Data governance for the AI era
Governing
for Answers
Govern the answer, not just the dataset.
When AI turns data into fluent answers, every weakness in meaning, ownership and quality reaches people as something they believe. This paper proposes a five-pillar model that moves governance from the dataset to the answer, without abandoning the fundamentals that still do the heavy lifting.
The five pillars
From the dataset to the answer.
This framework turns the five pillars of Governing for Answers into practices you can adopt, assess and evidence. It is a working draft, published to be challenged.
PILLAR 01Meaning
Definitions AI can read
THE TESTCan your AI assistant tell a user which definition it used, and why?
PILLAR 02Scope
Govern what AI actually reads
THE TESTFor any AI answer, can you say which documents it drew on, who owns them, and why they were the right ones?
PILLAR 03Automation
Governance as code
THE TESTWhat share of your critical quality rules run without a person starting them?
PILLAR 04Accountability
An owner for every answer
THE TESTWhen an AI answer is wrong, does anyone know whose job it is to fix it?
PILLAR 05Assurance
Evidence as a by-product
THE TESTCould you produce evidence for a regulator in days rather than months?
Three ways in
ProposalThe case for governing the answerWhy governance must change, the answer supply chain, roles, roadmap, and where this might be wrong.
FrameworkPractices you can adopt and evidencePrinciples, practices, maturity levels, metrics, regulatory mapping and a 90-day checklist for each pillar.
ToolkitsBuild the materials, guidedTemplates for each pillar. Start with the S1 register: describe your AI use and a guided assistant builds it with you.