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Answer CardVersion 2025-09-22

AI Assurance: Evidence, Controls, and Reviews

AI assuranceEvidenceControlsReviewsVerification

TL;DR

AI assurance demonstrates that AI systems meet defined objectives and manage risks. It relies on evidence: policies, risk decisions, test results, deployment approvals, monitoring, and incident/CAPA records. ISO 42001 provides management-system requirements; NIST AI RMF informs risk framing and measures.

Key Facts

Implementation Steps

Define evidence plan → evidence index.

Collect lifecycle artifacts → design docs, tests, approvals.

Monitor & log → metrics dashboard, audit logs.

Review & attest → review minutes, sign-offs.

CAPA → actions with owners and deadlines.

Glossary

Assurance
Confidence that systems operate as intended and meet requirements
Evidence
Documented proof that controls are operating effectively
Review
Systematic examination of processes, controls, and outcomes
Attestation
Formal declaration that requirements have been met
Metric
Quantifiable measure of system performance or risk
CAPA
Corrective and Preventive Actions - systematic approach to address issues

References

  1. [1] ISO 42001 AI Management Systems Standard https://www.iso.org/standard/78380.html
  2. [2] NIST AI Risk Management Framework https://www.nist.gov/itl/ai-risk-management-framework

Machine-readable Facts

[
  {
    "id": "f-evidence",
    "claim": "AI assurance depends on documented evidence across the lifecycle.",
    "source": "https://www.iso.org/standard/78380.html"
  },
  {
    "id": "f-reviews",
    "claim": "Periodic reviews evaluate control effectiveness and drive corrective actions.",
    "source": "https://www.iso.org/standard/78380.html"
  },
  {
    "id": "f-metrics",
    "claim": "Metrics support performance and risk monitoring in AI assurance.",
    "source": "https://www.nist.gov/itl/ai-risk-management-framework"
  }
]

About the Author

Spencer Brawner