NIST AI Risk Management Framework: Deep Implementation Guide

Introduction to NIST AI Risk Management Framework

The NIST AI Risk Management Framework (AI RMF), released in January 2024, represents the most comprehensive, operationally-focused guidance for managing AI risks at scale. Unlike traditional frameworks that focus on ethics or responsible AI principles in abstract terms, the NIST AI RMF provides a structured, repeatable approach to identifying, assessing, and mitigating concrete harms from AI systems.

The framework exists to answer a fundamental question that organizations repeatedly face: "How do we systematically reduce the probability and magnitude of harmful outcomes from our AI systems?" This is distinct from compliance checklists—it's about actual risk reduction.

The NIST AI RMF structures risk management through four interconnected core functions that create a cycle of continuous improvement. These functions work together to create what the framework calls the "AI risk management lifecycle." Each function contains multiple subcategories, each subcategory contains functions (specific capabilities), and each function is measurable against four maturity levels.

The Four Core Functions Explained

The NIST AI RMF organizes risk management into four functions that must operate in concert:

These aren't sequential steps—they're ongoing functions. A mature AI risk program executes all four simultaneously. GOVERN establishes the structures and policies. MAP ensures you know what systems exist and what they do. MEASURE creates the evidence you need to understand risk. MANAGE turns that evidence into action and improvement.

GOVERN: Setting the Foundation for AI Risk Management

GOVERN establishes the organizational infrastructure, accountability structures, and risk appetite statements that make the other three functions possible. Without GOVERN, you have no clear ownership, no standards, and no way to scale practices.

GOVERN Subcategories

Gov-1: AI Risk Management Policies and Procedures establishes documented policies and procedures for AI risk management. This includes:

• A written AI risk management policy endorsed by board/C-suite, clearly stating that AI risk management is a responsibility across the organization
• Documented procedures for how different functions (engineering, product, compliance, legal, operations) integrate their efforts around AI risk
• Clear definitions of what constitutes "AI" in your organization for scope purposes—many organizations are surprised to discover they have 3x more AI systems than they thought when they create a clear definition
• Decision frameworks for when systems require what level of rigor (e.g., a recommendation engine serving 1M users daily might require more evaluation than an internal analytics dashboard)
• Exception and waiver processes—organizations need a systematic way to handle cases where policy can't be followed, and track why
• Update frequency for policies (annual review minimum) and trigger-based review (when regulations change, major incident occurs, new technology emerges)

Gov-2: AI Risk Management Roles and Responsibilities defines who owns what. Crucially, this isn't just "create an AI ethics board." You need:

• Clear executive sponsorship—someone at VP level or above responsible for overall AI risk posture
• Functional ownership model where each system has a clearly identified owner responsible for ensuring evaluation
• Integration with existing risk frameworks—map AI risk management to existing enterprise risk management structures (operational risk, compliance risk, strategic risk)
• Defined roles for evaluation practitioners—who designs evaluations, who executes them, who reviews results, who communicates findings, who ensures remediation
• Cross-functional governance bodies—typically a "risk assessment committee" that meets to review new systems, high-risk systems, and remediation progress
• Escalation paths for serious findings (e.g., if an evaluation identifies a critical issue, who needs to know within 24 hours)
• Training requirements—who needs training in AI risk management, how often, what content

Gov-3: Policies and Procedures for AI Risk Management Throughout the Lifecycle recognizes that risk management can't start at deployment. It must address:

• Pre-development: Is there a stage-gate where proposed AI systems undergo risk screening before substantial resources are committed?
• Development: How does the development team build in evaluation from day one? What's the minimum set of evaluation activities before beta testing?
• Deployment: What evidence must exist before a system can go live? Who signs off? What are the exit criteria?
• Operation: How frequently are live systems re-evaluated? What triggers deeper evaluation (user complaints, regulatory changes, significant updates)?
• Retirement: How are systems decommissioned, and what happens to the downstream systems that depend on them?
• Documentation: Every stage generates documentation that lives in a central system for audit and continuity purposes

Gov-4: Processes for Managing Documented AI Risk Decisions requires that all substantive decisions about AI risk be documented in a way you can retrieve and analyze later.

• Risk decision register—every major decision (approve system for production, accept risk given constraints, require additional evaluation, remediate, retire) gets recorded with rationale
• Evidence links—each decision should reference the evaluation results, stakeholder input, or other evidence supporting it
• Timeline tracking—when was decision made, when was decision implemented, what was the outcome
• Remediation tracking—when a risk mitigation is required, track: what was required, when it was due, when it was completed, what evidence shows it was successful
• Analytics—quarterly reporting on: decisions made, remediation closure rates, aging high-risk findings, patterns in failed remediations
• Regulatory readiness—auditors will want to see decision register; design for that from day one

Gov-5: Resource Allocation for AI Risk Management explicitly requires resource planning and budgeting.

• Budget allocation by system (e.g., high-risk systems get more evaluation budget) or by function (e.g., budget allocation for evaluation infrastructure)
• FTE planning—how many evaluation practitioners, data scientists, domain experts, and annotators do you need?
• Tool and infrastructure spending—evaluation platforms, annotation tools, compute for benchmark evaluation
• Training budget—certification programs, conference attendance, skill development
• Relationship costs—time spent in cross-functional meetings, stakeholder collaboration, regulatory engagement
• Capacity planning—as your portfolio grows from 5 to 50 AI systems, how does your evaluation capacity scale?

MAP: Understanding Your AI Risk Landscape

MAP answers the fundamental question: "What AI systems do we have, what do they do, who uses them, what could go wrong?" Many organizations skip or rush through MAP, then struggle because they don't have a clear picture of their AI footprint.

MAP Subcategories

Map-1: AI Actors and Processes identifies all relevant parties and information flows related to AI systems.

• Develop comprehensive inventory of AI systems—this is harder than it sounds. Many organizations discover they have shadow AI: systems built by teams they forgot about, legacy systems no one acknowledges, experimental systems running in corners of the organization
• For each system: system owner, development team, deployment environment, user population, data sources, external dependencies
• For each system: upstream systems (what data feeds into this system) and downstream systems (what uses this system's output). This dependency mapping is critical because a failure in an upstream system can cascade
• Governance and decision-making actors—who approves system deployment, who can request system changes, who owns the evaluation
• Third parties—are you using an external model (OpenAI, Claude), external evaluation service, external annotation service, or developing everything in-house
• Information flows—what data goes through the system, what data does the system generate, who accesses results, how are results archived

Map-2: Purpose, Use, and Organizational Context ensures you understand what the system is actually for and why it matters.

• Stated purpose—what is the system designed to do in a single sentence
• Actual use vs intended use—often different. A system designed to provide recommendations might actually be used as a binding decision-maker
• Business context—what problem does this solve, what happens if the system goes down, what is the financial impact
• User population—who uses the system, how many users, what are their expertise levels, are there power imbalances (e.g., is this used by judges deciding someone's fate, or by internal operations staff)
• Performance expectations—what does good performance look like in operational context, not just technical metrics
• Regulatory/legal context—is this system subject to specific regulations, does it make decisions about healthcare, finance, employment, justice

Map-3: AI Capability and Input Data Characteristics describes the technical foundation of the system.

• AI techniques used—is this a large language model, a recommendation system, computer vision, time series, ensemble, specialized domain model
• Input data characteristics: volume, data types, temporal characteristics, known limitations, data provenance, freshness requirements
• Training data: what data was the model trained on, how much, what time period, any data labeling or augmentation
• Model architecture details relevant to risk—how does the model make decisions, is it interpretable, what are known limitations of this architecture
• Model evolution—how often is the model retrained, what triggers retraining, who controls that process
• Model versions in production—are multiple versions running, how are they managed, can you rollback

Map-4: Evaluation, Monitoring, and Continuous Improvement Activities catalogs what you already know about the system's performance.

• Existing evaluation activities—what evaluation has been done (benchmark testing, user testing, production monitoring)
• Documented evaluation results—where do you keep evaluation results, how long do you retain them, who has access
• Monitoring in production—are you actively monitoring system performance, user feedback, error rates, fairness metrics
• Feedback loops—how do you capture and analyze user complaints, erroneous outputs, performance degradation
• Update frequency—how often are evaluation results reviewed, how frequently does the system get updated based on findings
• Known issues—what problems with the system are already known but not yet fixed
• Evaluation gaps—areas where you know you should be evaluating but aren't

Map-5: Risks and Potential Impacts is where you actually start asking "what could go wrong?"

• Harm taxonomy—NIST AI RMF defines specific categories of potential harm: inaccurate outputs, inappropriate bias, excessive system autonomy, lack of transparency, absence of human oversight, negative externalities
• Risk identification—for your specific system, which harms are possible
• Impact assessment—if a harm occurred, what would be the consequence? Quantify if possible (financial, reputational, safety, compliance)
• Probability assessment—how likely is this harm to occur
• Risk score—typically probability x impact, gives you a way to prioritize which risks matter most
• Existing mitigations—what reduces the likelihood or impact (deployment controls, human oversight, restrictions on use)
• Residual risk—after accounting for existing mitigations, what's the remaining risk
• Acceptable risk determination—is the residual risk acceptable given the business value and mitigations

MEASURE: Systematic Evaluation of AI Risk

MEASURE is where the evaluation expertise comes in. This function is about designing and executing rigorous, systematic evaluation activities that produce evidence about what risks are real and how severe they are.

MEASURE Subcategories

Meas-1: Performance Testing Designed to Reduce Harmful Outcomes requires planned, documented evaluation activities with clear purposes.

• Evaluation plan—for each system (or system update), document what you plan to evaluate, why, what evidence will answer the question, what success looks like
• Benchmark design—if using benchmarks, ensure they're relevant to the actual risks (not just general accuracy on popular benchmarks)
• Test data curation—evaluation data should reflect realistic conditions, edge cases, and known problem areas
• Evaluation methodology—which techniques: automated metrics, human evaluation, user testing, adversarial testing, scenario analysis
• Success criteria—specific targets (accuracy >95%, false positive rate <5%, fairness metric parity >90%)
• Evaluation roles—who runs evaluation, who reviews results, who interprets them for decision-making
• Result documentation—structured recording of what was tested, what was found, any limitations of the evaluation

Meas-2: Continuous Monitoring and Detection of Model Performance Degradation acknowledges that evaluation doesn't stop at deployment.

• Production monitoring infrastructure—systematic collection of model inputs, outputs, outcomes
• Key performance indicators—which metrics do you track (accuracy, latency, error rates, user satisfaction)
• Automated anomaly detection—flagging when performance degrades beyond thresholds
• Ground truth collection—for systems where you eventually learn the correct answer (loan approval → did they repay), collect that systematically
• Monitoring frequency—real-time monitoring for safety-critical systems, daily for most systems, less frequent for stable systems
• Alerting thresholds—at what point do you alert humans that something might be wrong
• Historical baseline—tracking performance over time to spot slow degradation
• Alert response procedures—when monitoring detects an issue, who needs to know and what do they do

Meas-3: Processes for Evaluating Behavioral and Output Quality focuses on what actually matters to users and stakeholders.

• Behavioral metrics—does the system behave predictably, can users understand its decisions, does it fail gracefully
• Output quality evaluation—structured assessment of output correctness, usefulness, appropriateness
• Fairness evaluation—systematic testing across demographic groups, user segments, input distributions
• Safety evaluation—can the system be used in harmful ways, what safeguards exist, are they effective
• Robustness evaluation—how does the system handle out-of-distribution inputs, adversarial perturbations, constraint violations
• Interpretability evaluation—can users understand why the system made a particular decision
• Consistency evaluation—does the system produce consistent decisions for similar inputs

Meas-4: Processes to Validate and Govern Data recognizes that AI systems are only as good as their data.

• Data governance—documented ownership, lineage, and quality standards for data used in AI systems
• Data validation—checking that data meets quality standards before it enters the system
• Data drift detection—monitoring whether input data distribution is changing over time
• Data bias analysis—systematic assessment of bias in training data, evaluation data, and production input data
• Data minimization—ensuring you're only using data necessary for the stated purpose
• Data retention—how long data is kept, when it's deleted, privacy and security controls
• Data access controls—who can access what data, audit logging of access
• Data versioning—for evaluation, you need to track what data version was used in what evaluation

Meas-5: Assessment and Documentation of Limitations and Uncertainties ensures honest acknowledgment of what the system can and can't do.

• Documented limitations—what kinds of inputs does the system struggle with, what populations is it less accurate for
• Confidence intervals—not just point estimates, but ranges of uncertainty around performance metrics
• Scenario analysis—documented evaluation of how the system performs in specific challenging scenarios
• Model card or system card—external documentation describing system capabilities, limitations, appropriate use, and known risks
• Uncertainty quantification—for systems making high-stakes decisions, explicitly quantifying uncertainty in predictions
• Edge case identification—systematic documentation of known edge cases and how they're handled
• Assumptions documentation—explicit listing of assumptions made in development and evaluation

MANAGE: Response, Remediation, and Continuous Improvement

MANAGE is what you do with the evidence from MEASURE. It's about making decisions, implementing mitigations, and continuously improving systems and practices.

MANAGE Subcategories

Mgmt-1: Decisions and Actions on AI Risks formalizes how you use evaluation evidence to make decisions.

• Risk-based decision framework—clearly documented criteria for what evaluation results lead to what decisions (approve for production, require additional evaluation, restrict deployment, retire system, etc.)
• Decision documentation—every major decision recorded in risk decision register with supporting evidence
• Risk acceptance—formal process for accepting residual risk, including documented justification and sign-off
• Risk rejection—process for determining that a system doesn't meet risk acceptance criteria
• Conditional approval—systems deployed with restrictions (specific user populations, additional monitoring, required human oversight)
• Escalation procedures—when evaluation finds serious issues, clear process for escalating to appropriate decision-makers
• Stakeholder communication—how evaluation results are communicated to system owners, end users, and affected parties

Mgmt-2: Plans and Procedures for Risk Mitigation and Controls outlines how you reduce risk after it's identified.

• Mitigation strategies—technical (improve model accuracy, add fairness constraints), operational (add human review, restrict features), governance (limit deployment scope, increase monitoring)
• Control implementation—for each mitigation, clear ownership, timeline, and success criteria
• Control validation—how you verify that mitigation actually reduces risk
• Control maintenance—ongoing monitoring to ensure controls remain effective as system and context change
• Control degradation—detecting when controls become ineffective and triggering remediation
• Alternative approaches—documented consideration of different ways to mitigate same risk, rationale for selected approach
• Cost-benefit analysis—explicitly considering cost of mitigation vs value of risk reduction

Mgmt-3: Systems, Procedures, and Monitoring for Ongoing Performance and Risk Management ensures systems don't drift after deployment.

• Monitoring plans—for each deployed system, documented approach to ongoing monitoring
• Monitoring metrics—which metrics are tracked, at what frequency, to what precision
• Baseline comparison—current performance compared to baseline or previous versions
• Degradation detection—automated alerts when performance drops below acceptable thresholds
• Trigger-based evaluation—circumstances that trigger deeper evaluation (user complaints spike, regulatory changes, model updates)
• Regular re-evaluation—schedule for periodically deep-diving into system evaluation, not just continuous monitoring
• Feedback integration—user complaints, error reports, and other feedback systematically incorporated into monitoring and evaluation

Mgmt-4: Incident Response, Monitoring, Transparency, and Communication Procedures addresses what happens when something goes wrong.

• Incident definition—what counts as an incident (system produces clearly wrong output, user complains about bias, performance degrades suddenly)
• Incident detection—how incidents are identified (monitoring alerts, user reports, stakeholder escalation)
• Incident classification—severity levels (critical: system causing harm, high: significant degradation, medium: user-facing issues, low: isolated cases)
• Response procedures—who to notify, investigation procedures, remediation steps, communication timeline
• Root cause analysis—systematic investigation of what caused incident, not just treating symptom
• User notification—when do you notify affected users, how do you explain what happened
• Regulatory notification—if required by regulation, when and how do you notify regulators
• Post-incident review—after-action meeting to understand what happened and prevent recurrence
• Transparency reports—documenting incidents and their handling for internal review and external compliance

Mgmt-5: Processes and Procedures for Improvements to AI Systems and Practices creates feedback loops for continuous improvement.

• Lessons learned process—systematic capture of insights from incidents, evaluations, and monitoring
• System improvement prioritization—identifying which improvements would most reduce risk or improve performance
• System updates—planning and testing updates based on identified improvements
• Evaluation improvement—based on what evaluation revealed about system behavior, improving evaluation for next iteration
• Tooling and infrastructure improvement—based on operational experience, upgrading monitoring, evaluation platforms, annotation processes
• Training and skill development—identifying skill gaps that became apparent during evaluation or incident response
• Cross-system learning—insights from evaluating one system applied to similar systems in portfolio
• Industry trend integration—new evaluation techniques, regulatory guidance, and threat models incorporated into practices

Integration with Existing Frameworks

Relationship to NIST Cybersecurity Framework: Many organizations already have cybersecurity programs based on NIST CSF. The AI RMF complements this but focuses on different risks. CSF emphasizes data confidentiality, integrity, and availability. AI RMF emphasizes accuracy, fairness, transparency, and appropriate autonomy. Both are necessary.

• Cybersecurity: Protects systems from unauthorized access, data theft, system manipulation
• AI Risk Management: Manages risks from system behavior even when operating as designed

Integration approach: Many organizations create a "Risk Management Framework" that incorporates cybersecurity, operational risk, compliance risk, and AI risk. AI RMF becomes the AI-specific layer within that broader framework.

Integration with Enterprise Risk Management: NIST AI RMF maps well to three lines of defense model:

• First line: Operational management of AI risk (system owners, development teams) implement GOVERN and MAP
• Second line: Risk and compliance functions (risk committees, compliance officers) oversee MEASURE and MANAGE
• Third line: Internal audit periodically audits the whole program

Implementation Maturity Tiers

NIST AI RMF defines four implementation tiers. Organizations typically start lower and advance over time as capabilities mature.

Tier	Characteristics	Typical Organization Type
Partial	Ad hoc, reactive. Risk management activities happen but not systematically. No documented policies. No clear ownership.	Startups, early-stage AI adoption, organizations without formal risk programs
Risk-Informed	Documented AI risk management policy. Risk assessment processes exist. Some systems get evaluated. Not all functions mature yet.	Companies with 10-50 AI systems, early sophisticated AI adoption, regulated companies beginning to formalize
Repeatable	Standardized processes. Most systems evaluated. Roles and responsibilities clear. Tools and templates in place. Consistent execution but still some manual effort.	Mature companies with 30+ systems, sophisticated technology companies, large enterprises with AI programs
Adaptive	Fully integrated. Evaluation mostly automated. Continuous monitoring built-in. Proactive improvement. Rapid response to incidents and regulatory changes. AI risk management embedded in culture.	Large tech companies, advanced financial services, organizations with >100 AI systems and dedicated risk teams

Important note: You don't need to be "Adaptive" for all functions. Many organizations are Repeatable for GOVERN and MAP (stable functions), Adaptive for MEASURE (continuous monitoring), and Repeatable for MANAGE (well-defined processes). The tier that matters most is where you are for your highest-risk systems.

Federal Agency Adoption Patterns

Federal agencies are moving toward NIST AI RMF adoption due to Biden administration guidance. Key patterns:

• Defense Department (DoD): Stricter interpretation focused on safety-critical systems. AI RMF + additional DoD-specific risk management requirements. Heavy focus on MEASURE for weapons systems.
• Civil Agencies (HHS, DOJ, etc.): Tiered approach: high-risk systems (criminal justice, benefits, healthcare) get Repeatable tier; lower-risk systems (internal tools, administrative) get Risk-Informed tier.
• Timelines: Most agencies targeting Tier 2 (Risk-Informed) compliance by end of 2024, Tier 3 (Repeatable) by 2025-2026.
• Procurement changes: Federal contracts increasingly require vendors to certify AI RMF compliance. Creates cascading requirements through vendor supply chains.
• Audit emphasis: OIG (Office of Inspector General) audits increasingly check AI RMF maturity. Findings in audits are damaging to agencies.

Sector-Specific Adaptations

Financial Services: Banks and insurers map NIST AI RMF to existing model risk management (SR 11-7). Emphasis on MEASURE with quantitative risk metrics. Integration with capital requirements (what's the risk-weighted asset impact of AI system failure).

• Expected: Repeatable tier for credit systems, Adaptive tier for fraud detection
• Regulatory focal points: Validation practices, model performance monitoring, control testing
• Challenge: Balancing speed-to-market with rigorous evaluation in competitive environment

Healthcare: Health systems and medical device companies map NIST AI RMF to FDA AI/ML regulatory framework and 21 CFR Part 11. Heavy focus on clinical validity, patient safety, and documentation.

• Expected: Repeatable tier minimum for clinical-facing systems
• Regulatory focal points: Clinical validation, adverse event monitoring, software as medical device classification
• Challenge: Longer evaluation timelines due to clinical validation requirements

Government/Justice: Agencies using AI in criminal justice, benefits, immigration maps NIST AI RMF to civil rights requirements. Heavy focus on fairness assessment and transparency to affected individuals.

• Expected: Adaptive tier for decision-making systems
• Regulatory focal points: Fairness evaluation, algorithmic impact assessment, community transparency
• Challenge: Defining fairness operationally in contentious domain

Common Implementation Pitfalls

1. Treating NIST AI RMF as a Checklist is the most common mistake. Organizations create spreadsheets mapping functions to checkboxes, then declare success. The framework only works if embedded in actual operational practices.

2. Starting with MEASURE Before GOVERN leads to expensive evaluation infrastructure that nobody uses. Build the governance structure first.

3. Inconsistent Risk Assessment Across Portfolio occurs when different business units apply AI RMF differently. One unit calls something "medium risk," another calls it "low risk." Mitigation: Create shared risk taxonomy and assessment training.

4. Evaluation Results That Lead Nowhere happens when MANAGE function is weak. Teams evaluate systems, find issues, then nothing happens. Clear decision-making authority and accountability prevents this.

5. Scope Creep in GOVERN occurs when organizations try to be perfectly compliant with every subcategory before deploying any systems. NIST AI RMF is framework not a checklist. Phase implementation. Get core governance (GOVERN-1,2), MAP one system end-to-end, then expand.

6. Over-reliance on Automated Tools in MEASURE. Tools are valuable but can't replace domain expertise and human judgment. The combination of automated metrics plus expert evaluation is most effective.

Building Your Implementation Roadmap

Phase 1 (Months 1-3): Foundation

• Assess current state: What governance exists, what's documented, what's missing
• Stakeholder interviews: Understand perspectives from engineering, product, compliance, business units
• Draft AI risk management policy: High-level statement of approach, responsibility, accountability
• Identify executive sponsor and establish oversight governance body
• Select 1-2 pilot systems: Pick systems that are important but not highest-risk for initial deep-dive

Phase 2 (Months 4-6): Governance and Mapping

• Formalize roles and responsibilities (implement GOVERN-2)
• Develop procedures for lifecycle management (implement GOVERN-3)
• Create AI risk decision register and processes (implement GOVERN-4)
• Complete MAP process for pilot systems and highest-risk existing systems
• Document in system cards what systems exist and what they do
• Identify evaluation gaps for each pilot system

Phase 3 (Months 7-12): Evaluation Infrastructure

• Plan and begin evaluations for pilot systems (implement MEASURE-1)
• Set up production monitoring for deployed systems (implement MEASURE-2)
• Document data governance practices (implement MEASURE-4)
• Begin implementing decisions and mitigations based on evaluation findings (implement MANAGE-1, 2)
• Develop incident response procedures (implement MANAGE-4)

Phase 4 (Year 2): Scaling and Refinement

• Expand to full system inventory
• Risk-tier each system (high, medium, low) and adjust evaluation rigor accordingly
• Build automation into evaluation where possible
• Establish center of excellence: centralized evaluation team supporting business units
• Develop cross-system lessons learned process
• Advance maturity tier from Risk-Informed to Repeatable

Resource Estimate for Implementation: A mid-size organization (50+ AI systems, 5-10 critical systems) typically needs:

• 1 FTE Program Manager (implementation and ongoing governance)
• 0.5 FTE Data Governance Lead (MEASURE-4)
• 2-3 FTE Evaluation Practitioners (design and execute MEASURE)
• 0.5 FTE Monitoring/DevOps (continuous monitoring infrastructure)
• Infrastructure/tooling budget: $50-150k annually depending on system complexity

Implementation timelines: 3-6 months to reach Risk-Informed tier, 12-18 months to reach Repeatable tier, 24+ months to reach Adaptive tier (if pursuing that level).

Additional Resources

• NIST AI RMF 1.0: Download from nvlpubs.nist.gov — the official framework document
• AI RMF Playbook: NIST's companion guide with practical implementation templates and examples
• Federal Agency AI Guidance: White House memorandum on AI governance, agency-specific implementation guidance
• Sector-Specific Mappings: Financial Services (SR 11-7), Healthcare (FDA AI/ML framework), Justice (NIST AI RMF + fairness guidance)
• eval.qa Resources: Case studies, playbooks, and templates for implementing NIST AI RMF in real organizations