Data Quality Intelligence
Most discussions of AI governance focus on the model. Which model was used, how it was trained, what its limitations are. That conversation matters, but it misses the more practical question that organisations actually face in production: are we using AI in a way we can govern, evidence and defend? That question is not answered at the model. It is answered at the data and at the point of use.
Model governance is the part of AI governance that gets the most attention because it is the part most clearly owned by AI specialists. But the operational risk in AI deployment usually does not come from the model itself. It comes from the data the model consumes, the way users interact with the model, the policies that are supposed to constrain that interaction, and the evidence, or lack of evidence, that those policies were applied.
AI systems amplify their inputs. If the data is incomplete, inconsistent, duplicated or out of date, the output will be unreliable in ways that are often hard to detect from the output itself. Worse, AI tends to produce confident-sounding answers regardless of whether the underlying data supports them, which makes data quality problems particularly dangerous in AI workflows.
It is tempting to push governance into the prompt: tell the model what it can and cannot do, and rely on that instruction. This approach has two structural problems. First, prompt-level controls are owned by the prompt author and can be bypassed, ignored or simply written badly. Second, prompt controls cannot fix problems that originate in the data: a clean prompt over dirty data still produces dirty output.
In practice, three things have to be true for AI to be governed in production. The data flowing into AI has to be trusted, with quality issues remediated before they reach the model. The interactions with AI have to be controlled, with policies applied to prompts and outputs and human review used where required. And every part of that chain has to produce evidence: a structured, queryable record of what happened, what policy was applied, and how exceptions were resolved.
DQI Assess measures where the gaps are: data quality, governance maturity, AI readiness and policy coverage. DQI Integrate is being developed to close the data side of the gap by preparing, validating and remediating data before it reaches AI workflows. DQI Enforce is being developed to close the policy side of the gap by acting as a governance proxy on AI-bound traffic and producing an audit trail.