Systems and methods for intelligent identification and automated disposal of non-malicious electronic communications

A computer-implemented method for accelerating a disposition of non-malicious electronic communications, the computer-implemented method comprising:

• generating a phishing alert based on receiving a request from a subscriber to assess a threat of a third-party electronic communication, wherein the phishing alert includes a representation of the third-party electronic communication;
• extracting one or more corpora of feature vectors from the third-party electronic communication based on providing the third-party electronic communication as input to one or more feature extractors, wherein extracting the one or more corpora of feature vectors includes:
• (i) extracting a first corpus of feature vectors likely indicative of whether the third-party electronic communication is of a target non-malicious electronic communication type, wherein:
• the target non-malicious electronic communication type refers to a class of electronic communications that promotes one or more products, one or more services, or one or more events, and
• one feature of the first corpus of feature vectors indicates whether the third-party electronic communication includes an unsubscribe feature, and
• (ii) extracting a second corpus of feature vectors likely indicative of whether the third-party electronic communication is a suspected malicious electronic communication;
• computing, by a machine learning-based electronic communication classification model, an electronic communication-type classification inference that includes a probability of the third-party electronic communication being of the target non-malicious electronic communication type in response to the machine learning-based electronic communication classification model receiving the first corpus of feature vectors and the second corpus of feature vectors, wherein the machine learning-based electronic communication classification model uses at least the one feature of the first corpus of feature vectors to assist with computing the probability of the third-party electronic communication being of the target non-malicious electronic communication type;
• and
• automatically routing the phishing alert to a security alert queue based on the probability of the electronic communication-type classification inference being below a predetermined minimum electronic communication classification threshold.

A computer-implemented method for accelerating a disposition of non-malicious electronic communications, the computer-implemented method comprising:

• at a cybersecurity event detection and response service:
• receiving, from a subscriber, a request to assess a threat of a third-party electronic communication;
• extracting one or more corpora of feature vectors from the third-party electronic communication based on providing the third-party electronic communication as input to one or more feature extractors, wherein extracting the one or more corpora of feature vectors includes:
• (i) extracting a first corpus of feature vectors likely indicative of whether the third-party electronic communication is of a target non-malicious electronic communication type, wherein:
• the target non-malicious electronic communication type refers to a class of electronic communications that promotes one or more products, one or more services, or one or more events, and
• one feature of the first corpus of feature vectors indicates whether the third-party electronic communication includes an unsubscribe feature, and
• (ii) extracting a second corpus of feature vectors likely indicative of whether the third-party electronic communication is a suspected malicious electronic communication;
• computing, by a machine learning-based electronic communication classification model, an electronic communication-type classification inference that includes a probability of the third-party electronic communication being of the target non-malicious electronic communication type in response to the machine learning-based electronic communication classification model receiving the first corpus of feature vectors and the second corpus of feature vectors, wherein the machine learning-based electronic communication classification model uses at least the one feature of the first corpus of feature vectors to assist with computing the probability of the third-party electronic communication being of the target non-malicious electronic communication type;
• automatically attributing a classification label of the target non-malicious electronic communication type to the third-party electronic communication based on the probability of the electronic communication-type classification inference satisfying a minimum electronic communication classification threshold;
• and
• automatically routing a security alert that corresponds to the third-party electronic communication to an alert disposal queue based on the third-party electronic communication having the classification label of the target non-malicious electronic communication type.