AI hallucinations are often dismissed as a quality problem: the model gives a wrong answer, the user corrects it and life goes on. Recent publications from the American National Institute of Standards and Technology (NIST) and a series of incident reports from 2026 paint a different picture. They show that some AI risks, including hallucinations and related behaviour, can become more visible or consequential in production environments, where they can filter through into decision integrity and even into data leaks. To our analysis, this shifts the question from model quality to governance, monitoring and verification.
Hallucinations are a post-deployment problem
In the bulletin Challenges to the monitoring of deployed AI systems (NIST AI 800-4), NIST describes how deployed AI systems can behave differently during evaluation than in actual use. NIST frames deployed AI risks as post-deployment monitoring challenges, including security, human-factors and compliance issues. The report underlines that a model which appears reliable in the testing phase offers no assurance of its behaviour in practice. In our reading, organisations that deploy AI in situations where trust matters therefore benefit from active monitoring, anomaly detection and human oversight after going live.
That there is also movement on the technical side is evident from a second NIST publication, Hallucination Detection in Large Language Models Using Diversion Decoding. It presents a detection-oriented decoding method that can quantify and make visible hallucinations in language models. The publication presents a detection-oriented decoding method that can surface hallucinations and, in the evaluated setup, help reduce unsupported outputs. This is relevant because it suggests, to our analysis, that verification at the point of use — via multiple passes or multiple models — can support the reduction of hallucination risk. It remains a supporting measure, not a promise of correctness.
From wrong answer to data leak
The 2026 incidents make concrete why this matters. The Wharton Accountable AI Lab analysis discusses early-2026 AI exposure incidents involving chat systems and agents and draws governance lessons from them. The researchers place these incidents in the framework of decision integrity and urge logging, integrity checks and re-validation of output after an incident.
A telling example appears in the attack-path analysis by Foresiet covering six AI security incidents. It describes a case in which an internal AI agent within a large tech company hallucinated incorrect permission scopes. The agent issued incorrect instructions, which made sensitive internal data temporarily accessible to unauthorised employees. This shows that a hallucination does not always manifest as wrong text, but also as a faulty access instruction. The AI system thereby becomes itself a direct source of data leakage.
Mainstream tools are not immune either. Check Point Research reports a vulnerability in ChatGPT that was fixed on 20 February 2026 and could be used to leak user messages and uploaded files. The lesson Check Point draws is firm: organisations cannot simply assume that security on the vendor's side is sufficient. In our view, unexpected AI behaviour should be treated as a verifiable security and governance risk, not merely as a model glitch.
What this means for sensitive working environments
The common thread through these sources, to our analysis, is clear: anyone deploying AI in contexts involving confidential information cannot rely on a single model answer. NIST discusses monitoring and oversight, Wharton underscores logging and re-verification, and the incident reports describe what can happen when that control layer is missing. For professionals who work with sensitive data — lawyers, notaries, company doctors, journalists, researchers, compliance teams — this is no abstract discussion. A hallucinated claim or a faulty instruction can filter directly through into a piece of advice, a case file or a decision.
This is precisely the terrain where a verification console such as I am Vera fits in. Vera is not a chatbot and not a language model of its own, but a verification layer that makes the control steps around AI output visible. Instead of leaning on a single answer, Vera supports multi-model verification, in which answers from different models are placed side by side so that differences and weak points stand out. This does not promise correctness, but it gives the user greater insight into where an answer may possibly be incorrect.
In the area of data exposure, Vera follows an architecture that aligns with the concerns from the incident reports. With the Semantic Privacy Shield, pre-processing and anonymisation take place on EU infrastructure, and the workflow is designed so that only anonymised content is sent onward to the selected AI models. If the privacy check fails, nothing is forwarded. In addition, Vera Office makes it possible to view and edit documents within the same protected environment, without unnecessarily exposing the content to external systems.
The NIST publications and the 2026 incidents support a sober conclusion, to our analysis: AI output is best treated as something to be verified rather than blindly trusted. A verification chain can support that control by making steps visible and flagging risks. The professional final judgement, however, always remains with the user — and that, given what can go wrong in practice, is exactly as it should be.