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Organizations across industries are actively investing in AI to streamline operations, boost productivity, and stay ahead in competitive markets. However, most proceed with caution when rolling out new AI solutions internally as they need to meet standards for AI security, compliance, and responsible use through rigorous testing and assessments.
At the same time, teams may occasionally adopt AI solutions outside formal channels to simplify their workload. Often, these are commercially available tools that haven’t been vetted and approved by IT teams, which raises the issue of shadow AI, Vanta reports.
What is shadow AI?
Shadow AI refers to the use of AI tools and services within an organization outside formalized IT, security, or compliance oversight. It has become a growing trend in recent times due to the increasing accessibility of AI solutions. Options like ChatGPT, Midjourney, Claude, and Julius AI are easily available online and require little to no tech experience. This means that stakeholders may adopt them to support their everyday tasks without notifying management.
Let’s clarify the difference between shadow AI and shadow IT, which are similar concepts. While shadow AI refers to the use of AI tools without approval or oversight, shadow IT is the broader term that encompasses the unauthorized use of all software, hardware, and technology systems in an organization.
Though different in scope, both increase the chances of risk exposure and security breaches. Still, shadow IT is trickier to detect and control because it involves off-the-shelf apps, cloud-based services, and employee-owned devices that are easy to overlook.
According to Vanta’s AI governance survey, although 59% of companies feel confident in their visibility into AI tools, only 36% have or are developing an AI policy—meaning many organizations overestimate their controls and lack the formal structures necessary to manage AI responsibly.
Why should organizations worry about shadow AI?
The primary reason to worry about shadow AI is the low entry barrier for cloud-based AI tools. Most solutions require no additional setup or company credentials, and workers can use them without the guidance of AI teams.
Some of the reasons why stakeholders may resort to shadow AI are:
- Perceived productivity gain: From the layperson’s perspective, these tools surpass human processing limits and seem to deliver fast and easy results across creative use cases with no apparent harm.
- Gaps in internal governance: Many organizations still lack clear, accessible policies on how AI should (or shouldn’t) be used or what risks it poses.
- Slow approval process: Formal evaluations and approval chains are often seen as bottlenecks, so shadow AI emerges as a workaround to avoid slow internal processes.
In some cases, shadow AI doesn’t originate from internal us