Shadow AI

Shadow AI is becoming a growing concern for organizations as employees increasingly utilize unauthorized AI tools hidden within SaaS applications. This trend poses significant challenges to data security and regulatory compliance, leading to potential risks that many businesses are ill-equipped to address. Understanding the implications of Shadow AI is essential for protecting sensitive information and maintaining organizational integrity.

What is shadow AI?

Shadow AI refers to the use of AI tools or capabilities by employees without explicit approval or oversight from the IT department. These tools often exist outside the established governance frameworks, resulting in unmanaged digital identities and increased risk of data exposure. As AI technologies flourish, the growth of Shadow AI reflects a disconnect between rapid innovation and necessary security measures.

Prevalence of shadow AI

The rise of Shadow AI is alarming, with several studies highlighting its widespread occurrence. The **2025 SaaS Security Risks Report** emphasizes that **91%** of AI tools currently used remain unmanaged by security teams. Furthermore, AI technology adoption is progressing **four times faster** than the implementation of governance measures, highlighting the urgency of addressing this issue.

In examining the landscape of Shadow AI, it’s crucial to understand that up to **80%** of shadow applications are unfederated, and **96%** of organizations report encountering tools like ChatGPT without oversight. This rapid adoption creates an environment ripe for security vulnerabilities.

Risks associated with shadow AI

The use of unauthorized AI tools brings numerous risks that organizations must navigate carefully.

• Data exposure risks: Sensitive information, such as corporate documents and customer records, may be inadvertently exposed through these tools, leading to potential leaks.
• Compliance and regulatory violations: Utilizing unauthorized tools often results in unauthorized data transfers and retention that can violate industry regulations.
• Operational risks: Shadow AI can introduce duplicated efforts and fragmented workflows, hindering productivity and complicating project management.

Organizations must recognize and address these risks to safeguard their operations.

Identity and access risks of shadow AI

Shadow AI also raises significant concerns about identity and access management.

• Creation of shadow identities: Employees can create untracked identities that exist outside of robust systems like Single Sign-On (SSO), complicating user management.
• Over-permissioning issues: Many unauthorized AI tools allow excessive permissions through OAuth scopes and long-lived tokens, posing serious security threats.
• Data persistence and security: The content generated by AI tools often remains persistent, which complicates data security and increases the risk of leaks over time.

Proper management of identity and access is crucial to mitigate these risks.

Management and mitigation of shadow AI

To effectively manage Shadow AI, organizations need to increase visibility into unauthorized AI usage. Without clear oversight, efforts to control data access become increasingly challenging.

Tools and strategies for mitigating Shadow AI include:

• Increasing visibility: Organizations must implement measures to monitor and track AI usage comprehensively.
• Discovering unauthorized AI tools: Utilizing solutions like Grip allows organizations to identify and inventory unauthorized AI tools and features.
• Mapping tool ownership: Understanding who is using which tools helps clarify responsibilities and permissions.
• Enforcing SSO and Multi-Factor Authentication (MFA): These security measures help