Publication Peer Review

Shadow Access, a growing concern in cloud computing, refers to unintended, unauthorized access to systems and data, often intensified by the complexities of modern technological environments and inadequate management of access privileges.

Zero Trust, centered on the philosophy of "Never Trust, Always Verify," is positioned as a countermeasure to Shadow Access, advocating for robust authentication and vigilant access control. However, the practical implementation of Zero Trust faces challenges in cloud-native architectures where Shadow Access is prevalent. 

The integration of AI, especially Generative AI, LLM (Large Language Model), and RAG (Retrieval Augmented Generation), introduces additional dimensions of Shadow Access risk, such as unauthorized access, sensitive data exposure, and governance issues in AI applications. The document underscores the necessity of addressing these AI-induced Shadow Access vulnerabilities to strengthen cybersecurity in an evolving landscape where traditional Zero Trust approaches must adapt to the nuances of Generative AI technology.

This document focuses on the intersection of Shadow Access with two of the most top-of-mind technologies today - Zero Trust and Artificial Intelligence.