SmartSuite for the Model Validator
The Model Validator provides independent oversight of AI risk and performance assessments, reviews issues and determines approval, conditional approval or cancellation. They monitor key AI risk indicators such as drift, bias, safety, security and compliance signals and confirm remediation is completed.
What you own
The Model Validator provides independent oversight of AI risk and performance assessments, reviews issues and determines approval, conditional approval or cancellation.
- Independently validate model risk and performance assessments
- Decide approval, conditional approval or cancellation of model use
- Monitor drift, bias, safety, security and compliance indicators
- Review issues raised against models and set remediation conditions
- Confirm remediation actions are completed and effective
- Document validation decisions with traceable evidence
At a glance
The GRC processes this role owns, touches and depends on.
Owns
accountable for the process
Touches
contributes or approves
Depends on
consumes its output
How the GRC processes connect
Three lines of defense set the frame; the chain shows where each process hands off to the next. Colors follow the role: navy is owned, blue is touched, gray is depended on.
Reporting lines
Who this role reports to, who reports to them, and the peers they work with most.
Reports to
Direct reports
Works closely with
How SmartSuite supports this role
Lead
AI governance. Runs assessment workflows with reviewer decision logs and exception tracking so governance decisions are consistent and traceable.
Risk management. Monitors key AI risk and performance indicators such as drift, bias, safety and security signals.
Issues and actions. Ensures remediation actions are completed and validated before approval conditions are lifted.
Reporting. Records approval outcomes and open conditions for governance reporting.
Products this role uses most
Industry reference
The Federal Reserve's SR 26-2 and OCC Bulletin 2026-13 (17 April 2026, issued jointly with the FDIC) superseded SR 11-7 and OCC 2011-12 with risk-based guidance that keeps independent validation at its center: conceptual soundness, ongoing monitoring and outcomes analysis performed by people not involved in the model's development, with generative and agentic AI models outside its scope. The UK PRA's SS1/23 carries the same principle for banks, and ASOP 56 applies to actuarial models. NIST AI RMF's Measure function and ISO/IEC 42001 extend validation to AI and machine-learning systems.
The EU AI Act requires conformity assessment of high-risk systems; insurers validate pricing and underwriting models under NAIC and state expectations; healthcare validates clinical algorithms under FDA guidance; public bodies test rights-impacting AI under OMB guidance before deployment.
Related roles
Explore Further
Guides and definitions from the GRC Knowledge Center and the What-is glossary, chosen for this role.
Frequently asked questions
What is the Model Validator responsible for deciding?
The Model Validator provides independent oversight of AI risk and performance assessments and decides whether a model is approved, conditionally approved or canceled. They monitor drift, bias, safety, security and compliance indicators, review the issues raised against models and set remediation conditions, confirm that remediation is completed and effective, and document every validation decision with traceable evidence. Independence is the point: the validator was not involved in the model's development, as SR 26-2 and the PRA's SS1/23 require.
How does SmartSuite help a Model Validator make consistent, traceable decisions?
AI Governance runs assessment workflows with reviewer decision logs and exception tracking, so governance decisions are consistent and traceable. The validator reviews the Model Owner's assessment in the workflow, records the decision and its reasoning in the decision log, and logs any exception granted with its conditions. CRI AI RMF supplies the assessment structure where that profile is used. A later reviewer, or the Internal Auditor, can see why a model was approved and on what conditions.
How are drift, bias and other AI risk indicators monitored?
SmartSuite monitors key AI risk and performance indicators such as drift, bias, safety and security signals against each model, so the Model Validator sees when an indicator moves outside what the approval assumed and can raise an issue or revisit the conditions. The AI Risk Officer sees the same indicators for the risk view. Monitoring is part of the model's record, which is the ongoing monitoring that NIST AI RMF's Measure function and SR 26-2 expect.
How does the validator confirm remediation before lifting conditions?
Remediation actions are completed and validated in Issues Management before approval conditions are lifted, so a condition attached to a conditional approval stays open until the Model Owner has submitted evidence and the validator has confirmed it. The validator does not take a status update on trust; the evidence is on the issue, and the condition is lifted in the record. That sequence is what makes a conditional approval enforceable rather than advisory.
How does this role report outcomes to the Model Risk Manager?
SmartSuite records approval outcomes and open conditions for governance reporting, so the Model Risk Manager sees each validation's decision, the conditions attached and which remain open without asking for a summary. The AI Governance Lead has the same view for governance meetings. Because the decision log and the conditions are the record, the report upward is a filter on it, and the evidence behind any decision is one step away.
What does a validator need to start working in SmartSuite?
A validator needs the models they are assigned to review, with their owners and the assessments due, and the criteria they apply when deciding approval. New customers can start with SmartSuite Onboarding, a guided four-week program around one or two priority workflows; for a validation team the first workflow is the assessment review with its decision log. Decisions, conditions and exceptions from previous validations can be recorded so the history is complete, and from then on each model carries its decision log, which is what a regulator or auditor asks to see.
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