing adaptive learning features