Ungrounded AI Can Produce Unsupported Answers.

Faced with a gap, a language model may produce a statistically plausible continuation that reads like a real answer.

An ungrounded model may name a regulation that does not exist, cite an invented clause, or assert a requirement without support, using the same tone it uses when it is right. Without citations, retrieval evidence, or review, the answer may not reveal the error.

NIST names this risk in its AI Risk Management Framework Generative AI Profile. It defines confabulation as 'the production of confidently stated but erroneous or false content, known colloquially as hallucinations or fabrications, by which users may be misled or deceived.'

NIST lists confabulation among the risks organizations must manage. Its suggested actions include documenting model details, verifying provenance for training, testing, evaluation, validation, verification, fine-tuning, and retrieval-augmented generation data, and using transparency controls that support traceability. Organizations need to control and document what the model works from.

Give an AI access to curated, current, authoritative documents and its answers can trace back to those sources. Without relevant material, it relies on model memory that may be stale, generic, or wrong for your context.

Inputs are a major control. Two identical models can behave differently when one uses verified source material and the other relies on memory. The first can cite retrieved evidence; the second cannot.

Grounding requires allowed, current, permissioned, retrievable, cited, and reviewable sources. If any of those checks fails, the answer should report the gap.

The system must rank authoritative sources, preserve access rules, quote the passage behind each claim, and refuse when evidence is insufficient. ISO/IEC 42001 requires a documented process for recording the provenance of data used in AI systems. A grounded answer should be traceable.

Stanford benchmarked leading legal AI tools built around retrieval-augmented generation and found incorrect information in more than 17% of responses for Lexis+ AI and Ask Practical Law AI, and more than 34% for Westlaw's AI-Assisted Research. Retrieval can still select the wrong document, blend unrelated passages, or draw from a poor source set.

Curate and permission the source set, then trace every answer to the material it used so a human can verify it.

Sorena starts grounding with the inputs. Sorena Integrations connects trusted systems and documents so the AI can use verified material instead of relying only on model memory. You decide what it may read.

Those inputs feed Sorena SSOT, our Single Source of Truth, where the curated, permissioned source set lives. Answers draw from and trace back to that governed material.

Models will keep improving, and you should be able to adopt them without rebuilding source controls. Trust depends on curated inputs, enforced citations, and human review as well as model capability.

No model is trustworthy on its own for GRC work. The surrounding system must constrain what it reads and trace what it says.

An ungrounded model may guess without saying so. NIST calls this output confabulation.

Decide what the AI may read, curate those sources, and trace every answer back to them so a human can check the work.