Tools like ChatGPT, Claude, or Grok are now part of daily work. Employees use them to draft reports, review code, or prepare presentations in minutes. The real question for a business owner is this: what happens when your team pastes sensitive company information into one of these systems?

The real risk

The LLM industry is moving fast. Regulation is unclear, technology changes week to week, and investors push for quick returns. In that race, data protection is often treated as secondary.

This matters because the risk is not abstract or historical. It comes from what your employees type into these tools today. Every prompt is stored as interaction data. Some providers reuse it to train their models unless you disable the option or use a specific plan. Most keep logs for a short time to monitor abuse or fix bugs. Those logs can also be extended if regulators or courts demand it.

For a leader, this means that sensitive text placed in a chatbot is not simply “used and forgotten.” It can live in logs you do not control, under rules you did not set.

When things went wrong

This is not just theory. Incidents in the last two years show that even the largest providers make mistakes.

  • Meta AI had a bug that let outsiders see other people’s prompts and answers.
  • DeepSeek left a database open with chats, API keys, and logs until researchers reported it.
  • Adult LLM apps leaked private, explicit conversations onto the open web because of poor setup.
  • Researchers built an attack called Imprompter that tricks an LLM into sending your replies to a malicious server.

These failures are not edge cases. They show that leaks and attacks are real, even inside major platforms. For leaders, the lesson is clear: you cannot outsource responsibility for data security. If a leak involves your company, the damage lands on you, not the vendor.

The legal side

Courts are already changing how long providers keep data. In New York Times v. OpenAI, a judge ordered the company to hold on to consumer chats as evidence. Accounts on specific plans were excluded, but most users were not.

This shows how fast rules can shift. A provider may promise short log retention, but a legal order can override that at any time.

Sam Altman, CEO of OpenAI, called the order a bad precedent and argued for an “AI privilege,” similar to the protections given to doctors or lawyers. But no such rule exists today. If data is in a chat log, regulators and courts can request it.

Beyond the courts, the design of these systems adds another risk. They are probabilistic and non-linear, so their answers are unpredictable. A chat may cause a person to share details they did not plan to disclose, like a password or an email address. Studies on prompt injection and persuasion show this effect is real, even without intent from the model. The danger is not only oversharing in the moment, but that the data can remain in records through court orders or surface in leaks.

For leaders, the point is clear: vendor promises are not enough. Court orders and unpredictable outputs can still put company data at risk.

Setting boundaries

Employees will often use these tools with the best intentions, but they should not have to guess what is safe to share. Leaders need to set clear boundaries. Define what must never go into a chatbot: client identifiers, financial records, trade secrets, or any data covered by regulation.

Make those rules practical. If staff need to use these tools for writing or analysis, allow them to work with redacted or anonymized text. Replacing names or numbers with placeholders reduces risk without blocking productivity.

Treat this as policy, not personal judgment. Remind teams that once text is entered, it may stay in logs, appear in leaks, or be requested by a court. Clear guidance protects both employees and the company.

Choosing the right setup

If your company must work with sensitive data, there are safer ways to use these systems. Leaders should evaluate:

  • Running a model locally, so data never leaves company systems
  • Approving enterprise plans that include clear privacy commitments
  • Using tools that allow masking or anonymization before text is processed
  • Enabling settings that turn off memory or opt out of training, but logs may still exist

The safest rule still applies: if losing the data would harm the company, do not enter the real thing.

Bottom line

LLMs are powerful but not private. Bugs and leaks have already exposed chats. Courts and regulators can demand records. Until the rules change, staff should treat every prompt as something that could be stored or revealed. If you would not want it read in public, do not type it into these systems.