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Shadow AI refers to unauthorized adoption inside an organization of AI tools and platforms without oversight from IT or compliance. Much like Shadow IT, Shadow AI presents the same basic issues; employees create accounts and gain access to AI tools immediately, bypassing established governance. The speed with which generative AI tools are adopted—due to their capability for automation and productivity driving—increases these risks.
55% of employees reported using unapproved generative AI technologies at work, raising concerns about data security.
Shadow AI does not differ much from Shadow IT. It is a subset of applications wherein the AI tools tend to fall in the category of SaaS solutions, which are being accessed without oversight from IT. Both visibility and security risks for Shadow AI would be similar to Shadow IT, except that Shadow AI has additional concerns due to the sensitivity of data it normally processes and the fast AI tool adoption rate.
To get a handle on Shadow AI, an organization requires a full view of its cloud services. It starts with effective Shadow IT monitoring. Then it moves towards AI tools. Employees must have the needed awareness, including policy enforcement—like guidelines around the use of generative AI. An organization can leverage all the benefits that AI tools bring while keeping them both secure and compliant by promoting a monitored and controlled environment.
Scirge gives organizations the tools to discover and manage Shadow IT by tracking where and how corporate credentials are used across SaaS, supply-chain, GenAI, and other web applications. It helps discover Shadow SaaS and Shadow AI, and identify risks like password reuse, shared accounts, and phishing, while providing real-time awareness messages, automated workflows, and actionable insights.
