AI Tools Accelerate Shadow IT Growth

- The Torii report reveals that large enterprises average 2,191 applications, with the typical employee interacting with 40 different apps. Of all discovered applications, only 15.5% are formally sanctioned by IT departments. - "Shadow AI" is an emerging category of this problem, where employees independently adopt AI tools, often with default settings that prioritize function over security. This practice introduces new security vulnerabilities and complicates data governance, as sensitive corporate data may be processed by unvetted AI models. - A primary driver for shadow IT is the perception that official IT processes are not fast enough to meet business needs, leading employees to procure their own solutions. Studies have shown that as many as 80% of employees admit to using unapproved SaaS apps for work. - The security risks associated with unmanaged applications are significant, as they exist outside of established security protocols like firewalls and endpoint protection. One in five organizations has reportedly suffered a cyber attack linked to shadow IT. - Unsanctioned applications create serious compliance risks with regulations like GDPR and the Digital Markets Act. Storing or processing user data in unvetted third-party applications can lead to data leakage and regulatory violations. - Financially, shadow IT can account for 30-40% of IT spending in large enterprises, according to Gartner. This unmanaged spending often leads to redundant application subscriptions and inefficient resource allocation. - To regain control, IT and security teams are advised to use automated discovery tools that continuously scan networks for unauthorized applications and devices. Other mitigation strategies include educating employees on security policies and creating a streamlined process for requesting and vetting new tools.