What is Shadow AI?

Shadow AI lives up to its name. It lurks in the shadows, invisibly leaking sensitive data. And because it’s not obvious, security teams are only just realizing the impact it has on an organization.

Up to 90% of employees use personal AI tools for work, yet only 40% of organizations have official LLM subscriptions. That means a lot is flying under the radar.

Shadow AI is the unauthorized use of AI tools within a work environment, and this unauthorized use has a large price tag. According to IBM’s latest data, organizations with high levels of shadow AI exposure face an average of $670,000 in breach costs.

Businesses that fail to implement AI Data Security are most at risk. That’s why it’s more important than ever to secure your business from this threat.

Shadow AI vs. Shadow IT

The way shadow AI works is fundamentally different and infinitely more dangerous than shadow IT.

Traditional shadow IT typically involves using unauthorized tools like file-sharing apps or project management platforms. While the risk is there, it’s fairly easy to contain because the data is merely stored.

But shadow AI is a different matter. Rather than simply storing data, AI tools actively process data, learn patterns from it, and potentially expose it in ways that are difficult to predict or control. AI can also hallucinate, automate decisions, and generate outputs that escape human review and oversight.

Regular apps don't do any of these things. They're fundamentally passive tools compared to the active, learning nature of AI systems.

That’s why shadow AI needs serious, dedicated attention. Even more so when you consider the scale of unauthorized AI use.

Common Examples of Shadow AI

One report has revealed that 45% of workers didn’t inform their managers when they used AI, and only 32% are proactively disclosing their use of AI tools.

This lack of transparency makes shadow AI particularly difficult to identify and address. However, certain patterns of use have emerged as especially common across organizations.

A classic, real-world example of shadow AI use comes courtesy of Samsung.

In 2023, engineers working in the semiconductor division pasted confidential source code and meeting notes into ChatGPT. The reason was innocent. They simply wanted to debug code and generate meeting minutes faster. However, in doing so, they inadvertently exposed proprietary information, leading to a serious confidentiality breach. Samsung subsequently banned the use of these AI tools and imposed severe restrictions across the company.

Consumer generative AI

The Samsung incident points to the most common form of shadow AI: employees using consumer generative AI tools like ChatGPT or Claude for everyday work tasks.

Although workers use these tools in fairly innocuous ways (drafting emails, sense-checking documents, analyzing spreadsheets, and so on), the amount of sensitive data that enters them is staggering.

Cyberhaven discovered that 73.8% of workplace AI was via personal ChatGPT accounts. The key issue is that personal ChatGPT accounts lack the security and privacy controls that come with a ChatGPT enterprise account. This means sensitive data is flowing into consumer-facing platforms every single day, often without any record of what information left the organization or where it went.

Browser extensions and plugins

AI-powered tools that assist with everyday tasks can go rogue and insert AI-generated code directly into production environments. For example, a browser extension might start auto-generating emails, and a coding assistant might start creating its own production code. Also, these tools use data for training. Even a simple extension like Grammarly leverages user-generated content to train its AI.

Department-level experimentation

When marketing teams discover a new AI asset creation tool, or legal departments find a document summarizer that saves hours of work, the temptation to experiment is strong. The problem is that these public tools are neither secure nor compliant with privacy and regulatory frameworks. Even using a new tool "just to test it out" or "see what it can do" increases the organization's risk profile in ways that most employees don't fully understand.

Key Risks of Shadow AI

Data leakage is the most concerning and immediate risk of shadow AI. Shadow AI compromises more personally identifiable information and intellectual property than other types of breaches. And once the data is out there, it’s nearly impossible to get back.

Other types of risk include:

• Compliance violations: Shadow AI routinely sidesteps regulatory frameworks like GDPR, HIPAA, and SOC 2, and ultimately lacks compliance readiness. And with the EU introducing fines of up to 4% of global revenue, the monetary cost is severe.
• Security gaps: A staggering 97% of organizations breached via AI lacked proper access controls. The stark truth is that personal accounts don’t follow corporate identity rules, MFA standards, or logging requirements.
• Lack of auditing: When employees use unauthorized tools, the organization has no visibility into what's happening. There's no audit trail showing who used which tool, what data they processed, when the interaction occurred, or why they needed it. 

Financial Impact

IBM’s 2025 data also identified that:

• 20% of organizations have already suffered a breach tied to shadow AI
• Shadow AI breaches cost $4.63M on average, compared to $3.96M for standard incidents
• 63% of breached organizations lacked formal AI governance policies

What’s more problematic is that shadow AI has overtaken security skills shortages as a key factor in driving up breach costs, making shadow AI a board-level problem. 

The best way to prevent these risks is with proper governance and data risk assessment. 

How to Detect Shadow AI

So, how do you detect something invisible?

The solution is to implement strategies that bring AI usage out of the shadows and into clear view by:

• Continuously scanning network traffic, SaaS logs, and OAuth authorizations for access to generative AI platforms.
• Monitoring for large data uploads to AI services.
• Monitoring AI usage tied to personal email, where 45.4% of sensitive AI interactions originate.
• Tracking browser extensions, plugins, and unauthorized model deployments across all areas of the organization.

Manual audits won’t suffice. This is where AI security posture management (AI SPM) becomes valuable, automating discovery and providing visibility into AI usage across the board.

Governing Shadow AI

Let’s be realistic: Banning AI tools won’t work. Employees will find a way to route around the controls, and even approved tools can introduce AI features, often without users being fully aware of them. Instead, good governance should balance safety with productivity and find a way to work with employees, not against them. Based on what we've seen work in practice, successful shadow AI governance includes these elements:

• Clear policy: Define approved tools and their permitted use cases. Establish firm data restrictions over what can and cannot be used within AI models.
• Alternatives: Deploy secure enterprise-grade AI tools that offer comparable functionality.
• Controls: Implement real-time monitoring, data loss prevention (DLP) integrations, and role-based access tied to identity systems.
• Training: Teach employees that prompts become training data. Embed the knowledge using real examples of risky vs. safe usage.

The goal is not to stifle innovation. The purpose of governance is to enable secure AI adoption and ensure that critical data is protected.

Conclusion

Organizations that treat shadow AI as a data security challenge, rather than simple IT mishaps, are already winning. They can confidently adopt AI while simultaneously managing risk. The companies that emerge with a competitive advantage won’t be the ones pretending the problem doesn’t exist, but those that see it clearly and govern it appropriately.

Discover how Cyera’s AI SPM automatically uncovers shadow AI and enforces governance across your enterprise. Schedule a demo and transform invisible risk into visible control.

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