Shadow AI is the use of artifical intelligence (AI) applications and tools without the explicit approval or oversight of an organization’s IT department. Shadow AI is becoming increasingly common in today’s fast-paced digital environment, and typically occurs in one of two ways:
1. Staff enable newly released AI features of existing and already sanctioned tools, without realizing the update necessitates a review by IT and security teams prior to deploying.
2. Departments or teams, driven by innovation and agility, use SaaS AI apps to improve productivity and meet their objectives, without waiting for central IT or security review and approval.
According to Grip's 2025 SaaS Security Risks Report, shadow AI is no longer an emerging trend; it’s a growing reality. Key findings include:
These numbers reflect a broader shift: AI is being embedded across SaaS ecosystems faster than security teams can adapt. The result is a significant visibility and control gap, where innovation often outpaces governance.
Read the full report for more on shadow AI and shadow SaaS.
While AI apps can drive innovation and operational efficiency, shadow AI harbors significant risks. The lack of oversight and integration with established IT security and governance frameworks can lead to data breaches from AI apps developed with few (or questionable) security protections, non-compliance with data protection regulations, and the unintentional leak of proprietary data. Shadow AI applications may also duplicate efforts or work at cross purposes with other IT initiatives, leading to operational inefficiencies and increased costs.
What makes Shadow AI particularly risky isn't just the application; it’s also the unmonitored identities and permissions behind it. Many AI-powered SaaS tools allow users to:
When these actions happen without centralized identity oversight, organizations face:
Shadow AI compounds the challenges of shadow SaaS, creating an even more complex web of users, tools, and data flows that evade traditional security controls.
Shadow AI usually starts with good intentions. Teams adopt AI tools or enable new AI features in existing SaaS applications to gain speed, insight, or productivity, without realizing they’ve bypassed security and governance policies. From simple chatbots to note taking tools to advanced machine learning models, these tools are now easily accessible, especially in cloud-based SaaS environments. Because Shadow AI doesn't require technical expertise to deploy, it spreads quickly—and silently—across departments.
Stopping shadow AI starts with visibility. Organizations can’t manage what they can’t see. That’s where Grip comes in.
Grip helps security teams discover, assess, and control the use of AI tools across the enterprise, even when they’re enabled by end users or embedded inside SaaS apps. By detecting unauthorized AI usage, mapping associated identities and access, and evaluating the risk of newly adopted shadow AI apps, Grip gives teams the context they need to regain control over AI adoption, without stifling innovation. See how Grip reduces shadow AI risks, then take the next step and book a demo with our team.
Shadow AI refers to the use of artificial intelligence tools or AI-powered SaaS applications within an organization without IT or security team approval. This includes standalone AI tools or new AI features embedded in existing software that users enable without governance.
Shadow AI typically emerges when employees or teams adopt new AI tools to automate tasks, analyze data, or enhance productivity, without going through formal review processes. It also occurs when users activate AI features in SaaS apps that were previously approved in a non-AI form, bypassing renewed risk assessment.
Unapproved AI tools can access or store sensitive data, create unmonitored accounts, and introduce unauthorized access pathways. Without visibility into how AI tools handle data or interact with other services, organizations face increased risk of data leaks, compliance violations, and identity exposure.
Shadow AI tools may interact with corporate documents, customer records, source code, financial data, or employee credentials. When these tools are not reviewed for data protection practices, they can lead to unintended data sharing or AI model training on sensitive content.
To manage shadow AI risk, organizations need tools that automatically detect unauthorized AI tools and SaaS apps in use. Solutions like Grip help detect when a new AI tool enters a SaaS environment, who it belongs to, the risk severity of it, and recommended actions to take next, such as applying controls like SSO, MFA, or revoking access entirely. Learn more about Grip's shadow AI detection and management capabilities.
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