How AI-mediated narratives are becoming healthcare's invisible threat

A ghost breach happens when artificial intelligence alters decisions, triggers crisis responses, or degrades clinical outcomes without any conventional security alert. This corruption can originate externally, through AI-mediated narratives that enter information systems as credible intelligence, or internally, through model failures that introduce fabricated data directly into clinical workflows. The data itself may never be "stolen," and no ransomware is deployed. What is compromised instead is the integrity of information at the point of interpretation.

Writing in CyberScoop, cybersecurity professionals Mary Catherine Sullivan and Brett Callow document three real-world incidents in which AI-generated narratives triggered full-scale crisis responses at organisations that had suffered no actual compromise. In one case, a language model fabricated a detailed and technically convincing breach story that was picked up by a journalist before the company could establish that nothing had happened. In another, AI-powered news aggregators misread a republished archive article as a developing story, causing a company to field inquiries about an incident resolved years earlier. In a third, a cybersecurity publication ran AI-generated quotes attributed to a named security researcher who had never spoken to them. Sullivan and Callow concluded that "Fiction becomes signal." That means that once a false narrative enters automated threat intelligence pipelines, it can drive internal investigations, executive escalation, and defensive actions all responding to something that never happened.

Why healthcare is exposed

In Fairness of Artificial Intelligence in Healthcare: Review and Recommendations, the authors describe automation bias which is the tendency for clinicians to over-rely on AI outputs, especially when those outputs are presented with the authority of a well-formatted system. Their research found that incorrect AI advice negatively affected clinical performance across all expertise levels, with less experienced practitioners most likely to follow erroneous AI suggestions without challenge. In the context of ghost breaches, a ghost breach narrative does not need to fool a machine, it needs to fool a human who is already disposed to trust the machine presenting it.

A 2024 scoping review published in Archives of Public Health found that a large majority of the studies it surveyed flagged unpredictable AI errors as a serious patient safety concern. The review noted that power failures, flawed algorithms, and malicious interference could all compromise AI performance in ways that are difficult to anticipate or detect. Furthermore, the scoping review observed that patients expressed a particular fear of being told "we do not know what went wrong" when AI tools produce adverse outcomes, a phrase that shows the accountability void that ghost breaches exploit.

Emerging research suggests that hallucination, the tendency of AI models to generate false or misleading information, represents an equally dangerous threat. A 2024 study covered by TechTarget's Framework to help detect healthcare AI hallucinations reported that researchers from the University of Massachusetts Amherst and healthcare AI company Mendel developed and tested a hallucination detection framework across 100 AI-generated medical summaries. Both GPT-4o and Llama 3 produced hallucinations spanning five categories: patient information, patient history, diagnosis and procedures, medication instructions, and follow-up care. Chronological inconsistencies and incorrect reasoning were also documented across both models. As Andrew McCallum, Ph.D., professor of computer science at the University of Massachusetts Amherst, stated, "Ensuring the accuracy of these models is paramount to preventing potential misdiagnoses and inappropriate treatments in healthcare." What makes these findings relevant to ghost breaches is that the hallucinations required no attacker. The harm potential was inherent to the models themselves.

A 2025 study published in Information Development adds a behavioural dimension to this picture. The authors examining AI hallucination exposure among health consumers in the context of generative AI adoption, found that the more confident users felt in their ability to understand and control AI tools, the more frequently they encountered hallucinations, not because confidence improved detection, but because it increased engagement and reduced scrutiny. Also, the researchers found that users with negative attitudes toward AI and high risk perception were actually less exposed to hallucinations, not because they were better at identifying errors, but because they avoided the tools altogether. The implication for ghost breaches is that users most likely to be harmed by manipulated AI outputs are not the sceptics, but the confident and the trusting. Lastly, the authors concluded that addressing the hallucination problem requires a multi-tiered response combining user education, built-in fact-checking tools, and closer collaboration between AI developers and healthcare professionals.

What the existing frameworks miss

Current healthcare cybersecurity frameworks were designed when the primary threat model involved data confidentiality and system availability. Sullivan and Callow frame this shift by noting that the field is moving from incident response to narrative response. Their reporting shows that security teams need to treat credible external claims as potentially fabricated, and that communications teams must be prepared for narratives that form independently of what actually happened. In healthcare, the consequences of a false narrative include delayed procedures, misdirected treatment, and undermined public trust.

In the 2025 study published in Information Development, the authors point out a structural gap, they state that existing liability frameworks offer no clear guidance on responsibility when AI tools cause harm, leaving clinicians, developers, and institutions uncertain. Ghost breaches make this uncertainty not merely a legal inconvenience but an active security weakness. When who has accountability is unclear, no single stakeholder has sufficient incentive to invest in monitoring ghost breaches. The scoping review states that the absence of clear responsibility is itself an exploitable condition.

The hallucination research adds to this governance problem. TechTarget reported that the UMass Amherst and Mendel team found human hallucination annotation to be time-consuming and expensive, and identified automated detection as a necessary development to make ongoing oversight viable at scale.

Building defences against breaches

Sullivan and Callow propose testing how AI systems describe your organisation, your security posture, and any alleged incidents. This is a kind of external narrative auditing, understanding what machines "believe" about you before that belief turns into threat intelligence feeds and automated workflows.

The 2025 study published in Information Development advocates for independent algorithm audits conducted by external experts, dedicated hospital departments for ongoing AI quality control, and the development of interpretable algorithms that make decision-making processes visible to the clinicians relying on them. Furthermore, the study proposes building digital health literacy among patients and the public, including fact-checking mechanisms within AI frameworks to reduce bias and improve reliability, and requiring developers to collaborate with healthcare professionals to validate outputs before and during use.

The hallucination detection framework developed by the UMass Amherst and Mendel team points toward a technical component of this defensive. TechTarget noted that Mendel's Hypercube system showed promise in automating the initial hallucination detection step ahead of human expert review, using medical knowledge bases, natural language processing and symbolic reasoning to consolidate and cross-reference patient data.

Lastly, the 2025 study published in Information Development calls for policymakers to develop standards governing AI deployment in healthcare, with transparency requirements that force developers to disclose methodologies and performance metrics.

FAQs

What role do AI vendors play in preventing ghost breaches?

Vendors can build detection and transparency mechanisms into their products.

What happens to public trust in healthcare if ghost breaches become widely known?

Erosion of trust in AI-assisted care could drive patients away from beneficial technologies.

How do ghost breaches interact with existing misinformation problems in healthcare?

Ghost breaches can amplify and lend technical credibility to health misinformation that is already circulating.