AI Governance & Lifecycle Explorer

AI Governance & Lifecycle Model

UDSA
NIST AI RMF – Continuous Risk GovernanceGOVERNAccountability, policies,roles, risk appetiteStrategic oversightDefine & PlanMAPContext, use cases,stakeholders, impactRisk identificationAssess & AnalyseMEASURERisk metrics, testing,evaluation, thresholdsQuantitative assessmentAct & ControlMANAGEControls, mitigation,response, improvementRisk treatmentImprove & IterateResponsible AI Lifecycle1. Problem FramingDefine purpose, constraints, success criteriaIdentify stakeholders and ethical boundariesEstablish governance requirements2. Data CollectionSource, quality, consent, representativenessEnsure data provenance and documentationPrivacy and bias considerations3. Model DevelopmentDesign, training approach, assumptionsModel Development LifecycleDefineObjective, labels,featuresCollectTraining andvalidation dataPreprocessCleaning, transform,balancingTrainOptimisation andlearningEvaluateAccuracy, error,robustnessDeployIntegration, access,scalingMonitorDrift, degradation,anomaliesRetrain Loop4. Testing & ValidationPerformance, bias, robustness, safetyIndependent validation and stress testingCompliance verification5. DeploymentRelease controls, access, monitoring hooksStaged rollout and incident response readinessDocumentation and user guidance6. Monitoring & FeedbackDrift, incidents,misuse, outcomes7. Fairness & Impact ReviewHarm assessment,accountability, iterationImpact Feedback LoopCross-cutting ElementsTEVV Assurance LoopTest → Evaluate → Verify → ValidateAssures correctness, safety, andintended behaviour across all stagesTestEvalVerifyValidSecurity & Adversarial TestingContinuous threat assessment andadversarial robustness testing⚠ Data poisoningCompromised training data⚠ Prompt injectionMalicious input manipulation⚠ Model abuse / misuseUnintended or harmful useMonitoring, Feedback, andRisk ReassessmentContinuous monitoring of modelquality, drift, harm, and misuse📊 Quality, Drift, Harm indicatorsSecurity & Business metrics🔄 Feedback loops & reassessmentActions & risk updatesHuman Oversight andAccountabilityKeep accountability with peopleeven when systems are autonomous👥 Escalation & override controlsClear decision rights⚖️ Separation of duties & trainingBuilders vs approversDocumentation, Traceability,and Change ControlMaintain evidence, auditability,and safe evolution over time📝 Model cards & change logsData sheets & risk cards🔍 System inventory & approvalsVersion control & ownership✅ Release gates & sign-offsApproval records & audit trails🚨 Traceability from incidentsDecision logs & lineage trackingFeedback BridgeReal-world outcomes → Risk updates→ Governance controlsLegend:Governance (NIST RMF)Responsible AI LifecycleModel Development LoopCross-cutting ElementsFeedback Loops

Governance

NIST AI RMF framework for AI risk management

Lifecycle

End-to-end Responsible AI development process

Technical

Model development and deployment cycle

Feedback

Continuous iteration and improvement loops

Built for AI governance education, consulting, and executive training

Based on NIST AI Risk Management Framework and Responsible AI best practices

Created by Arinze Okosieme

Trusted AI Safety Expert (TAISE), CISSP, CCSP, CCZT

Trusted AI Safety, Zero Trust, Cloud & InfoSec Advisor & Coach

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Connect on LinkedIn: @arinze-okosieme