When Your Governance Tool Causes the Hallucination

Enterprise AI governance is supposed to prevent failures. Block sensitive data from reaching the model. Redact personally identifiable information before it enters the context window. Strip anything that looks like it violates a policy. The logic is intuitive: if the model never sees the dangerous content, the dangerous output never happens.

The logic is also wrong.

A growing body of research shows that blanket filtering, syntactic redaction, and coarse-grained governance controls do not simply reduce risk. They reshape the information landscape the model operates on, creating gaps, distortions, and absences that the model then fills with confident, fluent, and often wrong completions. The governance tool becomes the proximate cause of the hallucination.

The Mechanism: What Happens When You Remove Fragments

Large language models are probabilistic sequence predictors. They generate the next token based on everything in their context window. When a governance layer silently removes fragments from that window, the model has no signal that anything is missing. It does not pause. It does not flag uncertainty. It continues generating text as though the context it sees is complete.

The DataFilter study on prompt-injection defense documented this phenomenon directly. Early versions of the filter deleted suspected injections from the input, and the model responded by hallucinating completions for the missing content rather than faithfully working with what remained. Simple syntactic rules, such as "delete all imperative sentences," removed benign, task-relevant instructions alongside the threats. The filter corrupted the very context the model needed to respond accurately.

This is not an edge case. It is the default behavior. Security taxonomies for LLM agents, including OWASP's GenAI prompt injection guidance, emphasize that controls which silently alter or truncate prompts cause models to produce authoritative yet incorrect outputs. The model treats the altered prompt as ground truth because it has no channel to know data was blocked.

The Scale of the Problem

The over-filtering problem is not subtle. Amazon's FalseReject study found that state-of-the-art LLMs decline to answer 25 to 50 percent of safe prompts due to over-cautious safety training and filters. These are not borderline cases. They are legitimate, benign requests that the model refuses or mishandles because naïve rules cannot distinguish dangerous content from content that merely resembles it.

Scale that pattern to an enterprise context window assembled from dozens of fragments across multiple systems. A governance tool applies a blanket rule: redact all content mentioning a specific customer name, or block everything below a classification confidence threshold, or strip any fragment that references a regulated data category. Some of those fragments contained the precise information the model needed to answer correctly. The model does not know they existed. It fills the void.

The result is a hallucination that looks more authoritative than a typical confabulation, because it is surrounded by real, unredacted content. The user sees a coherent answer built on genuine fragments, with fabricated material woven seamlessly into the gaps. This is harder to detect than a hallucination produced from a completely empty context, because most of the answer is correct.

The False Trade-off Between Safety and Accuracy

Research on fairness, privacy, and utility trade-offs in language models consistently finds that generic privacy-enhancing transformations reduce performance on downstream tasks when they do not distinguish sensitive from non-sensitive tokens, or relevant from irrelevant content. One systematic study varied privacy and debiasing techniques and found that improvements along safety dimensions came with measurable drops in language modeling quality and downstream task accuracy.

But the trade-off is not inherent. It is a design failure.

More recent work on parameter-efficient fine-tuning shows that carefully designed methods targeting memorization of specific sensitive tokens can maintain or improve utility while mitigating privacy risk. The PRvL study on LLM-based redaction evaluated different strategies and found that methods reasoning about underlying semantics preserved more task-relevant content at similar or better privacy levels than naïve masking. The trade-off disappears when the filtering is context-aware.

LogSieve, a task-aware log reduction system, demonstrated that semantics-preserving filtering maintained average cosine similarity of 0.93 and GPTScore of 0.93 compared to full, unfiltered context. Entropic Context Shaping achieved a 71.83 percent relative improvement in F1 score over TF-IDF-based filtering by selecting context turns based on their measured information-theoretic utility rather than surface-level keyword matching. Both systems show the same conclusion: meaning-aware filtering preserves answer quality in ways that naïve filtering cannot.

The Compounding Problem: Authorized Junk

Over-blocking is only half of the failure mode. The other half is under-blocking.

Coarse governance rules evaluate fragments on a single dimension: is this user authorized to see this content? A fragment that passes the authorization check enters the context window regardless of whether it is relevant, current, or useful. This creates what amounts to authorized noise: content that is permissioned but meaningless for the task at hand.

An AI system filling its context window with authorized but irrelevant fragments produces worse answers than a system with a smaller, curated context. Boilerplate paragraphs, outdated templates, duplicated content from fourteen different sources, compliance language copied across hundreds of documents. All of it authorized. None of it helpful. All of it competing for the model's attention with the fragments that actually matter.

The ML-Assisted Fine-Grained Access Control research makes this point from the opposite direction. By implementing selective protection of sensitive regions rather than blanket redaction, and by using surrounding context to distinguish a generic date from a birthdate or a common name from a customer identifier, the system keeps non-sensitive content intact and usable. The contrast with naïve file-level or blanket redaction is stark: semantic-aware protection preserves the information the model needs while restricting only what genuinely requires restriction.

The Cure Creates the Disease

The pattern is consistent across the research. Governance tools designed to make AI safer instead create two simultaneous failure modes:

Over-blocking removes fragments the model needs. The model fills the gaps with hallucinated content that looks credible because it sits alongside real data. Users cannot tell which parts of the answer are grounded and which are fabricated, because the governance layer did its work invisibly.

Under-blocking admits fragments the model does not need. Authorized junk dilutes the context window, pushing the model's attention toward irrelevant content and away from the fragments that would have produced a correct answer.

Both failures stem from the same root cause: governance rules that operate at the wrong level of granularity. Document-level blocking, pattern-based redaction, and binary allow-or-deny decisions cannot handle a context window assembled from fragments that each require different treatment. One fragment in a context window may be sensitive, stale, and irrelevant. The fragment next to it may be current, authoritative, and essential. A blanket rule treats them identically.

What Precision Control Requires

The alternative is governance that operates at the fragment level, evaluating each piece of content on multiple dimensions before it enters the context window.

Is this fragment current, or has the source been superseded? Where did it originate, and is that source authoritative for this question? Is this user authorized to see this content for this specific purpose? Does this fragment conflict with other fragments already in the context window? And critically: is this fragment relevant enough to earn its place in a finite context window, or is it authorized noise?

These are not questions a blanket filter can answer. They require identity at the content level, not the document level. They require lineage that tracks fragments across systems, not access control lists that operate on file paths. They require continuous evaluation, because the answer to "is this current?" changes every time the source updates.

The research is clear that this is achievable. Context-aware filtering preserves utility. Semantic-aware redaction maintains privacy without destroying meaning. The technology exists to build governance that makes AI both safer and more accurate, rather than forcing a false choice between the two.

The question is whether enterprise governance architectures will evolve to operate at the level where AI actually works, or whether they will continue applying document-era rules to a fragment-era problem, creating the very failures they were designed to prevent.