Abstract: Aspects of the disclosure relate to automated simulated phishing lure generation for cybersecurity training. The computing platform may receive personalization data. The computing platform may generate, using a phishing lure generation model, one or more simulated synthetic phishing lures based on the personalization data. The computing platform may send the one or more simulated synthetic phishing lures to one or more user devices and one or more commands directing the one or more user devices to display the one or more simulated synthetic phishing lures, which may cause the one or more user devices to display the one or more simulated synthetic phishing lures. The computing platform may receive, from the one or more user devices, feedback data corresponding to user interactions with the simulated one or more synthetic phishing lures. The computing platform may update, using the feedback data, the phishing lure generation model.

Abstract: Aspects of the disclosure relate to dynamic message analysis using machine learning. A computing platform may apply a security scoring process to an endpoint relationship to compute a weighted security score for the endpoint relationship. Subsequently, the computing platform may determine a weighted grade for the endpoint relationship based on the weighted security score for the endpoint relationship. Based on identifying that the weighted grade exceeds a predetermined threshold, the computing platform may tag the endpoint relationship as compromised. Subsequently, the computing platform may monitor an electronic messaging server to detect messages corresponding to the compromised endpoint relationship.