4.1.3. Identifying Legal and Business Risks of Generative AI

First Principle: The use of generative AI introduces novel legal and business risks, including intellectual property infringement and the erosion of customer trust, which must be proactively managed.

Beyond technical failures, the business impact of irresponsible AI can be severe.

Key Risks:

• Intellectual Property (IP) Infringement: A model trained on copyrighted data might generate output that is substantially similar to its training data, creating a risk of copyright violation. Using generated content without understanding its origin is a significant legal risk.
• Biased Model Outputs: If a model produces biased or discriminatory content, it can lead to legal action, brand damage, and regulatory penalties.
• Loss of Customer Trust: If an AI system consistently provides inaccurate information (hallucinations), is biased, or violates user privacy, customers will lose trust in the company and its products. This is often the most damaging long-term consequence.
• End User Risk & Hallucinations: Providing false information can have serious real-world consequences. For example, a hallucinated medical or financial recommendation could cause direct harm to an end user.
• Data Privacy Violations: If a model is trained on private data and inadvertently leaks that information in its responses, it can cause a major data breach.

Scenario: A marketing team uses a generative AI image tool to create a logo for a new product. They don't check if the output resembles any existing copyrighted logos.

Reflection Question: What is the primary legal risk the team is taking? How does this underscore the importance of human review and due diligence when using generative AI for commercial purposes?

💡 Tip: The core business principle is accountability. Your organization is ultimately responsible for the output of the AI systems it deploys, regardless of how that output was generated.