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COMPEL Glossary / GL-72

Conformity Assessment

A formal evaluation that demonstrates an AI system meets the requirements of an applicable regulation, standard, or governance framework — for example the EU AI Act conformity assessment required for high-risk AI systems before market placement, or third-party certification against ISO/IEC 42001.

What this means in practice

Conformity assessments combine documented evidence (technical documentation, risk management records, data governance evidence, post-market monitoring plans) with internal review or independent audit, and produce an attestation, declaration, or certificate of conformity.

Context in the COMPEL framework

Conformity assessment activities span Model (designing the assessment approach and gathering evidence requirements), Produce (collecting and organizing evidence as a natural output of operations), and Evaluate (performing the assessment itself or supporting external auditors). COMPEL operationalizes conformity assessment by mapping mandatory artifacts to regulatory and standards requirements so that the documentation needed for assessment is generated as part of normal operations rather than as a separate workstream.

Where you see this

Conformity Assessment is most commonly referenced when teams work across the Model , Produce , Evaluate and Learn stages — especially within the Operational Readiness layer . It appears in governance artifacts, assessment instruments, and delivery playbooks wherever COMPEL is operationalized.

Related COMPEL stages

Related domains

Synonyms

conformity evaluation , compliance assessment , regulatory assessment

See also

  • Compliance Harmonization — The practice of implementing a single governance framework that satisfies multiple regulatory requirements simultaneously.
  • Evidence Pack — The complete, auditable collection of artifacts, test results, decision records, and attestations that demonstrate an AI system meets its governance, compliance, and operational requirements.
  • Governance Control — A defined mechanism — preventive, detective, or corrective — that enforces policy compliance, mitigates identified risks, or ensures operational integrity for AI systems.

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