5 AI Risks That Can Get You Fired

25 May 2026
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In the rapidly evolving world of artificial intelligence, staying ahead of the curve is crucial, but so is understanding the potential pitfalls. Martin Keen, a Master Inventor at IBM, outlines five critical AI risks that could lead to job termination. From ‘shadow AI’ to ‘zombie AI agents’, Keen provides a clear and concise overview of how employees can inadvertently jeopardize their careers by mismanaging their use of AI tools.

Visual TL;DR. Unapproved AI Use leads to Shadow AI. Shadow AI causes Data Leakage. Data Leakage can result in Job Termination. Hallucination Laundering can result in Job Termination. Prompt Injection can result in Job Termination. AI Agents Risks can result in Job Termination. AI Governance mitigates Unapproved AI Use. AI Governance mitigates Data Leakage. AI Governance mitigates Hallucination Laundering. AI Governance mitigates Prompt Injection. AI Governance mitigates AI Agents Risks.

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Unapproved AI Use: employees using AI tools without company IT approval
Shadow AI: unapproved AI tools create security vulnerabilities
Data Leakage: sensitive company data fed into third-party AI models
Hallucination Laundering: AI outputs presented as fact without verification
Prompt Injection: malicious inputs manipulate AI agent behavior
AI Agents Risks: autonomous AI agents can cause unintended consequences
Job Termination: mismanaging AI use leads to career jeopardy
AI Governance: essential for managing AI risks and compliance

Visual TL;DRQuickExplainDeeper

The Risks of Unapproved AI Use

Keen highlights ‘Shadow AI’ as the first major risk. This refers to employees using AI tools for work without the knowledge or approval of their company’s IT department. This practice can lead to significant security vulnerabilities, including data leakage. He cites an IBM report indicating that 20% of organizations have experienced a data breach caused by unapproved AI use. These breaches can occur when sensitive company data, such as proprietary code or customer records, is fed into AI models hosted on third-party servers, potentially exposing it to unintended access or misuse.

Data Leakage and AI Governance

The second risk, ‘Data Leakage’, is a direct consequence of Shadow AI. When employees input confidential information into unvetted AI tools, this data can become part of the AI’s training set or be stored insecurely. This means that the data, which should remain private, could inadvertently be revealed in future AI outputs or accessed by unauthorized parties. Keen stresses the importance of robust AI governance policies, which dictate which AI tools are approved, how they can be used, and what data is permissible to input. Without such governance, companies face significant risks to their intellectual property and customer data.

The full discussion can be found on IBM‘s YouTube channel.

Five AI Risks That Can Get You Fired—And How to Avoid Them — from IBM

‘Hallucination Laundering’ and Prompt Injection

Keen then delves into ‘Hallucination Laundering’, the third risk. This occurs when an AI generates plausible-sounding but factually incorrect information, and an employee then presents this AI-generated content as their own work without verification. This can lead to flawed decision-making and reputational damage for the company. The fourth risk, ‘Prompt Injection’, is described as a malicious technique where an attacker crafts an input that tricks an AI into bypassing its original safety instructions. This can lead to the AI revealing sensitive information or performing unintended actions. Keen notes that while newer AI models are more resistant, this remains a significant concern, especially when AI agents interact with internal systems.

AI Agents and Their Risks

The fifth risk discussed is ‘Unauthorized Agentic AI’. This involves employees deploying AI agents that can autonomously interact with company systems, databases, or APIs without proper oversight. These agents, if not properly managed, can perform actions that violate company policy or security protocols, such as deleting data or sending unauthorized communications. Keen emphasizes that the lack of visibility and control over these agents makes them a serious risk, as their actions are not monitored and can have unintended consequences. He draws a parallel to ‘zombie AI agents’ that might continue to operate with outdated or compromised credentials, posing a persistent threat.

The Importance of AI Governance

Keen concludes by underscoring the critical need for comprehensive AI governance. He states that simply banning AI tools is not a viable long-term strategy. Instead, organizations must establish clear policies for AI usage, including vetting processes for new tools, guidelines on data handling, and protocols for monitoring AI agent activities. By implementing strong AI governance, companies can mitigate the risks associated with Shadow AI, data leakage, hallucinations, prompt injection, and unauthorized AI agents, ensuring that employees can leverage AI’s benefits safely and effectively.

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