An Interoperable Framework for Independent AI Assurance. A structured, evidence-based methodology for Independent and Continuous AI Assurance across the full lifecycle of AI systems.
Independent AI Assurance is the systematic, evidence-based and professionally governed evaluation of an AI system against defined assurance objectives, undertaken to establish justified confidence in its governance, performance, security, reliability and continuing operation.
AI governance frameworks define principles, controls and organisational expectations. AIAF focuses on the assurance methodology required to evaluate whether those expectations are supported by sufficient and appropriate evidence in practice.
AIAF does not replace legislation, regulation, AI management systems, risk-management frameworks, sector rules or professional standards. It provides an interoperable assurance methodology that can be applied alongside them.
The framework connects governance expectations with evidence, technical evaluation, professional judgement, reporting and continuous re-assurance.
Understand system purpose, accountability, policies, controls, risk classification and applicable requirements.
Translate governance and risk expectations into clear, testable assurance questions.
Obtain documentation, records, system outputs, evaluation results and other relevant evidence.
Apply validation, testing, red teaming, robustness assessment and other procedures appropriate to the AI system.
Assess the sufficiency, appropriateness, limitations and significance of the available evidence.
Form proportionate conclusions against the defined assurance objectives and risk context.
Communicate evidence, limitations, findings and recommendations to intended users.
Repeat or update assurance after material system, model, data, prompt, tool, workflow or operating changes.
AIAF is designed to support credible, proportionate and repeatable assurance while preserving the role of accountable human judgement.
Assurance should be sufficiently separate from development, implementation and commercial interests to support objective conclusions.
Conclusions should be grounded in evidence that is relevant, reliable and appropriate to the assurance objective.
The nature, timing and extent of assurance procedures should reflect system impact, complexity and exposure.
Assurance should address development, deployment, production operation and significant change—not only a single pre-deployment review.
AI and automation may support assurance procedures, but accountable professional judgement remains necessary.
Reports should clearly communicate scope, evidence boundaries, assumptions, limitations and residual uncertainty.
AIAF treats assurance as a lifecycle discipline rather than a one-time certification event.
| Assurance stage | Purpose | Typical triggers |
|---|---|---|
| Initial Assurance | Establish evidence before deployment or material use. | New model, new application, first production release. |
| Production Assurance | Evaluate whether the system continues operating within approved expectations. | Periodic review, monitoring signals, incidents or emerging risks. |
| Change Assurance | Re-evaluate the system after material change. | Model, data, prompt, RAG, tool, workflow, agent or environment changes. |
| Enterprise Assurance Evidence | Provide decision-useful evidence for oversight and accountability. | Board, risk, audit, customer, regulator or procurement requirements. |
AIAF is intended to complement applicable governance, risk, security, privacy and AI management frameworks. It standardises the methodology of assurance—not the regulation of artificial intelligence.
AegisRT applies the framework through independent assessments, AI red teaming, technical evaluation, assurance reporting and Continuous AI Assurance engagements.
AIAF Version 1.0 was published in July 2026. ResearchGate is the official public publication page for this framework.
AI Assurance Framework (AIAF): An Interoperable Framework for Independent AI Assurance
Read the complete framework on ResearchGate.
AIAF Version 1.0
Author: Chenyi Ang
Published July 2026
Published by AegisRT
AIAF provides an interoperable methodology for Independent AI Assurance and Continuous AI Assurance. It is distinct from AegisRT's separate technical research and LinkedIn thought-leadership articles.
Use AIAF as a methodological foundation and engage AegisRT for independent assessment, AI red teaming, re-assurance and Continuous AI Assurance.
