Security threat mitigation

Abstract

The present disclosure provides methods, systems and computer readable media for training and implementing a generative machine learning model for identifying and mitigating security threats. Certain examples relate to generative model training, in which a training image is provided to a generative machine learning (ML) model in a training prompt, with an Indicator of Compromise (IoC) prediction instruction pertaining to the first security image. The model generates a predicted IoC and a parameter of the model is updated based on a loss function that quantifies error between a ground truth IoC and the predicted IoC. Other examples relate to the use of trained generative models for cybersecurity. A mitigation prompt comprising a second security image and an associated mitigation instruction is provided to a trained generative model. The model outputs an indication of a cybersecurity mitigation action based on the mitigation prompt, and the cybersecurity mitigation action is performed on the system. Certain example embodiments identify and automatically mitigate security issues using a multimodal generative model (MGM) though appropriate prompt engineering.