shadow-ai

What is Shadow AI?

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.

How Prevalent is Shadow AI?

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:

  • Shadow AI is grossly underestimated. As much as 91% of AI tools are unmanaged by security or IT teams.
  • AI adoption is outpacing security governance by a 4:1 margin. 80% of shadow AI apps that could be federated are not.
  • Despite many organizations banning ChatGPT, it was found present in 96% of organizations analyzed. Blocking is not an appropriate security measure; employees will find a way around it, increasing shadow AI.

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.

2025 SaaS Security Risks Report download

Shadow AI Risks  

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.

The Identity and Access Risks of Shadow AI

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:

  • Connect their corporate accounts
  • Ingest sensitive documents for training or analysis
  • Generate outputs based on proprietary or confidential information

When these actions happen without centralized identity oversight, organizations face:

  • Untracked data access across unmanaged apps
  • Credential exposure through OAuth permissions and API tokens
  • Loss of control over AI-generated content that may persist outside the enterprise boundary

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.

Guide to GenAI Shadow AI risks

Can Shadow AI Be Stopped?

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.

Frequently Asked Questions

What is shadow AI in cybersecurity?

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.

How does shadow AI happen in a SaaS environment?

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.

Why is shadow AI a security risk?

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.

What types of data are exposed through shadow AI?

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.

How can organizations detect and manage shadow AI?

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.

Related Content

Free Guide: Modern SaaS Security for Managing GenAI Risk

AI Apps: A New Game of Cybersecurity Whac-a-Mole

5 Steps to Detect and Control Shadow IT

When Does Shadow IT Become Business-Led IT

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