Concern 163 · Working draft · editorial review in progress
Trade-secret leakage through employee prompts
Trade-secret leakage through prompts happens when someone sends confidential business information to an AI service that is not approved to receive it.
Understanding the concern
A worker may paste source code, a customer list, or a product plan to get useful help. The data has then crossed an organizational boundary. What happens next depends on the service's terms, settings, retention, access controls, and the organization's agreement with it.
Sending a prompt does not automatically make its contents public or put them into training. The mistake is assuming a protection that has not been checked. Clear approved workflows are more useful than expecting every worker to interpret complex provider terms alone.
An example
An employee asks an outside consultant to improve a confidential proposal without checking whether the consultant is covered by the company's agreement.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
- Training
- The process of using examples to shape how an AI model behaves. Learning patterns from material does not establish that the material is true or fair.
- Prompt
- The instructions or information given to an AI to guide its response.
Evidence and its limits
The sources describe sensitive-information disclosure and data-governance risks. Actual provider use and retention require checking the specific agreement.
OWASP · Security guidance
LLM02:2025 Sensitive Information Disclosure ↗ (opens in a new tab)Describes exposure of personal and confidential information and defensive considerations; not proof of a particular incident.
NIST · AI 600-1 · 2024 · Official risk framework
Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile ↗ (opens in a new tab)Describes generative-AI risk categories and management actions; it does not measure the failure rate of every product.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
Which kinds of information may staff send to each tool, and where can they get a safe alternative?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Stewardship
Stewardship means caring for resources and systems that others depend on, including people who will use them later. It looks beyond the benefit of a quick launch.
Plan for maintenance, energy use, data protection, and eventual shutdown. Ask who will look after the tool when the original team moves on and how future users can avoid inheriting hidden costs.
Ask together: What are we responsible for caring for over time?
Prudence
Prudence means using careful judgment before and during action. It balances worthwhile opportunities with the need to avoid harm that could have been anticipated.
Start with a limited trial, set stopping points, and prefer choices that can be undone when uncertainty is high. Review what happens rather than assuming the original plan remains sound.
Ask together: What is a careful next step, and when should we stop or change course?
Accountability
Accountability means being answerable for decisions and taking responsibility for repair. It requires more than saying that the software made the choice.
Name the person or team responsible, keep useful records, and give them authority to correct problems. Make the route to a complaint clear to the people affected.
Ask together: Who will answer for the outcome and help put things right?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
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