Your IT team enabled Copilot and Gemini last quarter without checking with the lawyers. Now your employees are putting company secrets into systems that nobody owns, nobody governs, and nobody can reliably retrieve when opposing counsel sends a subpoena.

You have a discovery problem, and it’s hiding in plain sight.

This isn’t a technology problem. It’s a C-suite accountability issue that’s been masquerading as an IT rollout. And if you haven’t thought about it yet, you’re not alone—most enterprise leaders haven’t, either. That’s exactly why the problem is getting worse.

The hidden exposure in everyday AI use 

Here’s what’s actually happening on the ground. Roughly 77% of your employees are pasting sensitive information into AI tools, and about a fifth of those data include payment information or personal identifiers they shouldn’t be touching at all. This isn't done maliciously. They’re just doing it because it’s fast, it works, and they have no idea their “chat” is going to show up in a courtroom someday.

Worse, many are using personal or unapproved tools to do it. Shadow AI—generative tools outside corporate control—is now present in more than half of enterprises. Your people are scattered across ChatGPT, Gemini, Copilot, specialty AI SaaS platforms, and browser extensions. They’re doing real business work in systems you can’t see, can’t audit, and absolutely can’t collect from when discovery obligations kick in.

Meanwhile, fewer than half of companies have any formal AI governance policy in place. Think about that for a moment. You have heavy adoption but almost no guardrails. That asymmetry is exactly what creates litigation disasters.

Two different problems, one risk profile 

Enterprise AI tools—Copilot, Gemini in Workspace—at least sit inside your corporate infrastructure. Microsoft 365 Copilot logs and retains interactions as compliance records integrated into your e-discovery workflows. Google’s Gemini aligns generated content with existing Workspace retention settings. This is actually manageable, if you set it up right. Your legal team can place holds. Your IT can collect it. You can produce it. It’s not fun, but it’s knowable.

Shadow AI is the nightmare. When a senior leader is making a critical decision in a personal ChatGPT account, you’re flying blind. You don’t know how long prompts and responses persist. You don’t know where the data are processed or stored. You don’t know if it’s sitting in a system governed by terms that conflict with your privacy obligations. And when you need to produce evidence from that conversation? You either can’t, or your disclosure is incomplete—which looks to opposing counsel and regulators like you’re hiding something.

The legal exposure is real. Incomplete or deliberately withheld discovery can lead to sanctions, adverse inferences in judgment, and regulatory penalties. Shadow AI makes all of that more likely because you genuinely may not know what data exist or where they have gone.

Why this matters more than you think 

Your IT team sees AI as a productivity tool. Your business teams love it. But your legal and compliance teams should be seeing this as a records management and governance crisis that’s been repackaged as a technology implementation.

Regulators are beginning to care. Financial services, healthcare, and data protection agencies increasingly expect organizations to demonstrate that AI use is governed under coherent policies aligned to existing information governance frameworks. Boards are being explicitly warned: AI governance isn’t optional; it’s table stakes for defensible operations. And discovery readiness—the ability to collect, hold, and produce AI-related evidence—sits right at the center of that governance mandate.

Most organizations haven’t connected those dots yet. The result is that your AI systems are evolving faster than your discovery and retention practices.

What you actually need to do 

This is fixable, but it requires three simultaneous moves.

First, classify AI interactions as corporate records

Work with your legal team to define this explicitly: Prompts, responses, and generated content created in enterprise tools are business records by default, not personal notes. That means they’re subject to retention schedules, legal holds, and discovery obligations. It changes how IT configures systems and how people use them.

Second, harden your controls

Have IT apply the same rigor to AI platforms that you’d apply to email or financial systems. Implement consistent retention policies. Integrate AI activity into your data loss prevention and cloud access tools. Make shadow AI visible so you can detect it and reduce it. You’re trying to move as much risky behavior as possible from “invisible personal tools” into “visible, governed enterprise tools.”

Third, reset expectations with your workforce

Most employees think their AI chats are private and ephemeral. They’re not. Tell your people directly: Enterprise AI interactions are logged and discoverable. If you put company data into a personal AI tool, you’ve violated policy. Make it clear and make it stick.

The path forward 

Organizations that move on this now will invest in frameworks and training. Organizations that wait will spend money on litigation defense, regulatory fines, and the slow, painful process of explaining why their discovery efforts were incomplete.

Your board should ask: Where are we in that timeline? Because the clock is already running.