GCRI · Engineering
Spatial intelligenceObservations, models and their uncertainty in one view
AI evaluation and assurance engineering
AI evaluation and assurance engineering develops methods to test, monitor and provide justified confidence in AI systems. It addresses performance, robustness, safety and societal impact across the system life cycle. Artificial Intelligence and Autonomous Systems stewards this page for the Nexus Consortium.
AI evaluation and assurance engineering in six parts.
- 01The methodWhat aI evaluation and assurance engineering covers
- 02Reference practiceAI assurance draws on risk frameworks, management systems and evaluation practice
- 03In systemic risk workEvidence that an AI system is fit for its use
- 04Applied inPlatforms that apply this method
- 05StewardshipWho maintains this page
- 06SourcesWhere this page's references lead
What aI evaluation and assurance engineering covers.
- 01
Core methods
capability and safety evaluation; red teaming; robustness and adversarial testing; bias and fairness assessment; assurance case development; post-deployment monitoring.
- 02
Data and instruments
evaluation benchmarks; incident reports; model documentation; deployment logs; human feedback records.
- 03
Validation
Evaluation methods are checked for reliability and relevance to deployment conditions. Independent replication and third-party audits test claims.
- 04
Limits
Benchmarks may not reflect real-world use or novel capabilities. General-purpose systems have open-ended behaviour that resists complete testing.
- 05
Use in risk decisions
AI assurance determines whether automated systems can be trusted in high-consequence settings. It helps manage risks when AI is embedded across finance, health and infrastructure.
AI assurance draws on risk frameworks, management systems and evaluation practice.
| Reference | What it sets |
|---|---|
| NIST AI RMF | Measure and manage functions |
| ISO/IEC 42001 | AI management system |
| EU AI Act conformity requirements | Obligations for high-risk systems, applied in stages |
| OECD AI Incidents Monitor | Record of reported AI incidents |
Evidence that an AI system is fit for its use.
Assurance builds an argument that a system is acceptably safe for a defined use, backed by evaluations, red teaming, monitoring and incident records. For systemic risk the use context matters most: the same model may be acceptable for drafting and unacceptable for triage.
- Define the use context before evaluating.
- Monitor after deployment; capability and use drift.
- Record incidents and near misses.
- Applications on AI and Autonomous Systems.
Platforms that apply this method.
Who maintains this page.
Artificial Intelligence and Autonomous Systems stewards aI evaluation and assurance engineering: it keeps this description current and reviews how the method is applied across the platforms.
Where this page's references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
Where this page leads across the Consortium.
- Applied inGCRIAI evaluation and assuranceApplied in Artificial Intelligence and Autonomous Systems.
- Applied inGRFAI evaluation and assurance engineering for AI and digital governanceApplied in AI and Digital Governance.
- Applied inGRAAI evaluation and assurance engineering for AI and financial model riskApplied in AI and Financial Model Risk.
Questions and answers.
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The same subject across the Consortium.
- GRFglobalriskforum.comAI evaluation and assurance engineering for AI and digital governanceMandates, commitments and accountability
- GRAglobalriskalliance.comAI evaluation and assurance engineering for AI and financial model riskExposure, loss and capital
- Nexus Standardsnexustandards.comAI and autonomous systems risk standardsStandards that apply
- Risk Academyrisksacademy.comAI assurance and auditWhere it is taught
- Nexus Reportstherisk.globalArtificial Intelligence and Autonomous Systems in Nexus ReportsWhere it is reported
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