Generative AI: 5 Real Incidents Every Board Should Be Aware Of

Generative AI is rapidly entering enterprise workflows. While the opportunity

is enormous, recent real-world incidents highlight the governance risks

boards should consider.

Here are five examples that illustrate why AI oversight is becoming a

board-level issue.

1. Legal Liability from AI-Generated Information

In 2023, attorneys submitted a court filing containing non-existent cases

generated by AI. The court sanctioned the lawyers, reinforcing that

organizations remain accountable for AI-generated outputs.

2. Confidential Data Exposure

Engineers at Samsung inadvertently uploaded sensitive semiconductor source

code to a public AI system while troubleshooting technical issues. The

company subsequently restricted internal use of public GenAI tools.

Board implication: clear policies for handling sensitive data with AI are

essential.

3. Reputational Damage from Uncontrolled AI Systems

Microsoft's AI chatbot Tay was taken offline within hours of launch after

users manipulated it into producing offensive content.

Board implication: AI systems interacting with the public require strong

governance and safeguards.

4. Corporate Accountability for AI Decisions

A customer relied on incorrect information provided by an airline's chatbot

regarding fare policies. A tribunal ruled that the company was responsible for

the chatbot's advice.

Board implication: AI does not reduce corporate accountability.

5. AI-Enabled Misinformation and Deepfakes

In 2024, an AI-generated robocall imitating a presidential candidate's voice

attempted to discourage voters from participating in a primary election.

Board implication: synthetic media risks are becoming a societal and

regulatory concern.

Board Takeaway

Generative AI is not just a technology issue — it is a governance, risk, and

compliance issue. Boards should ensure that management has in place:

responsible AI governance frameworks, data protection and privacy safeguards,

human oversight for high-impact AI decisions, and continuous monitoring and

risk assessment.

Organizations that balance innovation with strong AI governance will be best

positioned to capture value while protecting trust.