Protecting customer data when using AI

Working with clients involves handling sensitive data right from the brief stage: names, contact phone numbers, contracts, unpublished strategies. When AI begins to participate in the workflow, an important question is which data is input into AI and which is not. The answer to that question determines your credibility with clients, as much as the quality of the product.

Data classification: the first step that cannot be overlooked.

Not every piece of data requires the same level of protection. It is necessary to clearly distinguish four layers:

• Public data: information that has been published externally, with low risk when inputting into AI.
• Internal data: processes, templates, internal documents that have not been released. Exercise more caution.
• Confidential data: contracts, strategies, financial reports. Do not input into public AI.
• Personal information (PII, meaning personally identifiable information): name, phone number, email, address, customer payment data. Highest level of protection, requiring clear legal basis each time it is processed.

The most common mistake is treating all data as a single block, then making decisions based on convenience rather than risk levels.

Choose the right tool for the type of work.

Before using any tool, three things need to be checked. Does this tool have a DPA, can the input data be used for training, and who has access to that data?

Minimization: core principle

Data minimization means only inputting into AI what is necessary for a specific task, not providing the entire record "just in case." This principle is part of international data protection standards and is also a principle of the Personal Data Protection Law 2025 (data should only be processed within necessary scope and purpose), and there is a clear practical reason: data that is not input cannot be leaked.

• Before inputting data into AI, ask: what data fields does this task really need?
• Blur or replace names, phone numbers, and addresses before submission if the task does not require identifiable information.
• Schedule deletion: delete data that is no longer necessary, do not keep it "just in case."

Don't process personal data just because it might be useful later. Each processing needs a legal basis.

ICO, Data minimisation (AI and data protection toolkit)

Common mistakes and how to recognize them

• Directly inputting client lists into free AI: a familiar action but carries the risk of data exposure. It’s not just because AI "repeats" immediately, but because there is no control over where that data goes afterward.
• Using personal AI accounts for company work: this account is outside the organization's control. No one can check or revoke access when an employee leaves.
• Thinking anonymization is enough: many pieces of data combined can still identify individuals even if each piece has had names removed. Anonymization needs to be accompanied by minimization.
• Unaware of the data policy of the tools being used: this is the most common gap. Using without reading the terms means accepting conditions without knowing what you are agreeing to.

Internal policy minimization

Data discipline is a condition of credibility, not an additional burden. Customers trust you with sensitive information because they believe in how you operate, not because they are sure you won't be careless.

A streamlined practice: confidential data and personal information of clients should not go into public AI tools. When AI support is needed, prioritize environments that commit to data protection and only input the minimum necessary, which has been obscured. Any decision involving personal data should have someone approve it. When it exceeds limits, refer to security or legal experts instead of handling it yourself.

You don’t need to be a technical expert to practice this. Just have a clear internal policy: which data is allowed into which AI, who is allowed to use it, and when in doubt, ask before proceeding.

This article is for reference. The scope, pricing, and specific commitments of Sinh Vũ are detailed in the proposal and signed contract.