Requirements early
Business, development, and model-risk teams agree on evidence, roles, benchmarks, and approval standards at the start.
Model validation and evidence · Provance
Model validation built into delivery.
Provance moves requirements, independent challenge, and evidence creation into the delivery cycle. One methodology covers traditional, AI/ML, and agentic systems so problems surface earlier and approval evidence is assembled as the work happens.
01 · What changes
Validation becomes part of delivery.
Requirements are known before development begins, problems surface while they are still inexpensive to fix, and the timeline becomes more predictable.
Problems surface earlier
Data, methodology, performance, fairness, and operational weaknesses are reviewed at defined points instead of accumulating until final validation.
Parallel work
Development and validation move on coordinated, parallel tracks rather than waiting in a single sequential queue.
Evidence by design
Artifacts are created, versioned, approved, and retained as the work happens so examination readiness is a property of the process.
02 · Seven checkpoints
Validation is a sequence of decisions, not one final event.
The checkpoints put independent challenge where it can still change the system and make the approval state explicit before production exposure expands.
Validation charter
Scope, tier, architecture, roles, evidence requirements, and validation strategy.
Validation plan
Proposed method, benchmark, testing strategy, and operating approach.
Data assessment
Suitability, lineage, quality, representativeness, and known limitations.
Method assessment
Implementation, assumptions, tests, and documentation reviewed while development continues.
Challenge report
Independent findings on performance, resilience, fairness, misuse, controls, and operating assumptions.
Decision record
Documented approval, limitation, condition, or rejection tied to the evidence.
Monitoring plan
Performance thresholds, change triggers, approved conditions of use, and ongoing review.
03 · Why the cycle compresses
The work moves in parallel.
Four operating mechanics shorten the timeline while preserving the evidence standard.
Front-loaded requirements
Scope defines the evidence and approval standard before development, preventing late discovery of material requirements.
Parallel execution
Development, data assessment, model-risk review, adversarial testing, and documentation proceed on coordinated tracks.
Tiered intensity
The validation burden scales to what the model can affect, preventing low-risk work from consuming the same resources as consequential systems.
Evidence assembled in flight
The record required for approval and examination is produced as the underlying decisions are made.
04 · Risk tiers
Governance matched to what the model can affect.
Three tiers determine the independence, evidence, approval, deployment, and monitoring required.
T1
Internal · Low stakes
Outputs remain inside the business and do not determine customer or regulated outcomes. Self-service validation operates under a defined production gate and standard software controls.
T2
Customer-adjacent · Moderate stakes
Outputs inform customer-facing activity under human oversight. Standard model-risk validation, staged deployment, and human-in-the-loop controls apply.
T3
Consequential · High stakes
Outputs materially affect customers, capital, regulated decisions, or legal obligations. Strong independent validation, approval, monitoring, and recurring review apply.
05 · Twelve tools
Twelve tools make the methodology operational.
The tools work together across six jobs the institution must perform: diagnose the current state, specify the work, assess the system, control production, govern the portfolio, and modernize validation for AI, machine learning, and agentic systems.
Know what must change.
Validation Readiness Assessment.
Define the evidence before development.
Templates and standards establish intended use, tier, roles, evidence, benchmarks, and approval logic.
Challenge the system.
Independent assessment of data, methodology, performance, fairness, robustness, documentation, and operating controls.
Control real-world exposure.
Staged Deployment Planner · Contingency Plan Template · Data Monitoring Specification.
Manage validation as an enterprise capability.
RTY Calculator · Exam Readiness Package · Portfolio Governance Module.
Validate what legacy model risk never covered.
Traditional validation assumed statistical models and a single production gate. Provance applies the same evidence standard to AI, machine learning, and agentic systems, including behaviors, controls, and ongoing changes that older validation approaches were not built to address.
06 · Regulatory coverage
One methodology, mapped to eight frameworks.
Provance maintains a framework-agnostic operating core and maps its evidence to major regulatory and governance standards across five jurisdictions.
SR 26-2 · SR 11-7
NIST AI Risk Management Framework
EU AI Act
EBA Guidelines
PRA SS1/23
OSFI E-23
MAS FEAT
The practical value: one validated artifact can satisfy multiple overlapping obligations without forcing the team to create separate evidence packages for every framework.
07 · Evidence and ownership
Evidence that survives scrutiny and stays current.
Every artifact is versioned, approved, stored, and verified under one Evidence Control Standard, creating a traceable record from initial scope through production operation.
The Continuous Validation Specification turns continuous validation into a rolling cadence matched to each system’s rate of change and risk. Performance breaches, material changes, incidents, and scheduled reviews can trigger revalidation.
Provance is designed for transfer. After handover, your team owns the methodology, the tools, the evidence, and the governance record.
08 · Provance and AIRO
Validation and adoption should reinforce each other.
AIRO addresses the operating path from opportunity to production and value. Provance addresses the independent evidence that an individual model or AI system is fit for intended use. The two methods can work together or independently.