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Framework solution

NIST AI RMF
for Real-World AI

Turn the NIST AI Risk Management Framework into an operational governance system.

CognitiveView helps organizations operationalize the NIST AI RMF across AI systems, applications, agents and third-party AI — connecting risk, controls, evaluation, evidence and continuous monitoring from development to deployment.

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CognitiveView NIST AI RMF dashboard showing Govern, Map, Measure and Manage progress alongside AI system assurance status and risk distribution
NIST AI RMF
AI management systemISO/IEC 42001
EU AI Act
AI risk managementISO 23894
Model risk managementSR 11-7
Equal credit opportunityECOA
Fair credit reportingFCRA
Financial privacyGLBA
Mortgage disclosureHMDA
Mortgage servicingReg X
Servicemember protectionsSCRA

The framework

The NIST AI RMF Framework

NIST AI RMF provides a voluntary, flexible approach to managing AI risks. CognitiveView operationalizes the framework across your entire AI lifecycle.

Learn about NIST AI RMF

  1. GOVERN

    Establish the foundation for responsible AI.

    • Policies & accountability
    • Risk management strategy
    • Roles & responsibilities
    • AI governance processes

  2. MAP

    Understand context and identify risks.

    • AI inventory and use cases
    • Stakeholders and impacts
    • Risk identification
    • Legal and regulatory context

  3. MEASURE

    Evaluate and analyze AI risks.

    • Performance testing
    • Fairness assessment
    • Safety and security testing
    • Evidence collection

  4. MANAGE

    Prioritize and respond to AI risks.

    • Risk treatment plans
    • Control implementation
    • Authorization decisions
    • Continuous monitoring

Operational assurance

From Framework to Operational Assurance

CognitiveView connects the NIST AI RMF functions to real AI systems, use cases, controls, evidence and decisions — so you can operationalize the framework, not just document it.

See How It Works

CognitiveView Operating Model

Framework requirements → operational evidence

NIST AI RMF

Govern, Map, Measure and Manage guide the operating model across the AI lifecycle.

  1. GOVERN
  2. MAP
  3. MEASURE
  4. MANAGE

CognitiveView Operating Model

01

Understand the exposure

Connect each AI system to its use case and identify the risks that need attention.

  1. AI System
  2. Use Case
  3. Risk
02

Build the evidence

Apply controls, evaluate performance and capture the evidence behind each assessment.

  1. Controls
  2. Evaluation
  3. Evidence
03

Make accountable decisions

Establish assurance, authorize deployment and monitor how the system changes.

  1. Assurance
  2. Authorization
  3. Monitoring

Key capabilities

Key Capabilities

End-to-end capabilities to operationalize NIST AI RMF.

Explore all capabilities
  • Know your AI exposure01

    AI Inventory & Risk Mapping

    Discover AI systems, use cases and map risks to NIST AI RMF outcomes.

    • Connect systems to use cases
    • Record ownership and context
    • Map risks to framework outcomes

    AI Systems

    NameUse CaseRisk TierRMF Status
    Loan AssistantCustomer ServiceHighMeasure
    Underwriting ModelCredit DecisioningCriticalManage
    Fraud DetectionRisk & FraudHighMeasure
    Servicing AgentBorrower SupportMediumGovern
    Learn more
  • Validate with measurable results02

    Evaluation & Testing

    Assess performance, fairness, robustness and safety.

    • Evaluate performance and fairness
    • Review robustness and safety
    • Record findings for assurance

    Model Evaluation

    • Accuracy92%
    • Fairness88%
    • Robustness81%
    • Safety90%
    Learn more
  • Keep decisions traceable03

    Evidence & Traceability

    Capture and organize evidence across the AI lifecycle.

    • Organize assessment records
    • Track controls and approvals
    • Connect evidence to decisions

    Evidence Trail

    • Risk AssessmentComplete
    • Fairness EvaluationComplete
    • Security ReviewIn Review
    • Control ImplementationComplete
    • Deployment ApprovalPending
    Learn more
  • Maintain assurance after deployment04

    Continuous Monitoring

    Monitor AI systems in production and manage ongoing risk.

    • Watch drift and performance
    • Track incidents and fairness
    • Reassess as AI systems change

    Model Monitoring

    • Drift0.12↓ -2.2%
    • Accuracy92%↑ +2%
    • Fairness Gap0.03↑ +0.4%
    • Incidents0— 0%
    Learn more

Get started

Put NIST AI RMF into action.

Govern AI risk. Measure what matters. Build evidence. Make better AI decisions.

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