Shadow Access is undesired or unauthorized access to resources, such as applications, networks, and data. Shadow Access is increasingly a cloud issue, resulting from the increased use of entitlements that connect cloud services together. Automated infrastructure with incorrectly permissioned accounts, the rise of non-human identities, and poor credential management contribute to the complexity of Shadow Access as well. The risks associated with unaddressed Shadow Access can be quite severe and multilayered. It can lead to data breaches, loss of intellectual property, and compliance violations.

This document explores the intricate relationship between Shadow Access and AI. It outlines how AI can reduce the risks of Shadow Access through continuous monitoring, context and visualization, automated risk analysis, and other security measures. It also emphasizes that Shadow Access is a lifecycle issue that requires ongoing efforts to address. It shows how solving Shadow Access issues enhances data security, ensures compliance, and fosters trust with stakeholders.

Key Takeaways:

• The origins and risks of Shadow Access
• How AI can help continuously monitor access, visualize patterns, perform automated risk analysis, and enable automated remediation to address Shadow Access
• How AI can help secure identity systems, prevent over-permissioned environments, and improve Identity and Access Management processes
• How to get started with addressing Shadow Access

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