Research-backed AI assurance resource
AI Assurance Evidence Review Kit v1.0
A practical Excel workbook for connecting AI system context, risks, controls, evidence, human review, evidence quality, gaps, and management review in one traceable workflow.
Public-file boundary: Use synthetic or non-sensitive information only. The public workbook is not a secure repository. Do not enter confidential, regulated, production, or security-sensitive information; use an organization-approved secured implementation for real organizational data.
Artifact Profile
v1.0Operational evidence workflow
What the Kit Is
The Kit is designed for AI governance, technology risk, compliance, internal audit, model risk, vendor risk, assurance, and financial-crime practitioners who need a structured review workflow rather than a high-level principles summary.
It organizes one review around six connected questions: What is the AI system? What can go wrong? What controls address the risk? What evidence exists? How do authorized people review AI-supported decisions? What gaps require action?
Five-step workflow
Profile → Map → Review → Assess → Summarize
1. Profile
Define the AI system, purpose, owners, decision support, limits, dependencies, and review authority.
2. Map
Connect material risks to control objectives, activities, owners, evidence, review status, exceptions, and remediation.
3. Review
Document how authorized people interpret, agree with, override, hold, or escalate AI-supported decisions.
4. Assess
Score evidence quality across eight transparent dimensions and record gaps without treating the score as proof.
5. Summarize
Use formula-driven measures, top gaps, owners, dates, and restrained charts to prepare management review.
Workbook contents
Seven Connected Worksheets
Start Here
Purpose, workflow, definitions, security notice, navigation, and release metadata.
AI System Profile
Bounded system description with owners, dependencies, limitations, automation level, and review authority.
Risk-Control-Evidence Map
Traceable relationships among risks, controls, evidence, review state, gaps, owners, and dated actions.
Human Oversight Review
Decision-level evidence of agreement, override, uncertainty, escalation, reviewer authority, and quality review.
Evidence Quality and Gaps
Transparent 0-3 scoring across eight dimensions with remediation, target dates, status, and priority.
Review Summary
Formula-driven measures, top gaps, priority actions, and two restrained charts for management review.
Sources and Methodology
Exact source inventory, source roles, scoring method, implementation limits, and release QA information.
Risk-control-evidence workflow
Make the Relationship Reviewable
The map is a relationship record. Each risk connects to a control objective, an observable control activity, an accountable owner, a specific evidence reference, a review state, and a dated action when a gap remains.
The synthetic example includes twelve mapped risks spanning data quality, model drift, alert prioritization, false negatives, false positives, threshold change, reviewer consistency, overrides, escalation, vendor dependency, access control, and evidence retention.
Review chain
- System context and decision support
- Material risk and affected objective
- Control objective and activity
- Control and evidence owners
- Evidence reference and review status
- Gap, remediation, target date, and priority
Human Oversight Review
Document Meaningful Human Authority
Decision record
Capture the trigger, reviewer role and authority, information considered, AI recommendation, human decision, agreement or override, rationale, escalation, and supporting evidence.
Follow-through
Record quality-review status, follow-up action, completion status, and unresolved work so human review is more than ceremonial sign-off.
The nine synthetic records demonstrate agreement, contextual override, escalation under uncertainty, conditional threshold approval, reviewer disagreement, QA reopening, delayed evidence, a vendor-model limitation, and documented agreement after complete review.
Evidence Quality and Gaps
Transparent Triage, Not an Assurance Conclusion
Eight dimensions are scored from 0 to 3: completeness, currency, provenance, integrity, traceability, reviewer approval, accessibility, and retention status. The workbook calculates a total out of 24, a percentage, and a descriptive category.
Strong
85-100%. Few documented weaknesses; contextual review is still required.
Adequate
65-84%. Usable with identified improvements or contextual limits.
Needs Improvement
45-64%. Material weaknesses should be remediated before reliance.
Weak
Below 45%. Evidence is incomplete or unreliable for the intended review.
Evidence-quality scoring is a prioritization and triage aid. A score does not prove control effectiveness, compliance, evidence sufficiency, audit acceptance, or regulator acceptance.
Worked example
Synthetic AML Alert Prioritization
The workbook uses one fictional financial-services AML transaction-monitoring example so the evidence chain stays coherent across worksheets. Rules and gradient-boosted scoring prioritize alerts for human review.
The example system provides a priority band, contributing factors, an uncertainty flag, and a queue recommendation. It does not make filing decisions. The example demonstrates a general review method; it does not make the Kit an AML compliance tool.
Example chain
A vendor version change creates a risk, a governance control requires version and testing evidence, the evidence record identifies missing version-level impacts, a model-risk reviewer restricts use pending testing, and the management summary elevates the gap for action.
Sources and methodology
Ten Sources with Defined Design Roles
The source inventory contains eight official or primary sources and two peer-reviewed studies. It identifies each source’s authority, date or version, design role, limitation, and URL.
Sources inform lifecycle governance, risk mapping, human oversight, model risk, AML alert management, data and lineage, documentation, and practitioner context. Source inclusion does not establish applicability, conformity, approval, endorsement, or complete framework coverage.
Implementation and limitations
Use a Secured Organization-Specific Working Copy
Implementation sequence
Retain an unchanged public reference copy, create an organization-approved working copy, replace synthetic content with approved organizational information, map actual criteria and policies, define decision rights, secure the repository, validate formulas and logic, and assign retention.
Responsibility remains local
Each organization remains responsible for applicability, configuration, security, validation, decision rights, approvals, monitoring, and evidence retention.
The Kit is a focused review workflow. It does not provide complete legal, regulatory, framework, model-validation, security, privacy, records-management, or audit-program coverage.
Artifact identity
Versioned Download Verification
50375062367df2de0f2175568bff5db0f35d81cdfd22d5d9daf1e96d45d75e4b 28a4461843bd1406f1eec565c21a2f3a9746deb88adb58c4f2305e6f4e044c8f Related resources
Continue the Evidence Workflow
Evidence Library
Browse the general Kit, specialized AML artifact, supporting evidence resources, and research materials.
AML AI Assurance Evidence Pack
Use the specialized AML public research prototype for deeper AML documentation exploration.
AML AI Human Oversight Evidence
Explore reviewer authority, escalation, rationale, override, and evidence expectations in financial-crime workflows.
AI Assurance Evidence
Review the broader editorial framework for connecting AI governance to control and evidence records.
AI Assurance Evidence Review Kit v1.0
Start with the Workbook and Guide
Download the ungated reference files, review the use boundaries, and configure a secured working copy for organization-specific use.