Abstract: An approach is provided for detecting a malicious activity on a computer system. First process trees are identified for computer processes that have been executed on a computer system. Each of the first process trees are vectorized. The vectorized first process trees are associated with respective labels. Each label represents an amount by which a respective vectorized process tree reflects the malicious activity. An artificial neural network is trained by using the vectorized first process trees and the associated labels as training input. After the training of the artificial neural network is completed, second process trees for currently executing computer processes are vectorized and provided as input vectors to the artificial neural network. Responsive to the artificial neural network providing an output indicating that a combination of the input vectors indicates the malicious activity, a remedial action is performed.

Abstract: Identifying private information and preventing privacy violations is provided by a process that evaluates digital information obtained by an organization as part of a digital information stream from a user. The evaluating identifies a user utterance, including keyword(s), entity/ies, and intent(s), and applies natural language understanding to the digital information to ascertain a contextual understanding for the user utterance. The process selects training set(s) of historical information from available training sets that includes vocabulary used in varying contexts. The process compares the identified user utterance to an ontology based on the selected training set(s), and determines a confidence level that the digital information includes digital private information. The process also flags for the organization an action to take with respect to handling of the digital information. The flagging is based on the determined confidence level that the digital information includes digital private information.