How to Discover and Audit Shadow AI Tools in the Enterprise

Maya Rosenstein
Aug 7
3 min read

By: Eldad Levi, Q-GRC Manager, Commugen

As Q-GRC Manager, my role blends quality assurance with GRC operations, so I see firsthand how fast Shadow AI tools like ChatGPT or Copilot slip into daily workflows unapproved, unlogged, and invisible to traditional audits.

This post shares how we approach Shadow AI discovery, risk scoring, and AI governance in a way that aligns with frameworks like GDPR, NIS2, and the EU AI Act without slowing innovation or relying on outdated spreadsheets.

Why Traditional Audits Miss Shadow AI?

Most audit frameworks are built to track tools, but Shadow AI spreads through behaviors. Employees adopt browser-based tools like ChatGPT, Copilot, or Gemini to write code, summarize reports, or translate documents without approval or logging. These activities don’t leave a software footprint.

Key reasons Shadow AI evades audits:

Used via personal browsers and plugins
Embedded in tools like Notion or Slack
Inaccessible to traditional DLP, CASB, or IAM systems
No audit trails, prompt logs, or formal disclosures

To manage AI risk, you need a behavioral discovery process, not just asset inventory.

Step 1: Where Shadow AI Tends to Hide

It usually doesn’t start in IT. Sales, Marketing, R&D, and Legal are typical hotspots, especially when teams are pressured to deliver quickly or handle high-stakes tasks.

These are the environments where AI quietly slips into daily workstreams, often unnoticed.

Our Shadow AI guide outlines where to focus your initial discovery and why these teams matter most.

Step 2: How to Discover Informal AI Use

Shadow AI use rarely leaves technical footprints. Instead of relying on logs, discovery happens through conversations: lightweight surveys, short interviews, and pattern mapping.

Our Shadow AI guide offers ready-to-use questions that help surface informal GenAI use without micromanaging.

Step 3: Build a Lightweight Shadow AI Inventory

Once discovered, not every AI tool needs the same level of attention. A lightweight inventory can go a long way.

Track just the essentials:

What to Track	Why It Matters
Tool & Use Case	Helps categorize risk
Access Method	Browser vs. embedded
Data Type	Sensitive vs. public

Step 4: Use a Shadow AI Risk Scoring Model

Not all GenAI use is equal. Some tools handle low-stakes summaries, while others touch customer data, code, or contracts.

Our Shadow AI guide explains a 4-factor model to help you quickly score Shadow AI risk and focus your attention where it matters most.

Step 5: Apply Tiered GRC Responses to AI Risk

You don’t need to block every use. Some should be monitored, others should be paused, and many can be formalized with policy.

Our guide breaks down a tiered model that can be applied across teams for those who want to respond without overreacting.

FAQ: How Can CISOs Discover Shadow AI Without Micromanaging Productivity?

Shadow AI isn’t a control problem, it’s a visibility one. Instead of heavy oversight, start with lightweight methods: anonymous surveys, interviews, and behavioral mapping. These approaches surface GenAI use without disrupting teams' productivity.

Platforms like Commugen can help structure this process—automating discovery, scoring, and oversight—so CISOs stay informed without getting in the way.

Why Traditional Cyber GRC Can’t Detect Shadow AI

Spreadsheets are too static. CASBs and DLP can’t inspect AI prompts. Policies written in PDFs never reach the user at the point of use.

That’s why platforms like Commugen’s AI Risk Management solution matter:

Map Shadow AI usage to teams and workflows
Score GenAI risk levels using contextual factors like data sensitivity, business impact, and visibility gaps.
Enforce AI governance policies in real time, with embedded approval flows and usage guidance at the point of AI interaction.
Build a dynamic Shadow AI inventory with live dashboards for executive oversight, audit readiness, and regulatory compliance reporting.