Publication Peer Review

The document titled "Shadow Access and AI" explores the intricate relationship between Shadow Access and AI, highlighting the risks and solutions associated with this growing challenge. Shadow Access refers to unauthorized or ungoverned access to enterprise systems, often resulting from poor identity posture, over-permissioned environments, and the rise of unsanctioned SaaS applications. As AI technologies, particularly Generative AI, become more integrated into organizational infrastructures, they create new risks by opening previously locked-down databases, but AI also offers potential solutions.

The document outlines how AI can help address Shadow Access issues through continuous monitoring, providing context and visualization, conducting automated risk analysis, and enabling automated remediation. It emphasizes that Shadow Access is a lifecycle issue that requires ongoing efforts to detect, mitigate, and remediate unauthorized access. The rise of non-human identities, poor credential management, and cloud environments contribute to the complexity of Shadow Access, but AI-driven approaches can enhance security, ensure compliance, and protect sensitive data across various cybersecurity processes. The document serves as a first step in a broader discussion on leveraging AI to eliminate and prevent Shadow Access.