Abstract: Aspects of the present disclosure provide systems, methods, apparatus, and computer-readable storage media that support relevance-based analysis and filtering of documents and media for one or more enterprises. Aspects disclosed herein leverage custom-built taxonomies, natural language processing (NLP), and machine learning (ML) for identifying and extracting features from highly-relevant documents. The extracted features are vectorized and then filtered based on entities (e.g., enterprises, organizations, individuals, etc.) and compliance-based risks (e.g., illegal or non-compliant activities) that are highly relevant to a particular client. The filtered feature vectors are used to identify and highlight relevant information in the corresponding documents, enabling decision making to resolve compliance-related risks. The aspects described herein generate fewer false positive or otherwise less relevant results than conventional document screening applications or manual techniques.

Abstract: Computer implemented methods are provided for facilitating social engineering campaigns. The methods generally include maintaining at least one database that includes metadata for a plurality of fictional personas, metadata for a plurality of potential targets, information for a plurality of social engineering campaigns, such as a plurality of campaign scenarios, and information regarding social engineering successes with respect to at least one fictional persona, at least one potential target, at least one social engineering campaign, or a combination thereof. The method further includes receiving a selection of a campaign scenario from the plurality of campaign scenarios, receiving a selection of at least one of the plurality of potential targets, and selecting at least one of the plurality of fictional personas for use in a social engineering campaign based on an expected effectiveness of the given fictional personas with respect to the selected targets.