How to Spot and Address Shadow AI in Your Organization

PMsquare Team, February 9, 2026

Shadow AI is already in your organization. Employees are turning to readily available generative AI tools to speed up research, draft content, summarize meetings, and write code. The goal isn’t to stop this momentum, but to guide it. For IT and business leaders, managing Shadow AI is the first step toward a secure, scalable enterprise AI program.

Unchecked, Shadow AI can fragment data, create compliance exposure, and erode trust in outputs. Managed well, it becomes a source of insight into what your teams need and a catalyst for governed, high‑impact innovation.

How to Recognize Shadow AI

Detecting unapproved AI usage requires looking for both technical and behavioral breadcrumbs. It’s less about surveillance and more about understanding the digital and human patterns within your organization.

Technical Indicators for IT Oversight:

• Unusual Network Traffic: Monitor for sudden spikes in traffic to public AI platform domains (like OpenAI, Claude, Midjourney, etc.). Sophisticated network analysis tools can flag anomalous data egress from employee devices or departments.
• Unexpected API Usage: If your developers are working in sandboxed environments, look for API keys and calls to external AI services that are not on your approved list.
• Browser Extension Audits: Many AI tools are delivered as browser extensions that can read and process webpage data. Regularly audit installed extensions on corporate devices to identify unsanctioned plugins.
• Expense Report Anomalies: Keep an eye out for a proliferation of small, recurring subscription charges from individual employees or teams to unfamiliar SaaS companies. A $20/month charge might be a powerful AI tool operating under the radar.

Behavioral and Operational Indicators:

• Shift in Output Quality and Style: Suddenly faster deliverables, a more polished but generic tone, or uniform phrasing across different authors.
• New Jargon in Conversations: Employees might start casually referencing outputs from tools you don’t recognize. Listen for mentions of specific AI models or platforms during team meetings.
• Low Adoption of Sanctioned Tools: Teams may resist the official solution because they perceive the unsanctioned option as better or easier to use.

A Strategic Framework for Addressing Shadow AI

Once you’ve identified instances of Shadow AI, the goal should be engagement, not enforcement. A heavy-handed approach can drive usage further underground. A strategic and collaborative approach brings it into the light and converts it into governed value.

1. Discovery: Begin by cataloging the tools you discover. Create a simple inventory: What is the tool? Which team is using it? What specific business problem does it solve for them? This initial step provides a clear map of unmet needs within your organization.
2. Assessment: Not all Shadow AI is created equal. Categorize the discovered tools based on their risk to the organization.
   • Low-Risk: A grammar-checking tool or an AI for summarizing public content.
   • Medium-Risk: A code-generation assistant that doesn’t have access to the full proprietary codebase.
   • High-Risk: Any tool where employees are uploading sensitive customer data, financial records, or strategic documents. Prioritize immediate mitigation.
3. Enablement: This is the most critical step. Talk to the employees using these tools. Ask them: What do you like about this tool? How does it make your job easier? What challenges were you facing that led you to find it? Use these insights to shape sanctioned options and adoption plans.

From Shadow to Strategy: Building Your AI Adoption Strategy

The insights gained from spotting and assessing Shadow AI are the foundation of a proactive and realistic AI strategy.

Sanction and Support
For low-risk, high-value tools that are popular with your team, explore creating an enterprise-level, sanctioned account. Centralizing reduces cost sprawl, improves data controls, and supports training and support at scale.

Educate and Redirect
Clearly explain the specific dangers (e.g., “This tool’s privacy policy states they can use your data to train their models, which exposes our client information”). Provide an immediate, safe alternative that meets the same need – ideally with better integrations and support.

Iterate on Your AI Governance Policy
Your findings should directly inform your AI acceptable-use policy. If dozens of employees are using AI for transcription, your policy should address it, and you should probably invest in a best-in-class transcription tool. Your governance should evolve with the needs of your business and the AI landscape.

What Leaders Should Do Next

Here is your quick checklist:

• Stand up a Shadow AI Assessment – inventory tools, teams, and use cases
• Classify risks and quick wins – publish a simple rubric that anyone can understand
• Pick 1-2 low-risk, high-value candidates – fast track santioning and enterprise rollout
• Publish a clear AI use policy – examples of approved, caution, and prohibited behavors
• Establish a lightweight intake and vendor vetting path – teams can request new AI tools
• Loop insights into your roadmap – treat Shadow AI as signal for real demand

Conclusion

Managing Shadow AI is not a one-time project, it’s a continuous cycle of discovery, engagement, and adaptation. By treating it as a source of business intelligence, you can build an AI adoption strategy that is both secure and deeply aligned with what your employees need to succeed.

How PMsquare Can Help

PMsquare partners with IT and business leaders to operationalize enterprise AI safely and pragmatically. We help you:

1. Run a Shadow AI Assessment to assess risk.
2. Stand up a right-sized AI governance model.
3. Sanction the right tools and drive adoption with enablement and change management.

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