The Hidden Risk in Enterprise AI, and the Smarter Way to Safeguard Data

What The Data Shows: AI Is Everywhere And Sensitive

Cameron opens with the big picture: AI adoption isn’t just for tech companies anymore. It’s spreading into retail, pharma, healthcare, financial services, and manufacturing. Basically, any industry where productivity mandates rapid experimentation

But adoption isn’t without risk. When Cyberhaven examined the data flowing into AI systems, whether it was off-the-shelf tools like ChatGPT or in-house models, just over a third of the data sent by employees in the last year was classified as sensitive corporate data.

This trend signals two things at once: AI is becoming increasingly embedded in mission-critical workflows, and employees are entrusting these tools with highly sensitive information, ranging from customer records to proprietary business insights. It’s powerful, but also precarious.

The Shadow AI Reality

Not only is sensitive data flowing into these tools, but many of the tools themselves carry steep risks. Cyberhaven’s analysis found that over 23% of AI apps in use fall into the critical risk category, while another 50% are rated high risk. That means nearly three out of four AI applications that employees are already using introduce major security concerns.

And critically, only 16% of corporate data went to enterprise-ready apps. The rest was funneled into personal accounts or unvetted tools. This is a pattern Cameron calls Shadow AI. It echoes the SaaS explosion of a decade ago: employees chasing productivity with tools IT never approved. The difference this time? AI spreads faster, is easier to adopt, and is far harder to see.

Security for AI: A Practical Framework (4 pillars)

With so much AI usage happening in the shadows, Cameron pivots from the problem to the solution: how can organizations gain visibility, measure risk, and enforce control?

That’s where Cyberhaven’s new “Security for AI” comes in, launched on July 25. Powered by Cyberhaven’s data lineage core, it delivers four capabilities that go beyond standard monitoring:

• Shadow AI Discovery – Build an exhaustive, continuously updated registry of AI tools in use, including embedded copilots inside SaaS.
  
  
• AI Usage Insights – Go deeper than discovery: map how data moves, which users rely on AI, and where sensitive flows occur.
  
  
• AI RiskIQ – Apply a five-dimension risk model (data security, model security, compliance, auth/access, vendor practices) to score tools from very low → critical risk.
  
  
• AI Data Flow Control – Enforce policy over sensitive data moving to and from AI, tailoring controls to each tool’s risk profile. 

Linea AI: Using AI to Secure AI

In this section, Dave Stuart explains that policies inevitably have blind spots. They only catch what they’re told to. Thankfully, Linea AI was designed to close those gaps, applying AI to discover risks you may not have even known to look for.

With this approach, teams can:

• Resolve incidents 5× faster with automated severity scoring and natural-language summaries.
  
  
• Expose hidden risks, since up to 40% of high- and critical events are uniquely detected by AI.
  
  
• Reduce manual reviews by 90%+, escalating only incidents that AI confirms as truly high or critical.

How Linea AI Detects and Prioritizes Risk

Linea AI builds a behavioral model for each customer, trained on their own historical data. That way, it knows what “normal” looks like inside a given environment, and flags anything that falls outside that baseline.

For example, copying sensitive data into Telegram might be perfectly fine in one company but highly unusual in another. Linea AI adapts to those differences, surfacing anomalies that static policies would miss.

And it doesn’t stop at detection. Instead of leaving teams with a flood of suspicious events to triage, Linea AI runs its own severity assessment and prioritization. Two incidents that appear identical on the surface can be instantly distinguished: one is truly high-risk, while the other is non-critical.

This built-in context and scoring means security teams can focus attention where it matters most, not waste time sifting through false alarms.

To illustrate, Dave walks through an example. Linea AI identifies an event with no matching policy—the kind that might have gone unnoticed in the past. But rather than leave it ambiguous, the system:

• Surfaces it as a potential high risk
  
  
• Auto-generates a notional policy to explain what was unusual
  
  
• Provides a natural-language summary of what happened, what data was involved, and where it was headed
  

That combination of detection, context, and explanation is what makes it clear why the incident warrants high severity and demonstrates how Linea AI uncovers threats that traditional tools never see.

Collaboration in Action

The partnership between Cameron Galbraith and Dave Stuart blended research-backed insight with practical demonstrations. Cameron framed the risks and trends; Dave showed how the technology addresses them in real environments. Together, they made a clear case for security-first AI adoption.

The takeaway

AI adoption isn’t slowing. Employees will continue to use new tools because they’re effective. So, the answer isn’t to block AI, but instead to see it, understand it, and control it so sensitive data stays protected while teams move faster.

Don't let shadow AI remain your blind spot. Watch the full webinar to continue discovering how to gain visibility into unapproved AI tools and see live demonstrations of automated risk detection.

Want more insights into AI risks? Download the full report on AI Adoption & Risk.