We introduce WebFAQ 2.0, a new version of the WebFAQ dataset, containing 198 million FAQ-based natural question-answer pairs across 108 languages. Compared to the previous version, it significantly expands multilingual coverage and the number of bilingual aligned QA pairs to over 14.3M, making it the largest FAQ-based resource. Unlike the original release, WebFAQ 2.0 uses a novel data collection strategy that directly crawls and extracts relevant web content, resulting in a substantially more diverse and multilingual dataset with richer context through page titles and descriptions. In response to community feedback, we also release a hard negatives dataset for training dense retrievers, with 1.25M queries across 20 languages. These hard negatives were mined using a two-stage retrieval pipeline and include cross-encoder scores for 200 negatives per query. We further show how this resource enables two primary fine-tuning strategies for dense retrievers: Contrastive Learning with MultipleNegativesRanking loss, and Knowledge Distillation with MarginMSE loss.

Preserving the confidentiality, integrity and availability (CIA) of an organisation's sensitive information systems assets against attacks and threats is a challenge in this digital age. Organisations worldwide make huge investments in information security technological countermeasures. Nonetheless, organisations in many cases fail to protect their information assets as they rely mainly on technical solutions which are not contextually compatible and sufficient. As a matter of fact, a significant number of organisational information security incidents are due to the exploitation of human elements that directly and/or indirectly cause the majority of security incidents. Therefore, employees’ information security awareness (ISA) becomes one of the critical aspects of protection against undesirable information security behaviours. However, to date, there is limited synthesised knowledge about methods for enhancing ISA and integrated insights on factors affecting employees’ ISA levels. This study, therefore, provides a systematic review of the literature on ISA and puts forward a state-of-the-art collection of ISA methods and factors for enhancing employees’ ISA within both private and public sector organisations. The results indicate that various methods and factors are used to enhance employees’ ISA in organisations. Theoretical models and gamification are the methods widely used in both private and public organisations, whereas the constructivist approach and violation detections are some of the methods used only in private organisations. Furthermore, this study offers some insights into the latest trends in ISA content development methods and factors, and fosters good ISA practice by disseminating information and knowledge amongst Information Security professionals to help them build an overarching ISA development programme in their organisations.