What if I am not sure how to classify some data?

When in doubt, classify at the higher sensitivity level. It is safer to over-protect data than to under-protect it.

How do I handle mixed-sensitivity documents?

Classify the entire document at the highest sensitivity level present. Alternatively, extract the non-sensitive portions and use those with AI.

Do procedures need to be different for each AI tool?

The data classification stays the same. But the approved tools for each level may differ. Enterprise tools with data protection may handle internal data, while public tools should only receive public data.

Quick answer

Map your data types, classify each by sensitivity, create clear rules for each classification level, train your team, and verify compliance monthly. Start simple and adjust based on real-world experience.

Updated June 2026 · MmowW AI Compliance

How to Create AI Data Handling Procedures for Your Team

Why Data Handling Procedures Matter

Most AI compliance problems come down to data handling. An employee pasting the wrong data into the wrong tool is the most common and most preventable AI risk. Clear data handling procedures eliminate guesswork and protect your company from accidental data exposure.

Step 1: Map Your Data

List every type of data your team handles in their daily work. Customer contact information, financial data, employee records, product specifications, client project details, internal communications, and business strategy documents. Be thorough since missing a data type means missing a risk.

Step 2: Classify by Sensitivity

Assign each data type to a sensitivity level. Public data that anyone can access freely. Internal data that should stay within the company but is not confidential. Confidential data that would cause harm if exposed. Restricted data that has legal protection requirements such as personal data under GDPR.

Step 3: Create Rules Per Level

For each sensitivity level, define specific AI handling rules. Public data can be used with any approved AI tool. Internal data can be used only with enterprise AI tools. Confidential data can be used only with specific approved enterprise tools and only after removing identifying details. Restricted data must not be entered into any AI tool without explicit written approval and documented security measures.

Step 4: Create Quick Reference

Convert your rules into a one-page reference card with examples. List common data types and their classification. Use color coding for quick visual reference. Put this where people will see it: on desks, on the intranet, and in the AI tool's bookmarks.

Step 5: Train and Verify

Train your team on the procedures using real examples from your business. Test understanding with scenario questions. Verify compliance monthly through usage tracking and spot checks. Update procedures when new data types or AI tools are introduced.

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This article is for informational purposes only and does not constitute legal advice. Regulatory requirements change frequently — verify current rules with official sources. Built by Sawai Gyoseishoshi Office, Hiroshima, Japan.