Using AI is not wrong; the mistake lies in what you apply.
Data risks when using AI do not lie in the technology itself, but in the habits of the team and the absence of internal principles.
The most significant risk is personnel inputting sensitive data (contracts, client information, source code) into public AI tools, where data may be stored or used to train models. This is not a theoretical concern: there have been large companies that tightened internal AI usage rights after discovering personnel pasted source code into public tools. The solution is not to ban AI, but to manage it as a disciplined data channel, clearly classifying what can be inputted and selecting tools appropriate to the level of sensitivity.
- content that is publicly available and not sensitive
- the team already has clear internal principles regarding data
- enterprise tools with a commitment to security
- inputting client contract data into public tools
- industry with strict personal data constraints
- there are no internal principles for data classification
Data risks when using AI in business do not come from AI automatically pulling your data. The risk comes from the habits of the team: pasting, working quickly, finishing tasks, without further thought. This is a real gap, and it expands proportionally with the number of people in the company using AI without common principles.
The real gap lies in habitual sticking
The most common behavior leading to data leaks is personnel pasting work content into the chat frame of public AI tools. It could be a contract that needs a quick summary, a piece of source code that needs debugging, a customer list that needs sorting, or a partner email that needs a reply. Each action may seem harmless on its own, but the data has left the company's control the moment it is sent.
It is noteworthy that many businesses tightly control emails, internal documents, and file access, but completely leave the AI chat framework open. This is a blind spot for oversight: a new channel, no rules yet, everyone handles it as they see fit.
A trustworthy AI system must protect data privacy and maintain resilience against attacks throughout the entire lifecycle of the system.
NIST AI Risk Management Framework (AI RMF)
Distinguish between public and enterprise tools
Not every AI tool has the same level of commitment regarding data. The most important differences you need to understand:
- Free tools or personal packages: many providers clearly state in their terms that user data may be used to improve or train models. Sensitive data should not be entered here.
- Enterprise package with confidentiality commitment: usually includes a clause not to use user data for training, with a clear data processing contract. More suitable when internal content needs to be handled.
- The solution for on-premise or private cloud deployment: data remains within the enterprise environment. The investment is higher but offers complete control.
Choosing the right tool for the right type of data is a management decision, not merely a technical one.
When is it acceptable to use, and when should it be stopped?
Acceptable with public tools: Draft communication content without confidential information, conduct market research from public data, brainstorm content ideas, and edit the style of texts without customer identification information.
Only use tools with security commitments or private environments: Summarizing or drafting contracts, handling customer data with names and contact information, debugging internal source code, analyzing strategic documents, any content that belongs to the company's intellectual property.
Common errors to avoid
- Label the entire contract or quote in a public tool just for a quick summary.
- Use free accounts without reading the terms regarding how the input data will be processed.
- There is no internal principle allowing each member to decide what to input into AI or which tools to use.
- The misconception that input data will disappear after closing the chat window, while in reality, it is often saved by the provider.
- Thinking only the technical team is at risk, while business and operations personnel often handle more sensitive data.
The viewpoint of Sinh Vũ
With customer data, Sinh Vũ maintains a strict boundary: sensitive information does not go to public AI tools. Not because technology is bad, but because this is part of professional caution and reputation. Saving a few minutes of operation is not worth the risk to customer trust.
What Sinh Vũ recommends to your team is to start from a simple internal principle: categorize your data into two groups, sensitive and non-sensitive, and then decide which tools are allowed to access each group. No need for complexity. Clarity is essential.
Regarding specific security configurations and legal constraints by industry, especially for businesses handling personal data under legal regulations, you should have data security and legal experts involved when establishing an internal AI framework. This is something Sinh Vũ cannot replace, and should not replace.
Decision checklist
Topic: Data risks and security when using AI. Sinh Vũ guide, sinhvu.com
Select each item you find appropriate, then print or save as PDF to take with you.
If you have marked most of the signs above, this is the time to discuss in more detail. Sinh Vũ can help you review and propose a direction.
References
NIST AI Risk Management Framework (AI RMF); Press coverage on Samsung 2023 (Bloomberg, TechCrunch), summarized through AI security reports; GenAI Data Leakage: Employees Pasting Confidential Data into AI Tools (usecure). Insights on security configurations and specific legal constraints by industry are directional and require data security and legal experts to apply accurately.
Frequently asked questions
Does the free AI tool save the data I input?
It depends on the specific tools and packages used. Many free tools clearly state in their terms that data may be used to improve or train models. Paid packages for businesses often come with commitments not to use user data for training. You should read the data policy of the tool you are using before entering any customer or internal information.
If I only use AI to rewrite text, should I be concerned about security?
Rewriting standard text is low risk, but the issue lies in the content of that text. If you copy a section from a contract, quote, or customer letter to edit the wording, sensitive data has already been exposed, even if the purpose is just to refine the style. The safe habit is to separate the content that needs editing from identifying or confidential information before putting it into the tool.