IBM’s five AI risks show where the office is losing control

IBM Technology published the video “Five AI Risks That Can Get You Fired—And How to Avoid Them” on May 24, 2026, with Martin Keen walking through five common ways workplace AI use can turn into a career problem. This is not a research paper or a technical benchmark. It is a short, branded governance lesson. Still, the subject is concrete enough that it should not be dismissed as routine vendor content.

The first risk is shadow AI: using unapproved tools outside IT, legal, and security controls. In practice, that can mean pasting a contract draft, internal code, a customer spreadsheet, or a strategy memo into a tool the company has not reviewed. The core issue is not simply “AI.” It is the loss of control over where data goes, how long it is retained, and who can access it. That is why IBM’s video points naturally toward AI governance, not as abstract ethics language, but as a working layer of rules.

The second risk is data leakage. Generative AI rewards speed: paste text, ask for a summary, get an answer. That is exactly where the expensive mistake happens. Sensitive data does not need to be hacked to leak; someone only has to place it in the wrong system. In an organization that wants AI without self-inflicted damage, employees need to understand the difference between public material, internal documents, confidential records, and regulated data.

The third risk is hallucination. A model can sound confident while being wrong, which is especially dangerous in legal, financial, medical, HR, or technical decisions. AI output should not be treated as the final authority. It should be treated as a draft that still needs source checks, number checks, and claim checks. IBM’s message lines up with broader institutional guidance such as the NIST AI Risk Management Framework, which frames AI risk as something managed across the system lifecycle.

The fourth risk is prompt injection: an attack or manipulation where a model receives hidden or malicious instructions through the content it is asked to process. If an AI tool reads emails, documents, web pages, or support tickets, the input itself can try to alter the model’s behavior. This is no longer a lab curiosity; OWASP describes prompt injection as a real security pattern for applications connected to language models.

The fifth risk is unauthorized AI agents. An agent that sends messages, retrieves data, changes records, or triggers actions on its own carries more risk than a simple chat window. Without clear permissions, logs, boundaries, and human review, a mistake can propagate faster than a team can contain it. IBM’s video is best read as an operational reminder: AI at work is not only a productivity question. It is a question of traceability, accountability, and control over automation.