What is shadow AI data loss prevention?

Shadow AI data loss prevention is detecting and controlling the sensitive data developers put into AI coding assistants outside sanctioned controls - credentials, .env contents, and source code pasted into Claude Code, Cursor, Codex, Gemini, or Copilot. Because that data lands in local AI session logs rather than email or uploads, traditional endpoint and network DLP do not see it; the coverage comes from scanning the AI session logs on the developer machine.

Did my developers paste API keys into ChatGPT or Claude?

You can answer that with evidence by scanning the local AI session logs. Skarn parses the on-disk transcripts of the AI coding assistants, detects leaked credentials across 200-plus types, and attributes each finding to the exact session and message - per developer, redacted, with a risk score - instead of leaving it a guess. It runs locally with no upload.

Why can traditional DLP not see it?

Endpoint and network DLP inspect email, web uploads, USB, and traffic in transit. A secret pasted into a local AI coding assistant is written to that tool's local session store, not exfiltrated over a monitored channel, so it never crosses a DLP boundary. The AI session log is the blind spot.

Guide

Shadow AI data loss prevention for developers

Shadow AI data loss prevention means detecting the sensitive data developers put into AI coding assistants - and it happens on a surface your DLP does not watch. Credentials, .env contents, and source pasted into Claude Code, Cursor, Codex, Gemini, or Copilot land in each tool's local session log, not in email or an upload, so endpoint and network DLP never see them.

The coverage comes from scanning those AI session logs on the developer machine. Skarn does it locally, with nothing uploaded, and answers the direct question: what has leaked, and from which developer.

Last updated 2026-07-03.

The gap in plain terms

DLP watches the wrong doors

Email, web uploads, USB, and network traffic are covered. The local AI coding session transcript is not - it is written to disk on the machine and never crosses a monitored channel.

Git scanners watch the wrong files

gitleaks and trufflehog scan commits. A secret pasted into a chat and never committed never enters git.

So the leak sits unseen

Between the two, the credential a developer pasted to debug an integration sits in plaintext in an AI session log, outside every control - until you scan that log.

How to close it

Scan the AI session logs locally

Run an AI session security scanner over the assistant session stores. Skarn covers Claude Code, Codex CLI, Cursor, Gemini CLI, and GitHub Copilot from one binary - see the how-to guide.

Keep it local and audit-ready

No upload, no telemetry, nothing sent to a vendor. Output is SARIF and JSON for your SIEM, with findings crosswalked to MITRE ATLAS, the OWASP Top 10 for LLM Applications 2025, and CWE - evidence a regulator accepts.

Gate and coach, do not block the tool

Gate CI on severity or risk score, rotate exposed credentials, and coach developers. The goal is visibility and a control, not banning the assistant.

Skarn surfaces exposure - it does not auto-remediate. Your team owns rotation, coaching, and policy.

Find your shadow-AI exposure

Book a scoped, consent-first exposure assessment and see, in your own data, what is leaking into AI sessions right now. Nothing sent to a cloud.

Book an exposure assessment Skarn for CISOs

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