AI tools are becoming part of daily work: they help write texts, analyze information, prepare presentations, and answer questions. However, many users remain uncertain about which information is safe to send in a query and which is better kept private or anonymized.

There is no universal answer applicable to all situations. But there are practical principles that help make the right decision in each specific case.

Why This Is a Matter of Habit, Not Paranoia

Reasonable caution when working with AI tools is not a fear of technology but a professional approach. The same principles apply when using any external service: a search engine, cloud storage, corporate tools. It is always appropriate to ask yourself: “If this data ends up elsewhere, how critical would that be?”

This is especially important in a professional context: working with client data, internal company documents, medical or financial information requires heightened attention regardless of which tool you use.

Data Categories Requiring Special Attention

1. Personal Data of Third Parties

Names, surnames, addresses, phone numbers, email addresses, passport data, or any other information that uniquely identifies a specific person is a high-risk area.

If you are writing a query with real client or colleague data, ask yourself: can you replace them with anonymized equivalents (“client A”, “user B”, “city N”) without losing the meaning of the task? In most cases, the answer is yes. AI does not need real names to help with contract analysis or drafting a letter.

2. Data with Corporate Classification

Internal strategies, pre-publication financial results, negotiation positions, employee personal data—all of this typically falls under corporate confidentiality policies. Before sending such data to any external tool, including AI, it's worth checking your organization's policy.

Some companies allow the use of public AI tools only for tasks not involving confidential information. Others create internal solutions. Familiarize yourself with your organization's current rules—this is basic professional caution.

3. Medical and Financial Information

Diagnoses, test results, information about chronic diseases, bank account details, investment position details—these are categories that should not be included in queries in their original form.

If you need AI help with a medical text, use general phrasing not tied to a specific person. If you need help with financial analysis, work with anonymized figures or use conditional examples.

4. Credentials and Access Keys

Never include passwords, access keys, tokens, PIN codes, or any other credentials in queries—even “for example”. This rule has no exceptions, regardless of the platform.

If you need help with code that should use such data, replace real values with placeholders like YOUR_ACCESS_KEY or PASSWORD_HERE.

5. Information About Minors

Data related to children—photos, names, location, health or education information—requires special caution and is usually protected by specific legal regulations.

Practical Approach: The Necessity Test

Before sending any query containing sensitive information, ask yourself three questions:

Is this data really needed by the AI to complete the task?

Often specific details do not affect the quality of the response. If you need help with a contract structure, the type of contract and general situation matter, but party names and amounts do not.

Can real data be replaced with conditional data?

“Company A”, “employee B”, “amount X rubles”—in most cases, AI can handle the task using generalized designations.

If this information becomes visible to someone else, how critical is that?

This doesn't mean the information will necessarily leak—it's about the principle of minimization. Send only what is truly necessary for the task.

How to Structure Queries with These Principles

A good AI query is specific but not excessive. Here are a few practical examples:

Instead: “Help me write a letter to dismissed employee Ivan Petrov, passport series 1234 number 567890, reason for dismissal — systematic tardiness.”

Better: “Help me write an official letter to an employee about termination of employment due to violation of internal regulations. Tone — neutral, official.”

Instead: “Check my code with access key: sk-prod-123abcdef...”

Better: “Check this Python code — am I correctly using the environment variable for the access key? [code with placeholder os.getenv("ACCESS_KEY")]”

In both cases, the AI gets enough context to help, and unnecessary information is not transmitted.

About Neiron AI's Privacy Policy

If you have questions about how data in queries is handled on the Neiron AI platform, refer to the privacy policy at /privacy. It describes how the platform works with information from queries.

If you have specific questions or requirements, contact support for clarification.

Common Mistakes and How to Avoid Them

Mistake: copying entire correspondence

Often users want help with a letter or conflict situation and paste the entire correspondence with names, positions, and details. In most cases, it's enough to describe the situation in your own words, removing specific names.

Mistake: documents with automatic metadata

If you copy text from a corporate document, make sure there is no hidden data in the clipboard (author names, internal notes). It's better to copy only the needed text fragment.

Mistake: verifying document authenticity

Some users ask AI to “check” whether a document is fraudulent—and paste real passport data or financial information. AI tools are not suitable for such tasks, and the data is exposed to unnecessary risk.

Mistake: “it's anonymous anyway”

A combination of several anonymized data points (city, age, profession, diagnosis) can collectively identify a person. Be careful with combinations, even if each element seems harmless alone.

When Using AI Requires Extra Caution

Some professional contexts require additional checks even when following basic principles:

Legal services: client cases, court correspondence
Medicine: medical histories, records, test results
Financial services: client data, transaction information
HR work: personal data of candidates and employees

In these areas, check your organization's internal policies and, if necessary, seek legal advice on the permissibility of using external AI tools for specific tasks.

How to Explain This Rule to Colleagues

If AI is used in a team, the data rule should be clear to all participants. There's no need to create a complex legal document for each query. A short memo is enough: do not send personal data unnecessarily, remove payment details, replace real names and contracts with conditional designations, do not attach documents if the task can be solved with an anonymized fragment.

Such a memo helps keep work moving. A person sees not a ban on AI tools, but a clear procedure for preparing materials. For contentious situations, it's better to use /support or contact the responsible person within the team. If the material is for public publication, additionally check whether the text contains internal names, closed numbers, or details from which the original data can be reconstructed.

Summary

A reasonable approach to working with AI is to minimize the data transmitted to the necessary minimum. Anonymize personal data, replace real keys with placeholders, do not copy corporate documents in full unnecessarily. These habits do not limit the capabilities of AI tools—they make work professional and conscious.

How to Decide Which Data Not to Send in AI Queries