Guide
AI Governance and Semantic Alignment
AI governance is no longer an aspiration. The EU AI Act, ISO 42001, NIST AI RMF and DORA all demand traceability, accountability and oversight for high-stakes AI. But every governance framework contains a quiet assumption: that the business terms the AI reasons over are well-defined and consistent across the organization. They almost never are. That gap is what semantic alignment closes.
The two layers of AI governance
Modern AI governance is built on two layers. The first is model governance: model risk, bias, performance, explainability, lineage. The second, much less mature layer is semantic governance: the meaning the model acts on.
A perfectly governed model that operates on inconsistent definitions of policyholder, incident or vulnerable customerproduces unreliable, non-reproducible decisions — and no model governance suite will catch it, because the model is doing what it was told. Read more about the underlying concept on /semantic-governance.
What semantic alignment looks like in practice
- Every critical business term has one canonical, owner-accountable definition.
- Every AI agent resolves a term via the same governed API before reasoning over it.
- Every change to a definition is versioned and produces an audit event.
- Every drift, contradiction or override is captured as a structured governance decision, not a Slack message.
Where existing AI governance suites stop
Model risk platforms cover the model. Data catalogs cover the columns. Wikis cover the prose. None of them close the loop: none provides a single, machine-readable definition that an AI agent can resolve against at runtime, with an audit trail of who approved it. That is the role of the semantic governance layer.
Evidence required by EU AI Act and ISO 42001
EU AI Act Art. 9–15 requires risk management, data governance and traceability. ISO 42001 §6–8 requires planning, support and operation evidence. In both, a governed registry of business terms — with owners, versions, status and history — is the cheapest path to evidence. WikiSure exports a signed Semantic Governance Report suitable for both frameworks.
See it in production
WikiSure runs governed alignment on 120 imported insurance concepts. See the validation snapshot or browse the public definitions registry.
Frequently asked
What is AI governance?
AI governance is the set of policies, processes and controls that make AI systems safe, accountable and auditable. It covers model risk, data lineage, transparency, oversight and — increasingly — semantic alignment between the AI and the business it serves.
What is semantic alignment in the context of AI?
Semantic alignment means the AI agent interprets every critical business term in the same approved, versioned way as the humans and systems it collaborates with. Without semantic alignment an AI agent can be technically correct and still produce a wrong decision because it used the wrong meaning of 'customer' or 'incident'.
Is semantic alignment required by the EU AI Act or ISO 42001?
Both frameworks require traceable, well-documented inputs and definitions for high-risk AI. EU AI Act Art. 9–15 specifically mandates risk management, data governance and traceability — none of which can be evidenced without a governed semantic layer.
How does semantic alignment differ from prompt engineering?
Prompt engineering tells the AI how to behave in a single interaction. Semantic alignment ensures the underlying meanings the AI reasons over are consistent across every interaction, every agent and every system. It is infrastructure, not instruction.
What does a governed semantic layer add to an existing AI stack?
It gives every agent one resolvable source of truth for business terms, an audit trail of every meaning change, accountable owners per definition, and a contract the AI must respect before reasoning. The AI stops inventing meanings.
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