During a recent conversation with a healthcare CISO, they shared something that perfectly encapsulates the challenge facing enterprises today:
“We have a lot of concerns around Copilot... People have forgotten who has access to certain file shares or SharePoint sites. They’ve misplaced something where everybody can see it.”
This isn’t an isolated concern. In our conversations with enterprises deploying Microsoft Copilot and other GenAI tools, 90% express the same fundamental fear: What if an intern gets access to CEO emails?
But here’s what’s really keeping security leaders up at night ─ it’s not just interns. It’s the marketing analyst who can suddenly ask Copilot for “vendor agreements” and gain access to financial contracts they were never meant to see. It’s the new hire who can prompt their way into acquisition documents from a completely different division.
One security leader from a Fortune 500 manufacturing company put it perfectly during our assessment:
“In the past, if sensitive information was overshared in terms of permissions, it would take employees weeks or months to find the right SharePoint, the right folder, the right file to be able to see that sensitive information. But with Copilot, they can just ask a question about it.”
This is the crux of what we call the “GenAI Amplification Effect” where existing data governance problems become exponentially more dangerous when combined with AI’s search capabilities.
When you deploy Microsoft Copilot, Google Gemini, or similar enterprise AI tools, they don't just connect to your data ─ they index it using vector embeddings that make information discoverable through natural language queries. What once required:
Now requires only:
During a risk assessment for a major healthcare system, we discovered that over 70% of Copilot queries returned sensitive patient information (PHI) to users who shouldn’t have access. The root cause? Years of “Everyone Except External Users” permissions on SharePoint sites containing patient records.
A facilities manager could ask: “Show me recent patient complaints about our emergency room” and receive detailed PHI from medical records ─ not because Copilot was broken, but because the underlying permissions were misconfigured.
At a global financial services firm, we found that junior analysts could access senior executive compensation data, M&A documents, and regulatory filings simply by asking Copilot questions like:
The data existed in broadly shared SharePoint sites that were created during various projects and never properly restricted.
A manufacturing client discovered that product engineers could access competitive intelligence, pricing strategies, and customer contract terms from completely unrelated business units all through natural language queries to their enterprise AI system.
The critical insight that many organizations miss: GenAI didn’t create these risks. It simply made existing ones impossible to ignore.
The real culprits are:
In our assessments, we consistently find that organizations with the most complex data environments have the highest oversharing risks. When you have:
...manual governance becomes impossible.
Organizations subject to HIPAA, GDPR, SOX, or industry-specific regulations face significant penalties when AI tools surface regulated data inappropriately. We've seen clients discover potential violations during AI risk assessments that could have resulted in millions in fines.
Internal strategic documents, pricing models, and competitive analysis becoming accessible across business units can compromise competitive advantage and leak sensitive market strategies.
When salary information, performance reviews, or executive communications become broadly accessible, it creates both legal liability and workplace culture issues.
Customer contracts, pricing agreements, and sensitive business terms surfacing inappropriately can violate NDAs and damage client relationships.
Before rolling out Copilot or other enterprise AI tools, organizations need to understand their current exposure:
Once AI tools are deployed, ongoing vigilance is essential:
Rather than overwhelming central IT teams, effective AI governance distributes responsibility:
The goal isn’t to prevent AI adoption but to make it secure. Organizations that take a proactive approach to AI data governance can:
The question isn’t whether your data is overshared. In our experience assessing hundreds of enterprise environments, some level of oversharing exists in virtually every organization.
The real question is: Will you discover it before or after you deploy AI to your entire organization?
If you’re planning to deploy or expand enterprise AI tools:
The “Intern Problem” is real, but it’s also solvable. Organizations that address data governance proactively can unlock AI’s transformative potential while maintaining security, compliance, and stakeholder trust.
During a recent conversation with a healthcare CISO, they shared something that perfectly encapsulates the challenge facing enterprises today:
“We have a lot of concerns around Copilot... People have forgotten who has access to certain file shares or SharePoint sites. They’ve misplaced something where everybody can see it.”
This isn’t an isolated concern. In our conversations with enterprises deploying Microsoft Copilot and other GenAI tools, 90% express the same fundamental fear: What if an intern gets access to CEO emails?
But here’s what’s really keeping security leaders up at night ─ it’s not just interns. It’s the marketing analyst who can suddenly ask Copilot for “vendor agreements” and gain access to financial contracts they were never meant to see. It’s the new hire who can prompt their way into acquisition documents from a completely different division.
One security leader from a Fortune 500 manufacturing company put it perfectly during our assessment:
“In the past, if sensitive information was overshared in terms of permissions, it would take employees weeks or months to find the right SharePoint, the right folder, the right file to be able to see that sensitive information. But with Copilot, they can just ask a question about it.”
This is the crux of what we call the “GenAI Amplification Effect” where existing data governance problems become exponentially more dangerous when combined with AI’s search capabilities.
When you deploy Microsoft Copilot, Google Gemini, or similar enterprise AI tools, they don't just connect to your data ─ they index it using vector embeddings that make information discoverable through natural language queries. What once required:
Now requires only:
During a risk assessment for a major healthcare system, we discovered that over 70% of Copilot queries returned sensitive patient information (PHI) to users who shouldn’t have access. The root cause? Years of “Everyone Except External Users” permissions on SharePoint sites containing patient records.
A facilities manager could ask: “Show me recent patient complaints about our emergency room” and receive detailed PHI from medical records ─ not because Copilot was broken, but because the underlying permissions were misconfigured.
At a global financial services firm, we found that junior analysts could access senior executive compensation data, M&A documents, and regulatory filings simply by asking Copilot questions like:
The data existed in broadly shared SharePoint sites that were created during various projects and never properly restricted.
A manufacturing client discovered that product engineers could access competitive intelligence, pricing strategies, and customer contract terms from completely unrelated business units all through natural language queries to their enterprise AI system.
The critical insight that many organizations miss: GenAI didn’t create these risks. It simply made existing ones impossible to ignore.
The real culprits are:
In our assessments, we consistently find that organizations with the most complex data environments have the highest oversharing risks. When you have:
...manual governance becomes impossible.
Organizations subject to HIPAA, GDPR, SOX, or industry-specific regulations face significant penalties when AI tools surface regulated data inappropriately. We've seen clients discover potential violations during AI risk assessments that could have resulted in millions in fines.
Internal strategic documents, pricing models, and competitive analysis becoming accessible across business units can compromise competitive advantage and leak sensitive market strategies.
When salary information, performance reviews, or executive communications become broadly accessible, it creates both legal liability and workplace culture issues.
Customer contracts, pricing agreements, and sensitive business terms surfacing inappropriately can violate NDAs and damage client relationships.
Before rolling out Copilot or other enterprise AI tools, organizations need to understand their current exposure:
Once AI tools are deployed, ongoing vigilance is essential:
Rather than overwhelming central IT teams, effective AI governance distributes responsibility:
The goal isn’t to prevent AI adoption but to make it secure. Organizations that take a proactive approach to AI data governance can:
The question isn’t whether your data is overshared. In our experience assessing hundreds of enterprise environments, some level of oversharing exists in virtually every organization.
The real question is: Will you discover it before or after you deploy AI to your entire organization?
If you’re planning to deploy or expand enterprise AI tools:
The “Intern Problem” is real, but it’s also solvable. Organizations that address data governance proactively can unlock AI’s transformative potential while maintaining security, compliance, and stakeholder trust.
