Glossary

Data governance

What is Data governance?

Data governance comprises the rules, roles and processes by which an organisation determines who may collect, use, change and delete which data. It covers responsibility for data quality, access rights, retention periods and compliance with data protection requirements. It matters especially for AI projects, because models and agents are only as reliable as the data they draw on.

How KI.ordo helps

KI.ordo supports data governance with roles (Viewer, Editor, Admin, Owner), a change log and data export.

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Topics

data management
compliance
access rights

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Related terms

Data protection regulations

Data protection regulations are legal rules governing the handling of personal data. In the EU the General Data Protection Regulation (GDPR) is the key framework; among other things it requires a legal basis for every processing activity, data minimisation, transparency towards data subjects and data processing agreements with service providers. When using AI, organisations need to clarify which data ends up in inputs, knowledge sources and logs, and where it is processed.

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Compliance reporting

Compliance reporting is the regular, traceable documentation of whether an organisation meets legal requirements, standards and internal policies. A typical report sets out the period and scope, the status of each requirement, deviations, and measures with owners and deadlines. It serves management, supervisory boards, auditors or authorities as evidence, for example on data protection or information security.

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Template

Data validation

Data validation checks whether data is complete, correctly formatted and plausible before it is processed further. Typical checks cover mandatory fields, the format of email addresses, postcodes or dates, and logical contradictions – such as a delivery date before the order date. This catches errors early instead of letting them carry over into reports, invoices or AI analyses.

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AI Transparency

AI transparency means disclosing when AI is involved, which data and sources its results are based on, and how decisions are reached. This includes labelling AI content, traceable source references, logs and documented changes. Transparency builds trust, makes troubleshooting easier and is partly required by law, for example by the EU AI Act's transparency obligations, under which people should be able to tell that they are interacting with an AI.

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