Shadow AI

March 11, 2025

What is Shadow AI?

Shadow AI encompasses any AI tools, models, or automation processes used within an organization that are not formally approved, managed, or secured by IT or security teams. This includes:

Public Gen AI tools like ChatGPT, Gemini, Claude, or Midjourney used for content creation, coding, or research.
Unapproved AI-powered SaaS applications that employees leverage for automation, decision-making, or analytics.
Internally developed AI models running on personal or departmental servers without proper oversight.

Unlike traditional Shadow IT, which typically involves unauthorized SaaS applications, Shadow AI presents a unique set of risks—particularly when it comes to data security, bias, compliance, and governance.

Why Shadow AI is a Security and Compliance Risk

1. Data Leakage & Privacy Violations

When employees input sensitive corporate data, customer information, or intellectual property into AI tools, that data may be stored, logged, or even used to train future AI models. Without visibility into AI usage, organizations have no way to track where their data is going.

Example: An employee uses a public Gen AI chatbot to refine a company report, unknowingly exposing proprietary data to third-party servers outside of compliance boundaries.

2. Compliance & Regulatory Risks

With regulations like GDPR, HIPAA, CCPA, and CMMC, organizations must control how sensitive data is accessed, shared, and stored. Shadow AI tools can inadvertently violate these regulations, leading to hefty fines and legal issues.

Example: A healthcare company uses an AI transcription tool without vetting its security policies, potentially exposing patient data in violation of HIPAA.

3. AI Bias & Decision-Making Risks

Many AI models operate as black boxes, meaning their decision-making processes aren’t always clear. Unvetted AI tools can introduce bias, errors, or unethical outcomes, impacting hiring decisions, financial predictions, and customer interactions.

Example: An HR team starts using an AI-powered recruitment tool without IT or legal review, only to discover it has an inherent bias against certain demographics.

4. Intellectual Property Risks

AI-generated content and code may not be owned by the organization using them. Some AI models have been trained on copyrighted materials, leading to potential IP infringement issues if companies deploy AI-generated content commercially.

Example: A marketing team uses AI-generated images in a campaign, only to receive a legal notice for copyright infringement.

5. Shadow AI Bypasses Security Controls

If employees access AI tools using personal accounts or create unauthorized app-to-app connections, security teams lose visibility into who is using AI, how they’re using it, and what data is at risk.

Example: An employee integrates a personal AI assistant with a company’s cloud storage, creating a security blind spot where sensitive files are being analyzed offsite.

How to Detect and Manage Shadow AI

Addressing Shadow AI requires visibility, governance, and enforcement. Here’s how organizations can take back control:

1. Discover & Inventory AI Usage

Use AI discovery tools to detect AI-related activity across browsers, SaaS applications, and employee devices.
Conduct employee surveys and audits to understand where AI is already being used.

2. Establish Clear AI Governance Policies

Define which AI tools are approved and which are restricted.
Create a data classification policy that outlines what information can and cannot be shared with AI systems.
Train employees on AI security risks and compliance obligations.

3. Secure AI Access with Identity Controls

Enforce SSO and MFA for all AI applications to ensure centralized identity management.
Prevent SSO bypass by redirecting employees from unapproved AI tools to approved alternatives.

4. Monitor & Enforce AI Security in Real Time

Deploy security tools that can detect and block unauthorized AI usage.
Use automated security guardrails that guide employees towards safe AI usage.

5. Review AI Outputs & Data Handling

Ensure that AI-generated content is reviewed before use in business-critical decisions.
Monitor AI-driven automation and integrations for bias, security, and compliance risks.

Frequently Asked Questions (FAQ)

1. How does Shadow AI differ from Shadow IT?

Shadow IT refers to unauthorized SaaS applications, while Shadow AI specifically refers to the use of AI-powered tools and models without security or IT approval. Shadow AI introduces unique risks like data leakage, compliance violations, and biased decision-making.

2. What types of AI tools contribute to Shadow AI?

Shadow AI can include:

Generative AI tools (ChatGPT, Gemini, Claude, DALL·E, Midjourney).
AI-powered SaaS applications (automated chatbots, AI analytics platforms).
Internally developed AI models used without oversight.

3. Why is Shadow AI a security concern?

Shadow AI bypasses traditional security controls, leading to data privacy issues, compliance violations, and security blind spots. Employees may unknowingly expose sensitive data by inputting it into AI models that log and retain user inputs.

4. Can AI-generated content create legal risks?

Yes. AI-generated content may be subject to copyright restrictions, and companies using AI-generated material without proper verification could face intellectual property disputes. Additionally, AI-generated decisions (like hiring recommendations) can introduce bias and legal liability.

5. How can organizations detect and prevent Shadow AI?

Organizations can detect and prevent Shadow AI by:

Using AI discovery tools to identify unauthorized AI activity.
Implementing AI governance policies to define approved usage.
Enforcing security controls like SSO, MFA, and data classification.
Monitoring AI-generated content for compliance and accuracy.

6. Is banning AI tools the solution?

No. Instead of banning AI tools outright, organizations should provide secure, approved AI alternatives that meet business and security requirements. Guidance and governance are more effective than restriction.

AI is here to stay, and organizations can’t afford to ignore the rise of Shadow AI. Without proper governance, AI adoption can lead to compliance risks, data exposure, security gaps, and financial liabilities.

The key isn’t blocking AI, but controlling and securing its use—through clear policies, proactive security measures, and real-time enforcement.

How well does your organization manage AI adoption? If you’re unsure, now is the time to audit your AI landscape before Shadow AI spirals out of control.