Foundation Route

Confidentiality Audit: What Not to Paste Into ChatGPT

Build a clear, shareable policy that tells your team exactly which data is safe for AI tools and which stays out.

10 steps ~1h For all professionals Free

A confidentiality audit for ChatGPT gives your team clear boundaries on what data can and can't go into AI tools. On aidowith.me, a 10-step route walks you through cataloging your company's data types, rating each one by sensitivity level, and building a classification matrix your whole team can follow. You'll create a red/yellow/green list where red items (customer PII, financial records, source code with API keys) never enter any AI tool, yellow items need redaction before pasting, and green items are safe to use freely. The route also covers how to write an internal policy document, brief your team in under 15 minutes, and set up a quarterly review process that keeps the policy current. Research shows over 40% of employees paste sensitive data into AI tools without guidelines. This audit closes that gap in about 1 hour of focused work with clear, actionable output.

Last updated: April 2026

The Problem and the Fix

Without a route

  • Over 40% of employees have pasted confidential company data into ChatGPT without any guidelines in place
  • A single data leak through an AI tool can trigger GDPR fines starting at 20 million euros
  • Most teams have zero written policy on what's safe to share with AI, leaving every paste a judgment call

With aidowith.me

  • Build a red/yellow/green classification matrix covering every data type in your organization
  • Ship a ready-to-distribute internal policy document your team can follow in under 15 minutes
  • Set up a quarterly review process so your policy stays current as AI tools and regulations evolve

Who Builds This With AI

Marketers

Content, campaigns, and briefs done in hours instead of days.

Sales & BizDev

Prep calls, draft outreach, research prospects in minutes.

Managers & Leads

Reports, presentations, and team comms handled faster.

How It Works

1

Catalog and classify your data types

List every category of data your team handles: customer PII, financials, contracts, internal comms, source code. The AI helps you rate each by sensitivity and map it to a red, yellow, or green tier.

2

Build your confidentiality policy document

Draft a clear, jargon-free policy that explains what's allowed, what needs redaction, and what's banned. The AI formats it for easy scanning with examples for each tier.

3

Create a team rollout and review plan

Design a 15-minute briefing for your team, write a quick-reference card for daily use, and schedule quarterly reviews to update the policy as new tools and regulations appear.

Protect Your Company Data From AI Leaks

Build a clear confidentiality policy your team can follow starting today.

Start This Route →

What You Walk Away With

Catalog and classify your data types

Build your confidentiality policy document

Create a team rollout and review plan

Set up a quarterly review process so your policy stays current as AI tools and regulations evolve

"We had no AI policy before this. Now every team member has a one-page cheat sheet on their desk. Took me 45 minutes to build the whole thing."
- Operations Manager, healthcare startup

Questions

It covers every type of data your team might paste into an AI tool, from customer records to internal strategy docs. You'll classify each type as safe, needs-redaction, or banned. The output includes a policy document with specific examples, a quick-reference card for daily use, and a review schedule. It's practical and specific to your company's data.

Yes. The audit applies to any AI tool that processes text input, not just ChatGPT. The classification matrix and policy document are tool-agnostic by design. You'll list which AI tools your team uses and apply the same red/yellow/green rules across all of them. The underlying risks are the same regardless of which provider you use.

The route sets up quarterly reviews as a starting point. If your company handles highly regulated data in healthcare or finance, monthly checks make more sense. Each review takes about 20 minutes: you scan for new data types your team handles, new AI tools being used, and any regulatory changes that affect what's safe to share externally.