AI Assurance
AI assurance is the structured use of evidence, testing, evaluation, documentation, governance, and ongoing monitoring to determine whether an AI system satisfies defined requirements for performance, safety, security, legality, accountability, fairness, and institutional use. Assurance supports justified confidence; it is not a blanket guarantee that an AI system is safe or correct.
AI assurance connects high-level principles with verifiable practice. It asks what claims are being made about a system, what evidence supports them, who performed the assessment, which use conditions were tested, what limitations remain, and how performance and risk will be monitored after deployment. The required level of assurance should reflect the consequence and sensitivity of the use case.
Foreign ministries may use AI in low-risk administrative work and in higher-consequence functions involving sensitive analysis, crisis information, public communication, or citizens. The same assurance threshold should not apply to every use. A risk-based assurance approach helps institutions decide whether a system is suitable for a specific task and whether safeguards, human review, restricted deployment, or rejection are required.
AI assurance appears in impact and risk assessments, model and system testing, red-teaming, security review, data-quality checks, procurement evidence, legal review, performance thresholds, human-factors evaluation, incident reporting, audit logs, post-deployment monitoring, and independent or internal assurance functions.
Closely related entries kept separate because each carries a distinct institutional meaning.
- Responsible AI
The design, deployment, and governance of artificial intelligence systems in ways that support safety, accountability, fairness, transparency, and human oversight.
- Auditability
Auditability is the degree to which a system, process, or decision can be independently examined and reconstructed using reliable records of inputs, outputs, actions, changes, identities, approvals, rules, and relevant context. Auditability enables accountability and learning, but it requires records that are complete, protected, interpretable, and accessible to authorized reviewers.
- AI Register
An AI register is a structured and maintained inventory of AI or algorithmic systems used, procured, developed, piloted, or authorized by an organization. It records information needed for governance, such as purpose, owner, provider, data, users, risk level, decision role, deployment status, safeguards, assurance evidence, review dates, and retirement or exit arrangements.