How Enterprises Control AI Hallucinations in Data Entry

In 2024 the NIST Generative AI Profile defined confabulation as the production of confidently stated but erroneous or false content. The profile identifies confabulation as a key technical risk and associates it with actions such as reviewing and verifying sources and citations in generative AI outputs.

That distinction matters when a single wrong invoice total can trigger financial exposure. Instead of chasing a perfect model, enterprises design workflows that detect, contain, and correct errors before data reaches systems of record.

Key Points

NIST identifies AI confabulation as a key risk and links it to controls such as source verification and monitoring.
Grounded extraction links every field to the document region that produced it, supporting traceability and audit.
Confidence scores trigger human review when reliability drops below set levels in high-stakes workflows.
Structured outputs constrain models to JSON schemas, reducing format errors before data reaches core systems.
ABBYY reports that its pre-trained document models can achieve straight-through processing rates above 90 percent in some workflows, while NIST separately emphasizes ongoing monitoring.

From Generation to Verifiable Extraction

High-stakes pipelines often avoid open-ended prompts and focus on extracting predefined fields such as invoice number, amount, or due date. In grounded designs, each value is linked to its exact location in the source file.

This pattern is described as grounding by Microsoft in its document analysis guidance.

Grounding allows reviewers and auditors to inspect a cited region in the document rather than accept an opaque assertion. The system should be able to show where a figure originated and why the extraction logic accepted it for posting into downstream applications.

This traceability also shortens dispute resolution, because reviewers can jump directly to the highlighted region instead of scanning an entire contract or invoice. It supports both quality assurance and regulatory or internal audit requirements.

Confidence as a Control Gate

Many extraction systems associate each extracted value with a confidence score, often on a 0 to 1 or 0 to 100 scale, that reflects model certainty. Organizations set thresholds based on risk tolerance.

For example, guidance from AWS recommends higher confidence thresholds, around 90 percent or above, for business processes involving financial decisions. It allows lower thresholds for less sensitive uses such as archival transcription.

According to AWS documentation for Textract, workflows can discard or flag results below the chosen threshold, routing the affected content to a human reviewer when necessary. This approach makes the acceptable error rate an explicit design parameter rather than an assumption baked into the model.

Box applies a similar pattern in its extraction products. As described by Box, scores of 0.90 and above are suggested as high confidence that typically need minimal review.

Scores between 0.70 and 0.89 are treated as medium confidence that may warrant light review. Scores below 0.70 are treated as low confidence where manual review is recommended.

Constraining Output and Validating Logic

Format errors can break downstream systems even when facts are correct. Structured outputs on Amazon Bedrock constrain model responses to follow user-defined JSON schemas and tool definitions.

This approach helps prevent stray fields, type mismatches, and other parsing failures before data enters production workflows.

After extraction, deterministic rules compare totals against line items, check vendor identifiers against master data, and ensure dates fall within allowed policy windows. These checks are typically implemented as validation logic that either clears a document for straight-through posting or routes it to an exception queue.

Document-processing vendor ABBYY reports that its pre-trained models can achieve over 90 percent straight-through processing (STP) in some deployments. Built-in normalization and validation rules perform cross-checks, sum checks, and vendor matching before data is exported to downstream systems.

A Lifecycle of Vigilance

The NIST Generative AI Profile stresses that monitoring must continue after deployment. Measures include reviewing and verifying sources in outputs and tracking system performance against defined metrics.

It associates confabulation risk with ongoing monitoring activities and incident reporting rather than treating it as a one-time testing concern.

Feedback from reviewer corrections can support iterative improvements in extraction quality and threshold settings. In grounded workflows, provenance information links the source region, the extracted value, and the associated confidence score.

This allows teams to analyze systematic errors and adjust prompts, schemas, or validation rules.

By combining grounding, confidence gating, structured output, and iterative feedback, enterprises can manage hallucination risk in data entry as a measurable quality parameter. Hallucinations become subject to explicit controls, thresholds, and monitoring rather than remaining an unpredictable side effect of model behavior.