All postsStart free
App Security5 min read

Shadow AI: The Data Leak Hiding in Your Employees' Apps

ER

Elena Rodriguez

VP of Engineering · June 17, 2026

Illustration of corporate data flowing out of an office into unsanctioned AI chatbot apps

Surveys this year keep landing on the same uncomfortable number: a large majority of employees use AI tools their company never approved, and many paste in data they should not. Shadow AI, the unsanctioned use of chatbots and AI features, has quietly become one of the biggest data-leakage channels in the enterprise. The data walks out through an app on a personal or corporate phone, often with no logging at all.

Why shadow AI is so hard to see

Unlike a classic exfiltration attempt, shadow AI looks like normal productivity. An employee pastes a snippet to debug it or summarizes a document, and sensitive data leaves through a sanctioned-looking interface that security tooling never inspects.

On mobile especially, AI features are embedded everywhere, in keyboards, browsers, and assistants, so data can leave without the user even opening a dedicated chatbot.

What leaks, and how

The patterns are consistent across the builds and workflows we review.

  • Source code, API keys, and config pasted into external AI tools for help
  • Customer records and internal documents sent to summarize or rewrite
  • AI-enabled keyboards and assistants with broad access to in-app content
  • No logging, DLP, or policy controls on AI feature usage inside apps

How RASMISER helps you regain visibility

RASMISER flags where your own app exposes data to third-party AI SDKs and assistants, where content leaves the client without controls, and where embedded AI features have more access than they need, mapped to OWASP MASVS.

With runtime protection in place, the data paths leaving your app stay monitored and locked down, so an embedded AI feature cannot become a silent exfiltration route.

The takeaway

Shadow AI is a leak that looks like productivity, which is exactly why it spreads. Inventory where your app hands data to AI features and put controls on those paths before the next paste becomes a breach.

Scan your app free

Keep reading

Illustration of an AI model generating personalised phishing messages targeting employees on mobile devices
Cyber Threats

AI-Generated Phishing: Why Every Employee Is Now a High-Value Target

LLMs can now write flawless, hyper-personalised phishing messages at industrial scale. The generic mass-phish is dead — what replaced it is far harder to spot and your app is the delivery mechanism.

Illustration of an AI malware strand mutating its code to bypass antivirus detection layers
Cyber Threats

Autonomous AI Malware: The Threat That Rewrites Itself to Escape Detection

A new class of AI-driven malware can observe its environment, modify its own payload, and wait for the ideal conditions to strike. Static signatures are no longer enough, and your app binary is a target.