AI assurance is the use of independent evidence, testing, review, and governance to build justified confidence that an AI system meets defined requirements. It connects claims such as safe, fair, secure, or reliable to concrete evaluations and controls, while making limitations and residual risk visible to decision makers.
Assurance begins with a claim and the evidence needed to support it. AI agent evals test behavior, while AI governance assigns review and accountability. Assurance is confidence backed by evidence, not a promise of zero risk.
When does AI assurance matter?
Use it before procurement, deployment, and major changes. AI risk management decides which risks need treatment, and an AI safety case can organize the argument. Evidence must match the system’s real context of use.
Frequently asked questions
What is AI assurance used for?
Use it before procurement, deployment, and major changes. AI risk management decides which risks need treatment, and an AI safety case can organize the argument.
Is AI assurance a certification?
Not necessarily. Certification is one assurance mechanism; testing, audits, impact assessments, and safety cases are others.
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