Secrets, customer commitments, shadow AI and rights over content

Lesson objective

By the end of this lesson, you will be able to spot, in an everyday AI use, what touches on trade secrets or a confidentiality commitment, propose a response to shadow AI that goes beyond a ban, and say what you can claim over content produced with AI.

Where it fits

Protecting the company

How do I avoid an AI use that discloses a secret, breaches a customer commitment or creates a rights problem?

Lessons in this module

1. Secrets, customer commitments, shadow AI and rights over content (this lesson)

What you will learn in the course

This lesson is part of the course Use AI at work without exposing your data or your company

• Sort the information in your work into green, amber or red based on its nature (public, internal, personal, sensitive, trade secrets, contractual commitments) and the tool considered.
• Check and configure, for each tool and plan, what happens to your inputs (training, retention, human review, business terms), based on the vendors' dated pages.
• Apply GDPR basics to the use of an AI tool (personal data, purpose, minimisation, sensitive data, processor, transfers) and prepare data by distinguishing pseudonymisation from anonymisation.
• Protect the company in everyday AI use (trade secrets, contractual confidentiality, shadow AI, rights over generated content and third-party content).
• Decide when to disclose the use of AI and who approves an output, placing the AI Act obligations in context (Article 50 transparency, Article 4 AI literacy).
• Write a team AI policy (green, amber, red data, tools and settings, transparency, human approval, incidents, review) and organise its adoption.

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