Data privacy: Data privacy, in the context of AI, is the set of rules and practices that govern what information you put into an AI system, who can see it, how long the vendor keeps it, and whether it is used to train future models.
When you paste text into a chat assistant, it travels to the vendor's servers. What happens next depends on your plan and settings. Consumer tiers of the major assistants may use your conversations to improve their models unless you opt out in the data controls settings. Business, enterprise, and API tiers generally do not train on your data by default and offer retention controls. Check the vendor's current data policy for your specific plan; these terms change.
The bigger risk is often not training but exposure. Data you paste may be stored, reviewed for abuse, subpoenaed, or included in a breach. Shared links, browser extensions, and third-party plugins each add a path for data to leave. Regulated data adds legal weight: health information under HIPAA, financial data under GLBA, student records under FERPA, and personal data under state laws like California's CCPA and the EU's GDPR.
The working rule is simple. Never paste confidential data into a consumer AI tool unless your organization has approved that tool for that data. Strip names, account numbers, and identifiers when you can. Use the company-approved tier, and ask IT which one that is. When you build an automation, document what data flows through it and where it is stored.
Example at work
A bookkeeper wants help categorizing 300 bank transactions. Instead of pasting the raw export, she replaces the client's name and account number with placeholders, removes the memo lines that name individuals, and uses the firm's paid business account rather than her personal free login. The categorization works just as well.
Why it matters
A privacy mistake with an AI tool is usually invisible until it becomes a breach notification, a lost client, or a regulatory finding. A few habits, choosing the right tier, redacting identifiers, and knowing your company's policy, remove most of the risk without slowing you down.
Related terms
- Personally identifiable information (PII)Personally identifiable information (PII) is any data that can identify a specific person on its own or in combination with other data, such as a name, address, Social Security number, email, phone number, photo, or account ID.
- Workplace AI policyA workplace AI policy is a written set of rules that tells employees which AI tools they may use, what data they may put into them, how to verify and disclose AI-assisted work, and who to ask when unsure.
- Proprietary modelA proprietary model is an AI model whose weights are kept private by the company that built it. You use it through the company's app or API, and you cannot download, inspect, or self-host it.
- Open-weights modelAn open-weights model is an AI model whose trained parameters (weights) are published for anyone to download, run, and modify, usually under a license that sets limits on commercial use or redistribution.
- AI governanceAI governance is the set of policies, roles, controls, and oversight processes an organization uses to decide how AI is adopted, used, monitored, and held accountable across the business.