Shadow AI in Action: 5 Steps to Shadow AI Governance

Maya Rosenstein
Aug 10
3 min read

By: Yitav Cohen, Head of Professional Services, Commugen

In 2025, generative AI is deeply embedded across departments—from marketing using ChatGPT to developers leveraging Copilot. Shadow AI is no longer a theoretical concern. It’s already shaping daily workflows, often invisibly. For CISOs, Compliance Leaders, and Risk Managers, the challenge isn't whether Shadow AI is present—it’s how to govern it.

This article explores the operational side of Shadow AI governance, based on real-world implementations with CISOs in banking, pharma, and critical infrastructure. We'll show why policy alone isn't enough—and how automation, contextual nudges, and live inventories form the new standard for scalable AI risk management.

What Is Shadow AI?

Shadow AI is the new face of unmanaged digital risk. Unlike traditional Shadow IT, which focuses on unsanctioned applications, Shadow AI interprets, learns from, and acts on organizational data. It sneaks in via browser extensions, freemium tools, or features embedded in common platforms like Excel or Slack, often evading legacy monitoring.

The real risk? It operates without oversight, logging, or accountability, making decisions and generating outputs independently.

Learn more about Shadow AI in our full Guide

Key Risk:

Data Leakage: Sensitive information may be fed into public AI systems.
Non-compliance: Violations of GDPR, NIS2, or company-specific frameworks.
Untraceable Decision Logic: Lack of explainability in decisions made by AI-generated outputs.
IP Exposure: Proprietary code or business logic copied into public tools.

Why Policy Alone Fails

Most organizations start with a Shadow AI Usage Policy but fail to enforce it effectively. Policies without integration into workflows, tools, and team behavior simply don’t stick.

Imagine emailing a policy PDF and hoping developers will stop using Claude. It won’t work.

"Effective AI governance must shift from documented intent to embedded action.”

-Yitav Cohen

From Policy to Practice: 5 Steps to Shadow AI Governance

1. Inventory Shadow AI Usage

Begin with visibility. Who’s using what, and where?

Use an AI Risk Management Module that automates this by integrating with browser telemetry, form usage logs, and internal app data.

2. Classify AI Use by Risk Level

Use a pre-configured AI Use Case Register to score risk-based AI interactions.

Example:

AI Use Case	Risk Level	Example
Code generation with PII	High	Devs using customer data in Copilot
Text summarization of internal docs	Medium	HR using ChatGPT
Image creation for slides	Low	Marketing using Midjourney

3. Embed Controls Directly into Workflows

To translate policy into meaningful action, organizations are embedding governance and policies into the tools and processes employees already use.

Key steps:

Define categories of AI usage: Establish whether certain tools or use cases are permitted, require review, or are prohibited.
Assign oversight responsibilities: Set rules for which teams (such as Legal, Compliance, or IT Security) need to review or approve certain types of usage.
Log high-risk usage: Ensure employees document their intent and context for using AI in high-risk scenarios.
Activate governance in real time: Build controls that operate within daily workflows, rather than as separate or retroactive reviews, to support traceability and reduce reliance on passive policy documents.

This approach embeds risk mitigation into operations, minimizing friction while increasing accountability.

4. Train Employees with Real-Time Microlearning

Replace generic training with just-in-time guidance.

Example: Trigger a reminder on safe data use when an employee opens ChatGPT.

5. Centralize Risk Monitoring and Audit Trail

Real-time dashboards give CISOs and DPOs a view into AI use, gaps, and emerging risks. The system auto-generates audit reports for frameworks like:

NIST AI RMF
ISO/IEC 23894
EU AI Act readiness

How Can CISOs Control Shadow AI Usage?

Q: What tools are needed to control Shadow AI?

A: A flexible cyber GRC platform with the following capabilities:

Central AI usage logging
Risk-level categorization
Conditional workflows for high-risk scenarios
Visual dashboards for board reporting

Q: Can we block Shadow AI with traditional DLP?

A: Only partially. DLP can stop known domains but won’t catch nuanced uses (e.g., browser extensions or embedded API use). That’s why policy enforcement, not just blocking, is essential.