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NIST AI 100-1

NIST AI Risk Management Framework. Operationalized.

The NIST AI RMF is a voluntary framework for managing AI risks — and the benchmark the Colorado AI Act's Sec. 6-1-1706 affirmative defense points to. Aguardic operationalizes Govern, Map, Measure, and Manage as enforceable policies with continuous evidence.

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Does This Apply to You?

NIST AI RMF Alignment Is Increasingly Expected

Federal & Government

  • Organizations selling AI products or services to U.S. federal agencies
  • Government contractors where NIST AI RMF alignment is required in RFPs
  • Federal agencies deploying AI systems under executive order guidance

Enterprise AI Teams

  • Companies adopting NIST AI RMF as their internal AI governance framework
  • Organizations that want a structured, internationally recognized approach to AI risk
  • Teams preparing for future U.S. AI regulation by building on NIST foundations

While NIST AI RMF is voluntary today, it is becoming the de facto standard that auditors, customers, and regulators reference when evaluating AI governance maturity.

Four Core Functions

Operationalize Every Function of the NIST AI RMF

Govern

Establish AI governance policies, define roles and accountability structures, and create organizational processes for AI risk management.

Map

Identify and categorize AI systems, their operational contexts, potential impacts, and stakeholders. Build a complete AI system inventory.

Measure

Assess AI system performance, evaluate bias and fairness, quantify risk levels, and benchmark against organizational thresholds.

Manage

Implement risk controls, respond to incidents, track remediation, and continuously improve AI systems based on measurement data.

Requirements Coverage

NIST AI RMF Coverage Matrix

No single tool covers every NIST AI RMF category. This is the function-to-control reference — what Aguardic enforces across Govern, Map, Measure, and Manage, the evidence it produces, and the work your risk team still owns.

14Covered
3Partial
0Not Covered
Total: 17
Covered·

GOVERN 1

Policies & Processes

Establish organizational AI risk management policies, processes, and procedures (GV-1 + GV-6).

How Aguardic helps

GOVERN pack's Missing Governance Policy rule flags AI projects launched without documented governance, acceptable use, or regulatory mapping. Undocumented Operating Procedures rule catches AI pipelines shipped without SOPs or runbooks. Policy-as-code versioning adds an enforceable audit trail.

Evidence produced

Missing governance policy detections · undocumented procedure flags · policy version history · enforcement configuration

What you handle

Author the organizational AI risk management policy document and ratify the policies Aguardic enforces.

Covered·

GOVERN 2

Accountability Structures

Establish accountability structures and mechanisms for AI risk management (GV-2).

How Aguardic helps

GOVERN pack's Missing Accountability rule flags AI-driven decisions, outputs, and deployments without a named owner, responsible team, or escalation path. Audit trails capture who triggered evaluations and reviewed escalations.

Evidence produced

Missing accountability detections · user action logs · escalation review records

What you handle

Define accountability structures — who owns AI risk, who approves decisions, who escalates — and document in your governance charter.

Partial·

GOVERN 3

Workforce Diversity & Domain Expertise

Ensure AI risk management teams include diverse and interdisciplinary expertise (GV-3).

How Aguardic helps

GOVERN pack's Lacking Diverse Expertise rule flags AI governance decisions, risk assessments, and development processes made without interdisciplinary input (ethics, legal, compliance, domain SMEs). Aguardic surfaces the gap; HR staffing decisions stay with your organization.

Evidence produced

Lacking diverse expertise detections · AI risk assessment participation logs

What you handle

Run HR programs for hiring domain experts, building diverse teams, and training staff on AI risks.

Partial·

GOVERN 4 & 5

Risk Culture & Stakeholder Engagement

Foster organizational culture that prioritizes AI risk awareness and engages affected stakeholders (GV-4 + GV-5).

How Aguardic helps

GOVERN pack's Insufficient Risk Culture rule flags AI programs missing lessons-learned processes, psychological safety signals, or risk-reporting channels. Missing Stakeholder Engagement rule catches AI systems designed without community or affected-party input. Compliance dashboards roll up into enterprise risk reporting.

Evidence produced

Risk culture indicator flags · missing stakeholder engagement detections · compliance dashboard exports

What you handle

Integrate AI risk into your enterprise risk register, run leadership risk reviews, and maintain stakeholder engagement programs.

Covered·

MAP 1

Context & Intended Purpose

Establish context and define the intended purpose and use of AI systems (MP-1).

How Aguardic helps

MAP pack's No Intended Use rule flags AI deployments that ship without documented use cases, target populations, or operational context. AI System Registry captures intended purpose, risk tier, data categories, and integration context.

Evidence produced

Missing intended use detections · AI System Registry records · system context documentation

What you handle

Define and approve the intended-purpose statement for each AI system registered.

Covered·

MAP 2

Categorization of AI Systems

Categorize AI systems by context of use, risk level, and potential impacts (MP-2).

How Aguardic helps

MAP pack's Undefined Context of Use rule flags AI systems deployed without domain boundaries, known limitations, or failure mode documentation. AI System Registry supports configurable risk tiers and impact scope.

Evidence produced

Undefined context detections · risk classification records · categorization history

What you handle

Set risk-tier thresholds for your organization and sign off on each system's categorization.

Partial·

MAP 3

Benefits & Costs

Assess potential benefits and costs of AI system deployment including societal impacts (MP-3).

How Aguardic helps

MAP pack's Missing Benefit-Cost Analysis rule flags AI deployment decisions made without documented benefit analysis, negative externality assessment, or community impact consideration. Aguardic surfaces the gap; financial modeling stays with your business team.

Evidence produced

Missing benefit-cost detections · AI deployment decision flags

What you handle

Conduct business-case cost-benefit analyses and document ROI and risk trade-offs per AI system.

Covered·

MAP 4

Risks & Impacts Identification

Identify and characterize risks and potential impacts across individuals, groups, communities, and ecosystems (MP-4).

How Aguardic helps

MAP pack's Uncharacterized Risks rule flags operational AI systems without risk inventories, threat models, failure modes, or potential-harm documentation. Pairs with AI System Registry risk classification.

Evidence produced

Uncharacterized risks detections · risk register exports · AI System Registry risk fields

What you handle

Run the formal risk identification workshop, classify risks across dimensions (privacy, fairness, safety, security), and assign risk owners.

Covered·

MAP 5

Impact Characterization

Characterize potential positive and negative impacts on rights, safety, and well-being of affected parties (MP-5).

How Aguardic helps

MAP pack's Missing Impact Characterization rule flags customer-facing AI and automated decision systems (screening, scoring, ranking) deployed without impact analysis on affected users, patients, citizens, or employees.

Evidence produced

Missing impact characterization detections · affected-party analysis flags

What you handle

Complete the impact assessment document, engage affected communities, and sign off with counsel before deploying in sensitive domains.

Covered·

MEASURE 1

Appropriate Methods & Performance Metrics

Select appropriate measurement methods and capture performance metrics before deployment (MS-1).

How Aguardic helps

MEASURE pack's No Performance Metrics rule flags model deployments pushed to production without accuracy, precision, recall, F1, AUC, latency, or benchmark data. Three-layer evaluation engine adds deterministic + semantic + knowledge-RAG methods.

Evidence produced

Missing performance metrics detections · evaluation methodology logs · measurement records per layer

What you handle

Select which evaluation methods apply per AI system and tune thresholds for your risk appetite.

Covered·

MEASURE 2

Trustworthiness: Bias, Fairness & Robustness

Evaluate AI systems for trustworthiness including bias, fairness, and adversarial robustness across the lifecycle (MS-2).

How Aguardic helps

MEASURE pack's Missing Bias Testing rule flags evaluation reports without demographic parity, disparate impact, or equalized-odds metrics. Missing Robustness Testing rule catches AI systems deployed without adversarial, edge case, or prompt-injection testing.

Evidence produced

Missing bias testing detections · missing robustness testing detections · continuous evaluation logs

What you handle

Define trustworthiness criteria for your context (e.g., fairness metrics) and sign off on target values.

Covered·

MEASURE 3

Risk Tracking Mechanisms

Track identified AI risks over time with defined metrics, dashboards, and review cadences (MS-3).

How Aguardic helps

MEASURE pack's No Risk Tracking rule flags identified AI risks that lack ongoing monitoring, trend analysis, assigned owners, or review cadences. Compliance dashboards track violation rates and enforcement actions over time.

Evidence produced

Missing risk tracking detections · trend reports · violation metrics · compliance dashboard exports

What you handle

Establish reporting cadences to leadership and review trend exports at defined intervals.

Covered·

MEASURE 4

Measurement Feedback Loop

Close the loop from measurement data into improvement, retraining, or remediation actions (MS-4).

How Aguardic helps

MEASURE pack's Missing Feedback Loop rule flags AI monitoring data, model performance reports, and accuracy dashboards that lack corresponding action items, remediation plans, or retraining triggers.

Evidence produced

Missing feedback loop detections · monitoring-to-action trace logs

What you handle

Staff the feedback review process, own the retraining or recalibration backlog, and document lessons learned.

Covered·

MANAGE 1

Risk Response & Incident Plans

Prioritize and act on AI risks with incident response plans, escalation procedures, and tiered enforcement (MG-1).

How Aguardic helps

MANAGE pack's No Incident Response Plan rule flags production AI systems without rollback, fallback, or failover procedures. No Escalation Procedures rule catches AI risks and failures without defined escalation paths. Enforcement modes (Block/Warn/Monitor) add tiered automated response.

Evidence produced

Missing incident response detections · missing escalation detections · enforcement action logs · risk-prioritized violation records

What you handle

Set enforcement-mode defaults per risk tier, staff the incident response team, and approve exception policies.

Covered·

MANAGE 2

Risk Treatment & Failure Detection

Plan risk treatment strategies and detect AI system failures that require risk response (MG-2).

How Aguardic helps

MANAGE pack's Missing Risk Treatment Plan rule flags identified AI risks without documented mitigations or correction timelines. AI System Failure Indicator rule detects model failures, hallucination rate spikes, inference errors, and safety incidents in real time.

Evidence produced

Missing treatment plan detections · AI failure indicator alerts · policy enforcement records · treatment effectiveness logs

What you handle

Plan treatment strategies per risk and assign owners to policy rollouts.

Covered·

MANAGE 3

Continuous Risk Monitoring

Continue monitoring AI risks on an ongoing basis with defined indicators, thresholds, and review cadences (MG-3).

How Aguardic helps

MANAGE pack's No Continuous Risk Monitoring rule flags AI risk programs that assess risks once without scheduled review, automated indicators, or monitoring cadence. Continuous post-deployment evaluation runs across every connected integration.

Evidence produced

Missing continuous monitoring detections · post-deployment evaluation logs · real-time alert records

What you handle

Define post-deployment review cadences and authorize deprecation or rollback decisions.

Covered·

MANAGE 4

Risk Communication

Communicate risk management results, limitations, and plans to upstream and downstream AI actors including users (MG-4).

How Aguardic helps

MANAGE pack's Missing Risk Communication rule flags known AI risks, limitations, and failure modes that are not disclosed or reported to affected parties, business units, or governance bodies.

Evidence produced

Missing risk communication detections · disclosure gap reports

What you handle

Maintain the risk communication cadence to leadership, users, and affected business units; route disclosures through counsel when required.

Browse the NIST AI RMF Policy Templates

Coverage mappings reflect Aguardic's current product capabilities mapped to NIST AI 100-1 framework functions and categories. The NIST AI RMF is a voluntary framework — validate these mappings against your organization's risk management requirements.

Federal / enterprise questionnaire?

Answer NIST AI RMF questions with function-level controls Aguardic enforces

Upload it. We draft answers citing Govern / Map / Measure / Manage function controls — at the level of detail federal procurement reviewers push back on when missing. Aguardic enforces the controls and produces the evidence on every AI interaction.

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Explore Other Frameworks

HIPAAHealthcareEU AI ActEU RegulationSOC 2Trust ServicesISO 42001AI ManagementColorado AI ActUS StateAIUC-1AI Agent Security

View all compliance frameworks

Operationalize NIST AI RMF today.

Install policy templates mapped to NIST AI RMF functions, register your AI systems, and start generating evidence automatically.

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NIST AI RMF — Operationalize AI Agent Governance and Risk Management - Aguardic