Zero Trust is Not Enough: Mitigating Data Repository Breaches


  • JS Hurley Hurley ODNI



Zero Trust, data repository, frameworks, machine learning


Successful mission operations depend on the ability of an organization to collect, manage, analyze, and secure its data. Traditional network frameworks have become less appealing because they rely on a “trust but verify” paradigm that does not stand up well against the advanced tools and techniques of modern cyber attackers. The Zero Trust Framework has emerged as a logical replacement but unfortunately does not adequately address the trustworthiness of data in data repositories. This broader view is important because data repositories have become arguably, the most prominent means for data sharing across many sectors around the globe. Unfortunately, data repositories are also undergoing widespread malware attacks and, in some cases, data critical to national security can be impacted. In this study, we propose a potential framework that relies more on data lineage, end-to-end metadata, and the use of machine learning tools and techniques to reduce and possibly mitigate the problems with data repositories that Zero Trust Frameworks fail to address.