Machine Learning based Cybersecurity Human Risk Management (CHRM) platform

安全意识培训（SAT）对减少网络犯罪的影响有限，因为它忽略了每个用户都是不同的这一事实。目前针对SAT的方法是提出明确的知识测试问题，并依赖用户的答案（通常是纯粹的猜测），而不是分析/衡量培训期间的实际行为。这意味着SAT供应商无法识别特定的人为风险因素，并允许有针对性的改进行动。OutThink是第一个网络安全人类风险管理（CHRM）平台。我们使用机器学习（ML）、自然语言处理（NLP）、应用心理学、软件遥感勘测和未来的安全系统集成（我们项目的关键部分）来识别个人的态度/行为-无论是在培训期间还是在日常安全行为中-并了解/衡量个人风险。有效的、有针对性的培训将使人类成为解决方案的一部分，而不是问题的一部分，每年为组织节省28亿欧元。在全球

Security Awareness Training (SAT) has limited impact in reducing cybercrime, because it neglects the fact that every user is different. The current approach to targeting SAT is to ask explicit knowledge test questions and rely on users’ answers (often pure guesses), rather than analysing/measuring actual behaviour during training. This means SAT providers cannot identify specific human risk factors and allow targeted improvement actions. OutThink is different: it is the first Cybersecurity Human Risk Management (CHRM) platform. We use Machine Learning (ML), Natural Language processing (NLP), applied psychology, software telemetry, and future security system integrations (a key part of our project) to identify individuals’ attitudes/behaviours – both during training and daily security behaviours – and understand/measure individual human risk. Effective, targeted training will make humans part of the solution, not the problem, saving organisations €2.8 bn p.a. globally.