No one knows how much sensitive corporate information employees paste into public AI systems like ChatGPT, Claude, or Gemini. There is no reliable global statistic, no peer-reviewed study, and no central reporting mechanism.

For an issue that worries CISOs, compliance teams, and executives, the most striking fact is the absence of facts.

Companies only discover these leaks when something goes wrong—or when a security review uncovers the trail. By then, it's too late.

The Pattern Everyone Recognizes

A marketing director uploads the draft Q4 earnings report to ChatGPT to "polish the language." The report includes revenue figures that won't be public for three weeks. She doesn't realize she's just fed material non-public information into a system that could be training on that data within hours.

An analyst pastes a client contract into Claude to extract key terms and deadlines. Faster than reading it manually. The contract includes confidential pricing, delivery schedules, and proprietary clauses. He assumes it's private. It's not.

A finance manager drags last month's Excel variance report into Gemini to "explain the anomalies." The file contains employee salaries, department budgets, and cost center details. She's violated data protection policy without knowing it.

The intention is never malicious. The outcome is always the same: corporate data transferred into systems the organization does not control.

Why This Keeps Happening

Employees aren't reckless. They're under pressure.

Deadlines make shortcuts feel necessary. Policies say "don't use public AI," but reality says "this solves my problem in seconds instead of hours." The gap between what's allowed and what's effective pushes people toward tools that work—even when those tools aren't approved.

And there's a false sense of privacy. It's just one document. No one will know. Everyone else is probably doing it too.

Most critically: the easy thing is not the safe thing.

Public AI tools are frictionless by design. Copy, paste, submit. Drag, drop, done. The barriers disappeared. The risks didn't.

And now, with multimodal AI processing PDFs, Excel files, and images directly, even the copy-paste step is gone. Upload becomes the default.

The Scale of the Problem

Organizations across finance, healthcare, real estate, pharmaceuticals, and the public sector are confronting the same blind spot: AI is powerful, convenient, and fast—and that combination encourages people to use it without thinking about what they're sharing.

Almost no one reports it. Employees don't log when they paste a sensitive spreadsheet into a chatbot. They don't tell compliance when they upload a customer document to get a rewrite. They don't escalate when they use AI to interpret a contract or reconcile data.

Over time, an unknown amount of internal knowledge accumulates inside external systems.

The danger isn't just data loss. It's loss of control.

Public AI models blend information from millions of users. Companies cannot guarantee where their data ends up, how long it's stored, or how it influences future model behaviour. They cannot prove compliance because the audit trail doesn't exist.

And when a GDPR auditor asks how customer data was processed, "we think someone might have used ChatGPT but we're not sure" is not a defensible answer.

What About "Enterprise" AI?

Some organizations have responded by deploying enterprise versions of public AI tools—ChatGPT Enterprise, Claude Team, and similar offerings that promise data isolation.

These are improvements over the free versions. But they still require data to leave the organization's infrastructure for processing. They still create external dependencies. And they still obscure the logic behind every result.

They solve the immediate data leakage problem but not the transparency and governance problem.

If a CFO asks why two numbers differ, the answer cannot be "the model suggested it." If an auditor asks how a report was generated, the logic cannot be hidden inside a proprietary black box.

Enterprise AI tools reduce risk. They don't eliminate it.

A Different Architecture

This is where ALLOS diverges fundamentally.

Instead of sending company information to external AI models, ALLOS brings AI to the company's environment.

Here's the critical difference:

Public AI (including enterprise versions): You upload data → AI processes it externally → Returns results

ALLOS: You describe intent → AI interprets structure → Formulas execute inside your infrastructure → Data never leaves

ALLOS interprets what the user wants to accomplish, then builds transparent formulas that execute inside the organization's systems—SAP, SQL Server, Oracle, Salesforce, whatever you use.

The AI never sees customer names, financial figures, or proprietary data. It only sees structure and intent.

You get AI assistance without data exposure. You get automation without losing the audit trail. You get speed without sacrificing control.

Every formula, every transformation, every document produced is visible, traceable, and repeatable. The organization retains full control—not just of the results, but of the process.

The Real Choice

As AI becomes indispensable in daily work, the lack of visibility around data sharing will only grow.

Companies cannot rely on assumptions about safe usage or hope that employees read every policy. They need systems that prevent data from leaving in the first place—and tools that make "the easy thing" also "the safe thing."

The most serious risk in AI today isn't that models hallucinate.

It's that employees unknowingly create invisible data leaks that no one can measure, track, or stop.

ALLOS doesn't eliminate AI risk. It replaces invisible risk with transparent intelligence—giving enterprises a way to harness AI without losing control of their information.

The question isn't whether your employees are using AI. They are.

The question is whether you've given them a safe way to do it—or forced them into the shadows.