Shadow AI in Your Codebase: A Hidden Supply Chain Risk
Blog post from Endor Labs
Machine learning (ML) and large language models (LLMs) are increasingly integral to modern application development, but their integration poses significant security risks, especially when adopted without oversight, a phenomenon known as "shadow AI." This occurs when developers integrate AI models and services into projects without formal review, leading to potential vulnerabilities such as prompt injection, which can be exploited to manipulate outputs or gain control over environments. Organizations often lack clear guidelines for AI use, resulting in challenges in auditing and ensuring compliance with regulations like GDPR. To mitigate these risks, firms need robust governance frameworks to manage AI usage, including policy frameworks, visibility into AI components, and automated discovery tools. Endor Labs offers solutions to help organizations build inventories of AI components, assess supply chain risks, and establish policies for safe AI integration, thereby enabling effective governance and compliance.
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