---
description: 'Embedded RAI standards for Phase 3: NIST AI RMF 1.0 trustworthiness characteristics, subcategory mappings, and framework isolation architecture'
applyTo: '**/.copilot-tracking/rai-plans/**'
---

# RAI Standards Reference

NIST AI RMF 1.0 is embedded directly in this file as the default framework for Phase 3 of the RAI planning workflow. The framework provides 7 trustworthiness characteristics and an authoritative subcategory structure across 4 core functions and 72 subcategories. Evolving regulatory frameworks (EU AI Act, ISO 42001, domain-specific regulations) are delegated to the Researcher Subagent at runtime.

At least one trustworthiness characteristic from the active framework should map to every AI system component in the RAI plan. The phase mapping table provides starting-point alignments; refine these during Phase 3 analysis.

## Attribution

> NIST AI RMF content based on: National Institute of Standards and Technology (2023)
> Artificial Intelligence Risk Management Framework (AI RMF 1.0). NIST AI 100-1.
> https://doi.org/10.6028/NIST.AI.100-1
>
> Republished courtesy of the National Institute of Standards and Technology.
> This material is a work of the U.S. Government and is not subject to copyright
> protection in the United States. Reference herein does not imply endorsement
> by NIST.

## NIST AI RMF 1.0 Trustworthiness Characteristics

NIST AI RMF 1.0 defines 7 trustworthiness characteristics for AI systems. These characteristics replace the legacy 6-principle framework as the sole embedded standard.

### Characteristic Keys

| Key                        | Characteristic                 | MS-2.x  | Description                               |
|----------------------------|--------------------------------|---------|-------------------------------------------|
| `validReliable`            | Valid and Reliable             | MS-2.5  | Base characteristic; assess first         |
| `safe`                     | Safe                           | MS-2.6  | Builds on validity and reliability        |
| `secureResilient`          | Secure and Resilient           | MS-2.7  | Builds on validity and reliability        |
| `accountableTransparent`   | Accountable and Transparent    | MS-2.8  | Cross-cutting vertical; assess throughout |
| `explainableInterpretable` | Explainable and Interpretable  | MS-2.9  | Builds on validity and reliability        |
| `privacyEnhanced`          | Privacy-Enhanced               | MS-2.10 | Builds on validity and reliability        |
| `fairBiasManaged`          | Fair with Harmful Bias Managed | MS-2.11 | Builds on validity and reliability        |

### Characteristic Hierarchy

* Valid and Reliable serves as the base characteristic supporting all others (assess first).
* Accountable and Transparent is a vertical cross-cutting characteristic enabling all others (assess throughout).
* Safe, Secure and Resilient, Explainable and Interpretable, Privacy-Enhanced, and Fair with Harmful Bias Managed build on the base.

Tradeoffs between characteristics (privacy versus accuracy, interpretability versus performance, fairness versus model complexity) are inherent and must be documented rather than assumed resolvable.

### Core Function Structure

The 4 core functions organize 72 subcategories across 19 categories:

| Function | Categories | Subcategories | Role                                                |
|----------|------------|---------------|-----------------------------------------------------|
| Govern   | 6          | 19            | Cross-cutting foundation; risk culture and policies |
| Map      | 5          | 18            | Context and risk framing                            |
| Measure  | 4          | 22            | Trustworthiness evaluation (TEVV)                   |
| Manage   | 4          | 13            | Risk response and recovery                          |

Govern is the foundational cross-cutting function established before the other three. Map, Measure, and Manage iterate in any order after Govern is in place.

## NIST AI RMF 1.0 Core Functions

NIST AI Risk Management Framework (NIST.AI.100-1, January 2023) organizes AI governance into 4 core functions with 72 subcategories across 19 categories. The framework is voluntary, iterative, and context-dependent. Govern is the foundational cross-cutting function established before the other three. Map, Measure, and Manage iterate in any order after Govern is in place.

### Govern (Cross-Cutting Foundation)

Risk management culture, policies, accountability structures, and stakeholder engagement. 6 categories, 19 subcategories.

| ID     | Description                                                 |
|--------|-------------------------------------------------------------|
| GV-1   | Policies, processes, procedures, and practices              |
| GV-1.1 | Legal and regulatory requirements identified and integrated |
| GV-1.2 | Trustworthy AI characteristics integrated into policies     |
| GV-1.3 | Risk tolerance determined and documented                    |
| GV-1.4 | Transparent risk management policies established            |
| GV-1.5 | Ongoing monitoring processes defined                        |
| GV-1.6 | AI system inventory maintained                              |
| GV-1.7 | Decommissioning processes defined                           |
| GV-2   | Accountability structures                                   |
| GV-2.1 | Roles, responsibilities, and communication channels defined |
| GV-2.2 | Training programs for AI risk management established        |
| GV-2.3 | Executive leadership responsibility for AI risk             |
| GV-3   | Diversity, equity, inclusion, and accessibility (DEI&A)     |
| GV-3.1 | Diverse teams for AI development and oversight              |
| GV-3.2 | Human-AI oversight mechanisms defined                       |
| GV-4   | Organizational risk culture                                 |
| GV-4.1 | Safety-first mindset in AI development                      |
| GV-4.2 | Risk documentation and communication practices              |
| GV-4.3 | Testing and incident sharing protocols                      |
| GV-5   | Stakeholder engagement                                      |
| GV-5.1 | External feedback mechanisms established                    |
| GV-5.2 | Adjudicated feedback integration into risk management       |
| GV-6   | Third-party and supply chain risk                           |
| GV-6.1 | Third-party IP and rights risks assessed                    |
| GV-6.2 | Contingency plans for third-party failures                  |

### Map (Context and Risk Framing)

System characterization, intended context, stakeholder mapping, and risk framing. 5 categories, 18 subcategories.

| ID     | Description                                           |
|--------|-------------------------------------------------------|
| MP-1   | Intended context and purpose                          |
| MP-1.1 | Intended purposes, laws, and settings documented      |
| MP-1.2 | Diverse actors and competencies identified            |
| MP-1.3 | Mission and organizational goals alignment            |
| MP-1.4 | Business value assessment                             |
| MP-1.5 | Risk tolerances for the specific AI system            |
| MP-1.6 | System requirements including socio-technical factors |
| MP-2   | AI system categorization                              |
| MP-2.1 | Tasks and methods defined                             |
| MP-2.2 | Knowledge limits and human oversight requirements     |
| MP-2.3 | Scientific integrity and TEVV planning                |
| MP-3   | Capabilities and benchmarks                           |
| MP-3.1 | Potential benefits enumerated                         |
| MP-3.2 | Potential costs and negative impacts                  |
| MP-3.3 | Targeted scope of deployment                          |
| MP-3.4 | Operator proficiency requirements                     |
| MP-3.5 | Human oversight processes for deployment              |
| MP-4   | Third-party risks                                     |
| MP-4.1 | Legal and IP risk mapping for third-party components  |
| MP-4.2 | Internal risk controls for third-party dependencies   |
| MP-5   | Impact assessment                                     |
| MP-5.1 | Likelihood and magnitude of impacts assessed          |
| MP-5.2 | Regular stakeholder engagement practices              |

### Measure (Trustworthiness Evaluation)

Quantitative and qualitative testing, evaluation, verification, and validation (TEVV). 4 categories, 22 subcategories. MS-2.5 through MS-2.11 map directly to the 7 trustworthiness characteristics.

| ID      | Description                                     |
|---------|-------------------------------------------------|
| MS-1    | Methods and metrics selection                   |
| MS-1.1  | Risk-based metric selection                     |
| MS-1.2  | Metric appropriateness assessment               |
| MS-1.3  | Independent assessors engaged                   |
| MS-2    | Trustworthiness evaluation                      |
| MS-2.1  | TEVV tools and documentation                    |
| MS-2.2  | Human subject evaluations                       |
| MS-2.3  | Performance evaluation in deployment conditions |
| MS-2.4  | Production monitoring established               |
| MS-2.5  | Validity and reliability demonstration          |
| MS-2.6  | Safety evaluation                               |
| MS-2.7  | Security and resilience evaluation              |
| MS-2.8  | Transparency and accountability assessment      |
| MS-2.9  | Explainability and interpretability evaluation  |
| MS-2.10 | Privacy risk assessment                         |
| MS-2.11 | Fairness and bias evaluation                    |
| MS-2.12 | Environmental impact assessment                 |
| MS-2.13 | TEVV process effectiveness review               |
| MS-3    | Risk tracking                                   |
| MS-3.1  | Risk identification and tracking approaches     |
| MS-3.2  | Difficult-to-assess risk tracking               |
| MS-3.3  | End user feedback and appeal mechanisms         |
| MS-4    | Measurement efficacy feedback                   |
| MS-4.1  | Deployment context connection to measurements   |
| MS-4.2  | Domain expert validation of measurements        |
| MS-4.3  | Performance improvement and decline tracking    |

### Manage (Risk Response and Recovery)

Risk prioritization, treatment, monitoring, and continuous improvement. 4 categories, 13 subcategories.

| ID     | Description                                                        |
|--------|--------------------------------------------------------------------|
| MN-1   | Risk prioritization                                                |
| MN-1.1 | Go/no-go determination based on risk assessment                    |
| MN-1.2 | Risk treatment prioritization by impact and likelihood             |
| MN-1.3 | High-priority risk response plans                                  |
| MN-1.4 | Residual risk documentation                                        |
| MN-2   | Benefit maximization and impact minimization                       |
| MN-2.1 | Resource management with non-AI alternatives considered            |
| MN-2.2 | Sustaining deployed value                                          |
| MN-2.3 | Unknown risk response procedures                                   |
| MN-2.4 | Supersede, disengage, or deactivate mechanisms                     |
| MN-3   | Third-party risk management                                        |
| MN-3.1 | Third-party monitoring and controls                                |
| MN-3.2 | Pre-trained model monitoring                                       |
| MN-4   | Risk treatment documentation                                       |
| MN-4.1 | Post-deployment monitoring, incident response, and decommissioning |
| MN-4.2 | Continual improvement activities                                   |
| MN-4.3 | Incident/error communication and recovery                          |

## RAI-Security Overlap Mapping

RAI and security concerns overlap at specific intersection points. Use this mapping to identify components requiring both trustworthiness characteristic evaluation and security model analysis.

| NIST Characteristic            | STRIDE Category                   | Overlap Area                                                       |
|--------------------------------|-----------------------------------|--------------------------------------------------------------------|
| Privacy-Enhanced               | Information Disclosure, Tampering | Data protection, model inversion attacks, training data extraction |
| Safe                           | Denial of Service, Tampering      | Adversarial examples, data poisoning, model degradation            |
| Fair with Harmful Bias Managed | Tampering                         | Biased training data injection, demographic targeting              |
| Accountable and Transparent    | Repudiation                       | Audit trail integrity, decision provenance                         |
| Explainable and Interpretable  | Information Disclosure            | Model explanation versus intellectual property protection          |

## Security-Adjacent Subcategories

These Measure subcategories overlap directly with security model analysis. Cross-reference them with the Security Planner's STRIDE analysis when both RAI and security assessments apply to the same component.

| Subcategory | Focus                        | Security Relevance                                                 |
|-------------|------------------------------|--------------------------------------------------------------------|
| MS-2.5      | Validity and reliability     | OWASP A04 Insecure Design; robustness under adversarial conditions |
| MS-2.6      | Safety evaluation            | STRIDE Denial of Service; failure mode analysis                    |
| MS-2.7      | Security and resilience      | All STRIDE categories; adversarial ML, model exfiltration          |
| MS-2.10     | Privacy risk assessment      | STRIDE Information Disclosure; data minimization, PET evaluation   |
| MS-2.11     | Fairness and bias evaluation | RAI-specific work items; biased training data injection detection  |

## Phase-to-NIST AI RMF Mapping

This table maps RAI Planner phases to NIST AI RMF subcategories. Use these alignments as starting points for each phase's standards coverage.

| RAI Planner Phase                 | AI RMF Function  | Key Subcategories                                                                                                    |
|-----------------------------------|------------------|----------------------------------------------------------------------------------------------------------------------|
| Phase 1 (Scoping)                 | Govern + Map     | GV-1 (policies), MP-1 through MP-5 (context, requirements, benefits and costs, third-party risks, impact assessment) |
| Phase 2 (Risk Classification)     | Govern           | GV-1 (policies), GV-3 (DEI&A), GV-5 (stakeholder engagement)                                                         |
| Phase 3 (Standards Mapping)       | Govern + Measure | GV-1 through GV-6 (full governance), MS-1 (measurement approach)                                                     |
| Phase 4 (Security Model Analysis) | Measure          | MS-2.5 through MS-2.11 (trustworthiness evaluation per characteristic)                                               |
| Phase 5 (Impact Assessment)       | Manage           | MN-1 through MN-4 (risk prioritization, response, monitoring, documentation)                                         |
| Phase 6 (Review and Handoff)      | Manage           | MN-3 (third-party monitoring), MN-4 (continual improvement, incident communication)                                  |

## Active Framework Resolution

The RAI Planner defaults to NIST AI RMF 1.0 as the active framework. A custom framework replaces the default when the `riskClassification.framework` object contains `replaceDefaultFramework: true`.

### Default (NIST Active)

When NIST is the active framework:

* NIST attribution displays in the Attribution section.
* Phase-to-NIST AI RMF Mapping applies for phase alignment.
* NIST Trustworthiness Characteristics and Characteristic Hierarchy inform assessment ordering.
* 3 default risk indicators apply during Phase 2 risk classification.

### Custom Framework Override

When `replaceDefaultFramework: true`:

* NIST attribution is suppressed; custom framework attribution from the framework document's frontmatter `attribution` field displays instead.
* Phase-to-NIST AI RMF Mapping is replaced with the custom framework's `## Phase Mappings` section.
* Custom framework characteristics replace NIST trustworthiness characteristics for assessment ordering.

When `replaceDefaultIndicators: true`:

* Default risk indicators are replaced with custom indicators from the framework document's `## Risk Indicators` section.

### Framework Document Schema

Custom framework documents stored in `.copilot-tracking/rai-plans/references/` include:

* Frontmatter with `type: framework`, `attribution`, and `replaceDefaultFramework: true`.
* `## Phase Mappings` section mapping custom phases to RAI Planner phases.
* `## Trustworthiness Characteristics` section defining custom characteristic keys.
* `## Risk Indicators` section (optional) defining custom risk indicators when `replaceDefaultIndicators: true`.

## Researcher Subagent Delegation

Evolving regulatory frameworks and emerging AI governance standards are delegated to the Researcher Subagent at runtime. These frameworks change frequently, vary by jurisdiction, or contain extensive domain-specific content best retrieved on demand.

| Standard                 | Rationale for Delegation                                                                           |
|--------------------------|----------------------------------------------------------------------------------------------------|
| EU AI Act                | Rapidly evolving risk classification tiers, version-dependent compliance requirements              |
| ISO 42001                | Emerging AI Management System standard, evolving certification guidance                            |
| WAF AI / CAF AI          | Cloud-specific AI governance guidance, frequently updated Azure content                            |
| HIPAA AI                 | Domain-specific healthcare AI regulations, requires current interpretation                         |
| Financial ML Regulations | Domain-specific financial services AI requirements, jurisdiction-dependent                         |
| Regional AI Frameworks   | Jurisdiction-specific AI governance (Singapore Model AI Governance, Canada AIDA, UK AI Regulation) |

When NIST is the active framework, do not delegate NIST AI RMF or RAI-Security overlap lookups; those standards are embedded above. When a custom framework is active, do not delegate lookups for the active framework's embedded standards. If NIST AI RMF subcategories are needed for comparison under a custom framework, delegate to the Researcher Subagent.

### User-Supplied Standards

Before completing standards mapping, check `.copilot-tracking/rai-plans/references/` for files with type `standard` in `referencesProcessed` state. When user-supplied standards exist:

1. Read each standard reference file from `.copilot-tracking/rai-plans/references/`.
2. Map the user-supplied standard's requirements alongside the active framework.
3. Include user-supplied standard mappings in the component standards mapping output under a **User-Supplied Standards** subsection.
4. Display the AI processing disclaimer for each user-supplied standard: "AI processed this user-supplied standard and may generate inconsistent results. Verify against the original source."

### When to Delegate

* Phase 3 identifies regulatory requirements beyond embedded frameworks.
* Compliance context requires EU AI Act risk tier classification.
* The AI system operates in a regulated domain (healthcare, finance, government).
* Regional or jurisdictional AI governance alignment is required.
* ISO 42001 certification readiness assessment is requested.

### Invocation Pattern

Use `runSubagent` with the Researcher Subagent:

```text
Agent: Researcher Subagent
Topic: {specific AI governance framework area to research}
Context: AI system "{name}" with depth tier "{tier}" in domain "{domain}"
Output: .copilot-tracking/research/subagents/{{YYYY-MM-DD}}/{system-name}-{framework}.md
```

Response format: Return findings as a markdown document with Standards Coverage, Findings, and Recommendations sections.

Execution constraints: Complete research within a single invocation. Do not delegate to additional subagents.

The Researcher Subagent returns: subagent research document path, research status, important discovered details, recommended next research not yet completed, and any clarifying questions.

When neither `runSubagent` nor `task` tools are available, inform the user that one of these tools is required and should be enabled. Do not synthesize or fabricate answers for delegated standards from training data.

Subagents can run in parallel when researching independent frameworks or governance domains.

### Query Templates

Use these templates when delegating to the Researcher Subagent:

* EU AI Act: "Classify {AI system} under EU AI Act risk tiers and identify applicable requirements for {use case} in {deployment context}."
* ISO 42001: "Map {AI system} governance practices against ISO 42001 AI Management System requirements for {organizational context}."
* WAF AI / CAF AI: "Identify Azure WAF AI and CAF AI governance controls applicable to {AI system} deployed on {Azure service}."
* HIPAA AI: "Evaluate {AI system} handling {PHI context} against HIPAA AI-specific requirements and OCR guidance."
* Financial ML: "Map {AI system} providing {financial service} against applicable ML regulations in {jurisdiction}."
* Regional: "Identify {jurisdiction} AI governance framework requirements applicable to {AI system} for {use case}."

Subagent research outputs follow the repository-wide `.copilot-tracking/research/subagents/` convention and are not subject to the parent agent's own file creation constraints.

Collect findings from the output path and incorporate them into the component's RAI standards mapping.

## Mapping Output Format

For each AI system component, produce a standards mapping block following this structure:

```markdown
### {Component Name} ({Depth Tier})

**Trustworthiness Characteristics:**
- {Characteristic}: {assessment criteria with justification}

**NIST AI RMF Coverage:**
- {Function}: {subcategories with justification}

**Security Overlap:**
- {overlap areas identified from RAI-Security Overlap Mapping}

**Delegated Framework Findings:** {researcher subagent results or N/A}

**Gap Analysis:** {identified gaps between current practices and standard requirements}
```

Include justification for each mapped standard, explaining why the characteristic or subcategory is relevant to the specific component. Flag gaps where a standard should apply based on the phase mapping table but no corresponding assessment exists.

### Output Detail Adjustments

When `userPreferences.outputDetailLevel` is set, adjust output accordingly:

| Detail Level  | Behavior                                                                                            |
|---------------|-----------------------------------------------------------------------------------------------------|
| summary       | Emit only the characteristic name, mapped standard reference, and one-line rationale.               |
| standard      | Emit the full mapping table with rationale column (current default behavior).                       |
| comprehensive | Emit the full mapping table plus evidence chains linking each mapping to source NIST subcategories. |

### Progressive Presentation

Present standards mapping progressively:

1. Trustworthiness characteristics (Valid and Reliable, Safe, Secure and Resilient, Accountable and Transparent, Explainable and Interpretable, Privacy-Enhanced, Fair with Harmful Bias Managed): always mapped first.
2. NIST AI RMF subcategories: map the most relevant subcategories per the system's context; present remaining subcategories upon request.
3. Specialized standards: include domain-specific standards (ISO, sector regulations) only when the user's system context indicates relevance.
